students have different learning styles essay

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Different Learning Styles—What Teachers Need To Know

The concept of “learning styles” has been overwhelmingly embraced by educators in the U.S. and worldwide. Studies show that an estimated 89% of teachers believe in matching instruction to a student’s preferred learning style (Newton & Salvi, 2020). That’s a problem—because research tells us that this approach doesn’t work to improve learning.

What Do We Mean by “Learning Styles”?

It’s true that people have fairly stable strengths and weaknesses in their cognitive abilities, such as processing language or visual-spatial stimuli. People can also have preferences in the way they receive information—Joan may prefer to read an article while Jay may rather listen to a lecture.

The “learning styles” theory makes a big leap, suggesting that students will learn better if they are taught in a manner that conforms to their preferences. More than 70 different systems have been developed that use student questionnaires/self-reports to categorize their supposed learning preferences.

VARK Learning Styles

One of the most popular learning styles inventories used in schools is the VARK system (Cuevas, 2015). Students answer 25 multiple-choice questions that range from how they like their teachers to teach (discussions and guest speakers, textbooks and handouts, field trips and labs, or charts and diagrams) to how they would give directions to a neighbor’s house (draw a map, write out directions, say them aloud, or walk with the person) (VARK Learn Limited, 2021). Based on their responses, the system classifies them as Visual, Auditory, Read-write, and/or Kinesthetic learners and recommends specific learning strategies.

If only it were that simple. While this brief survey may provide some insights for teachers, we must be wary of overestimating the value of the results. By placing students in categories that reflect “preferred learning styles,” we run the risk of oversimplifying the complex nature of teaching and learning to the detriment of our students.

What Does the Science Say?

Study after study has shown that matching instructional mode to a student’s supposedly identified “learning style” does not produce better learning outcomes. In fact, a student’s “learning style” may not even predict the way they prefer to be taught or the way they actually choose to study on their own (Newton & Salvi, 2020).

Simply put, students’ learning preferences as identified via questionnaires do not predict the singular, best way to teach them. A single student may learn best with one approach in one subject and a different one in another. The best approach for them may even vary day-to-day. Most likely, students are best served when a variety of strategies are employed in a lesson.

As appealing as a framework like VARK is—relatively easy to conceptualize and quick to assess—everyone engages in different modes of learning in various ways. The brain processes information in very complex and nuanced ways that can’t be so simply generalized.

Fads are common in education. Having been embraced for several decades, though, “learning styles” has moved beyond fad to what experts refer to as “neuromyth,” one of many “commonly accepted, erroneous beliefs based on misunderstandings of neuroscience that contribute to pseudoscientific practice within education (Ruhaak & Cook, 2018). In fact, the idea that “students learn best when teaching styles are matched to their learning styles” earned a spot in 50 Great Myths of Popular Psychology (Lilienfeld, Lynn, & Beyerstein, 2009), alongside “Extrasensory perception is a well-established scientific phenomenon” and “Our handwriting reveals our personality traits.”

Unfortunately, the myth has become so prevalent that the majority of papers written about learning styles are based on the assumption that matching teaching style to learning style is desirable (Newton, 2015). It’s no surprise, then, that well-intentioned educators (and parents and caregivers) buy into the concept as well.

What Harm Does It Do?

When a student is pigeonholed as a particular “type” of learner, and their lessons are all prepared with that in mind, they could be missing out on other learning opportunities with a better chance of success.

Adapting instruction to individual students’ “learning styles” is no small task—and teachers who attempt to do so are clearly motivated to find the best way to help their students. They could put their time to better use, though.

Better Learning Style Approaches

Universal Design for Learning (UDL) is an evidence-driven framework for improving and optimizing learning for all students. When a learning opportunity provides for 1) multiple means of engagement, 2) multiple means of representation, and 3) multiple means of action and expression, different styles of learning are accounted for at the outset, reducing the need to personalize every activity. Nonprofit CAST.org, where KU Special Education Professor Jamie Basham is Senior Director for Learning & Innovation, offers free UDL Guidelines, with detailed information on how to optimize learning for all your students.

Operating within a UDL framework, teachers should use Evidence-based Practices (EBPs)—specific teaching techniques and interventions that have sufficient published, peer-reviewed studies that demonstrate their effectiveness in addressing specific issues with particular populations of students. (We discussed EBPs for autism spectrum disorder in a previous blog.) In addition, the Council for Exceptional Children recommends a core set of High Leverage Practices –basic, foundational practices that every special education teacher should know and perform fluently.

Evidence-based Learning Style Approaches at KU Special Education

Faculty in the University of Kansas Department of Special Education are world-renowned for their research in UDL and evidence-based special education practices. Students can be assured that our online master’s degrees and graduate certificates focus on research-based teaching and assessment methods—just one of the reasons we’ve been rated the #1 Best Online Master’s Degree in Special Education by U.S. News & World Report for two years in a row. 1

Explore our special education programs and consider how earning an online master’s from a Top 10 Best Education School (among public universities) can help you achieve your goals

CAST (2018). Universal Design for Learning Guidelines version 2.2. Retrieved March 4, 2021 from udlguidelines.cast.org.

Cuevas, J. (2015). Is learning styles-based instruction effective? A comprehensive analysis of recent research on learning styles. Theory & Research in Education. 13 (3), 308–333. doi.org/10.1177/1477878515606621

Lilienfeld, S., Lynn, J., Rucio, J., & Beyerstein, B. (2009) 50 great myths of popular psychology: Shattering widespread misconceptions about human behavior. Wiley-Blackwell. ISBN: 978-1405131117

Newton, P. M. (2015). The learning styles myth is thriving in higher education. Frontiers in Psychology, 6 , 1908. doi.org/10.3389/fpsyg.2015.01908

Newton, P. M. & Salvi, A. (2020). How common is belief in the learning styles neuromyth, and does it matter? A pragmatic systematic review. Frontiers in Education, 5 (602451), 1-14. doi.org/10.3389/feduc.2020.602451

Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9 , 105–119. doi.org/10.1111/j.1539-6053.2009.01038.x

Ruhaak, A. E., & Cook, B. G. (2018). The prevalence of educational neuromythings among pre-service special education teachers. Mind, Brain, and Education. 12 (3) 155-161. doi.org/10.1111/mbe.12181

1 Retrieved on May 13, 2021, from usnews.com/best-graduate-schools/top-education-schools/university-of-kansas-06075 2 Retrieved on May 13, 2021, from usnews.com/best-graduate-schools/top-education-schools/edu-rankings

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Create Your Course

The 7 main types of learning styles (and how to teach to them), share this article.

Understanding the 7 main types of learning styles and how to teach them will help both your students and your courses be more successful.

When it comes to learning something new, we all absorb information at different rates and understand it differently too. Some students get new concepts right away; others need to sit and ponder for some time before they can arrive at similar conclusions.

Why? The answer lies in the type of learning styles different students feel more comfortable with. In other words, we respond to information in different ways depending on how it is presented to us.

Clearly, different types of learning styles exist, and there are lots of debates in pedagogy about what they are and how to adapt to them.

For practical purposes, it’s recommended to ensure that your course or presentation covers the 7 main types of learning.

In this article, we’ll break down the 7 types of learning styles, and give practical tips for how you can improve your own teaching styles , whether it’s in higher education or an online course you plan to create on the side.

Skip ahead:

What are the 7 types of learning styles?

How to accommodate different types of learning styles online.

• How to help students understand their different types of learning styles

How to create an online course for all

In the academic literature, the most common model for the types of learning you can find is referred to as VARK.

VARK is an acronym that stands for Visual, Auditory, Reading & Writing, and Kinesthetic. While these learning methods are the most recognized, there are people that do not fit into these boxes and prefer to learn differently. So we’re adding three more learning types to our list, including Logical, Social, and Solitary.

Visual learners

Visual learners are individuals that learn more through images, diagrams, charts, graphs, presentations, and anything that illustrates ideas. These people often doodle and make all kinds of visual notes of their own as it helps them retain information better. 

When teaching visual learners, the goal isn’t just to incorporate images and infographics into your lesson. It’s about helping them visualize the relationships between different pieces of data or information as they learn. 

Gamified lessons are a great way to teach visual learners as they’re interactive and aesthetically appealing. You should also give handouts, create presentations, and search for useful infographics to support your lessons.

Since visual information can be pretty dense, give your students enough time to absorb all the new knowledge and make their own connections between visual clues.

Auditory/aural learners

The auditory style of learning is quite the opposite of the visual one. Auditory learners are people that absorb information better when it is presented in audio format (i.e. the lessons are spoken). This type of learner prefers to learn by listening and might not take any notes at all. They also ask questions often or repeat what they have just heard aloud to remember it better.

Aural learners are often not afraid of speaking up and are great at explaining themselves. When teaching auditory learners, keep in mind that they shouldn’t stay quiet for long periods of time. So plan a few activities where you can exchange ideas or ask questions. Watching videos or listening to audio during class will also help with retaining new information.

Reading and writing (or verbal) learners

Reading & Writing learners absorb information best when they use words, whether they’re reading or writing them. To verbal learners, written words are more powerful and granular than images or spoken words, so they’re excellent at writing essays, articles, books, etc. 

To support the way reading-writing students learn best, ensure they have time to take ample notes and allocate extra time for reading. This type of learner also does really well at remote learning, on their own schedule. Including reading materials and writing assignments in their homework should also yield good results.

Kinesthetic/tactile learners

Kinesthetic learners use different senses to absorb information. They prefer to learn by doing or experiencing what they’re being taught. These types of learners are tactile and need to live through experiences to truly understand something new. This makes it a bit challenging to prepare for them in a regular class setting. 

As you try to teach tactile learners, note that they can’t sit still for long and need more frequent breaks than others. You need to get them moving and come up with activities that reinforce the information that was just covered in class. Acting out different roles is great; games are excellent; even collaborative writing on a whiteboard should work fine. If applicable, you can also organize hands-on laboratory sessions, immersions, and workshops.

In general, try to bring every abstract idea into the real world to help kinesthetic learners succeed.

Logical/analytical learners 

As the name implies, logical learners rely on logic to process information and understand a particular subject. They search for causes and patterns to create a connection between different kinds of information. Many times, these connections are not obvious to people to learn differently, but they make perfect sense to logical learners. 

Logical learners generally do well with facts, statistics, sequential lists, and problem-solving tasks to mention a few. 

As a teacher, you can engage logical learners by asking open-ended or obscure questions that require them to apply their own interpretation. You should also use teaching material that helps them hone their problem-solving skills and encourages them to form conclusions based on facts and critical thinking. 

Social/interpersonal learners 

Social or interpersonal learners love socializing with others and working in groups so they learn best during lessons that require them to interact with their peers . Think study groups, peer discussions, and class quizzes. 

To effectively teach interpersonal learners, you’ll need to make teamwork a core part of your lessons. Encourage student interaction by asking questions and sharing stories. You can also incorporate group activities and role-playing into your lessons, and divide the students into study groups.  

Solitary/intrapersonal learners 

Solitary learning is the opposite of social learning. Solitary, or solo, learners prefer to study alone without interacting with other people. These learners are quite good at motivating themselves and doing individual work. In contrast, they generally don’t do well with teamwork or group discussions.

To help students like this, you should encourage activities that require individual work, such as journaling, which allows them to reflect on themselves and improve their skills. You should also acknowledge your students’ individual accomplishments and help them refine their problem-solving skills. 

Are there any unique intelligence types commonly shared by your students? Adapting to these different types of intelligence can help you can design a course best suited to help your students succeed.

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How to help students understand their different types of learning styles 

Unless you’re teaching preschoolers, most students probably already realize the type of learning style that fits them best. But some students do get it wrong.

The key here is to observe every student carefully and plan your content for different learning styles right from the start.

Another idea is to implement as much individual learning as you can and then customize that learning for each student. So you can have visual auditory activities, riddles for logical learners, games for kinesthetic learners, reading activities, writing tasks, drawing challenges, and more.

When you’re creating your first course online, it’s important to dedicate enough time to planning out its structure. Don’t just think that a successful course consists of five uploaded videos.

Think about how you present the new knowledge. Where it makes sense to pause and give students the time to reflect. Where to include activities to review the new material. Adapting to the different learning types that people exhibit can help you design an online course best suited to help your students succeed.

That being said, here are some tips to help you tailor your course to each learning style, or at least create enough balance. 

Visual learners 

Since visual learners like to see or observe images, diagrams, demonstrations, etc., to understand a topic, here’s how you can create a course for them: 

• Include graphics, cartoons, or illustrations of concepts 
• Use flashcards to review course material 
• Use flow charts or maps to organize materials 
• Highlight and color code notes to organize materials 
• Use color-coded tables to compare and contrast elements 
• Use a whiteboard to explain important information
• Have students play around with different font styles and sizes to improve readability 

Auditory learners prefer to absorb information by listening to spoken words, so they do well when teachers give spoken instructions and lessons. Here’s how to cater to this learning type through your online course: 

• Converse with your students about the subject or topic 
• Ask your students questions after each lesson and have them answer you (through the spoken word)
• Have them record lectures and review them with you 
• Have articles, essays, and comprehension passages out to them
• As you teach, explain your methods, questions, and answers 
• Ask for oral summaries of the course material 
• If you teach math or any other math-related course, use a talking calculator 
• Create an audio file that your students can listen to
• Create a video of you teaching your lesson to your student
• Include a YouTube video or podcast episode for your students to listen to
• Organize a live Q & A session where students can talk to you and other learners to help them better understand the subject

Reading and writing (or verbal) learners 

This one is pretty straightforward. Verbal learners learn best when they read or write (or both), so here are some practical ways to include that in your online course:

• Have your students write summaries about the lesson 
• If you teach language or literature, assign them stories and essays that they’d have to read out loud to understand
• If your course is video-based, add transcripts to aid your students’ learning process
• Make lists of important parts of your lesson to help your students memorize them
• Provide downloadable notes and checklists that your students can review after they’ve finished each chapter of your course
• Encourage extra reading by including links to a post on your blog or another website in the course
• Use some type of body movement or rhythm, such as snapping your fingers, mouthing, or pacing, while reciting the material your students should learn

Since kinesthetic learners like to experience hands-on what they learn with their senses — holding, touching, hearing, and doing. So instead of churning out instructions and expecting to follow, do these instead: 

• Encourage them to experiment with textured paper, and different sizes of pencils, pens, and crayons to jot down information
• If you teach diction or language, give them words that they should incorporate into their daily conversations with other people
• Encourage students to dramatize or act out lesson concepts to understand them better 

Logical learners are great at recognizing patterns, analyzing information, and solving problems. So in your online course, you need to structure your lessons to help them hone these abilities. Here are some things you can do:

• Come up with tasks that require them to solve problems. This is easy if you teach math or a math-related course
• Create charts and graphs that your students need to interpret to fully grasp the lesson
• Ask open-ended questions that require critical thinking 
• Create a mystery for your students to solve with clues that require logical thinking or math
• Pose an issue/topic to your students and ask them to address it from multiple perspectives

Since social learners prefer to discuss or interact with others, you should set up your course to include group activities. Here’s how you can do that:

• Encourage them to discuss the course concept with their classmates
• Get your students involved in forum discussions
• Create a platform (via Slack, Discord, etc.) for group discussions
• Pair two or more social students to teach each other the course material
• If you’re offering a cohort-based course , you can encourage students to make their own presentations and explain them to the rest of the class

Solitary learners prefer to learn alone. So when designing your course, you need to take that into consideration and provide these learners a means to work by themselves. Here are some things you can try: 

• Encourage them to do assignments by themselves
• Break down big projects into smaller ones to help them manage time efficiently
• Give them activities that require them to do research on their own
• When they’re faced with problems regarding the topic, let them try to work around it on their own. But let them know that they are welcome to ask you for help if they need to
• Encourage them to speak up when you ask them questions as it builds their communication skills 
• Explore blended learning , if possible, by combining teacher-led classes with self-guided assignments and extra ideas that students can explore on their own.

Now that you’re ready to teach something to everyone, you might be wondering what you actually need to do to create your online courses. Well, start with a platform.

Thinkific is an intuitive and easy-to-use platform any instructor can use to create online courses that would resonate with all types of learning styles. Include videos, audio, presentations, quizzes, and assignments in your curriculum. Guide courses in real-time or pre-record information in advance. It’s your choice.

In addition, creating a course on Thinkific doesn’t require you to know any programming. You can use a professionally designed template and customize it with a drag-and-drop editor to get exactly the course you want in just a few hours. Try it yourself to see how easy it can be.

This blog was originally published in August 2017, it has since been updated in March 2023. 

Althea Storm is a B2B SaaS writer who specializes in creating data-driven content that drives traffic and increases conversions for businesses. She has worked with top companies like AdEspresso, HubSpot, Aura, and Thinkific. When she's not writing web content, she's curled up in a chair reading a crime thriller or solving a Rubik's cube.

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Center for Teaching

Learning styles, what are learning styles, why are they so popular.

The term  learning styles is widely used to describe how learners gather, sift through, interpret, organize, come to conclusions about, and “store” information for further use.  As spelled out in VARK (one of the most popular learning styles inventories), these styles are often categorized by sensory approaches:   v isual, a ural, verbal [ r eading/writing], and k inesthetic.  Many of the models that don’t resemble the VARK’s sensory focus are reminiscent of Felder and Silverman’s Index of Learning Styles , with a continuum of descriptors for how learners process and organize information:  active-reflective, sensing-intuitive, verbal-visual, and sequential-global.

There are well over 70 different learning styles schemes (Coffield, 2004), most of which are supported by “a thriving industry devoted to publishing learning-styles tests and guidebooks” and “professional development workshops for teachers and educators” (Pashler, et al., 2009, p. 105).

Despite the variation in categories, the fundamental idea behind learning styles is the same: that each of us has a specific learning style (sometimes called a “preference”), and we learn best when information is presented to us in this style.  For example, visual learners would learn any subject matter best if given graphically or through other kinds of visual images, kinesthetic learners would learn more effectively if they could involve bodily movements in the learning process, and so on.  The message thus given to instructors is that “optimal instruction requires diagnosing individuals’ learning style[s] and tailoring instruction accordingly” (Pashler, et al., 2009, p. 105).

Despite the popularity of learning styles and inventories such as the VARK, it’s important to know that there is no evidence to support the idea that matching activities to one’s learning style improves learning .  It’s not simply a matter of “the absence of evidence doesn’t mean the evidence of absence.”  On the contrary, for years researchers have tried to make this connection through hundreds of studies.

In 2009, Psychological Science in the Public Interest commissioned cognitive psychologists Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork to evaluate the research on learning styles to determine whether there is credible evidence to support using learning styles in instruction.  They came to a startling but clear conclusion:  “Although the literature on learning styles is enormous,” they “found virtually no evidence” supporting the idea that “instruction is best provided in a format that matches the preference of the learner.”  Many of those studies suffered from weak research design, rendering them far from convincing.  Others with an effective experimental design “found results that flatly contradict the popular” assumptions about learning styles (p. 105). In sum,

“The contrast between the enormous popularity of the learning-styles approach within education and the lack of credible evidence for its utility is, in our opinion, striking and disturbing” (p. 117).

Pashler and his colleagues point to some reasons to explain why learning styles have gained—and kept—such traction, aside from the enormous industry that supports the concept.  First, people like to identify themselves and others by “type.” Such categories help order the social environment and offer quick ways of understanding each other.  Also, this approach appeals to the idea that learners should be recognized as “unique individuals”—or, more precisely, that differences among students should be acknowledged —rather than treated as a number in a crowd or a faceless class of students (p. 107). Carried further, teaching to different learning styles suggests that “ all people have the potential to learn effectively and easily if only instruction is tailored to their individual learning styles ” (p. 107).

There may be another reason why this approach to learning styles is so widely accepted. They very loosely resemble the concept of metacognition , or the process of thinking about one’s thinking.  For instance, having your students describe which study strategies and conditions for their last exam worked for them and which didn’t is likely to improve their studying on the next exam (Tanner, 2012).  Integrating such metacognitive activities into the classroom—unlike learning styles—is supported by a wealth of research (e.g., Askell Williams, Lawson, & Murray-Harvey, 2007; Bransford, Brown, & Cocking, 2000; Butler & Winne, 1995; Isaacson & Fujita, 2006; Nelson & Dunlosky, 1991; Tobias & Everson, 2002).

Importantly, metacognition is focused on planning, monitoring, and evaluating any kind of thinking about thinking and does nothing to connect one’s identity or abilities to any singular approach to knowledge.  (For more information about metacognition, see CFT Assistant Director Cynthia Brame’s “ Thinking about Metacognition ” blog post, and stay tuned for a Teaching Guide on metacognition this spring.)

There is, however, something you can take away from these different approaches to learning—not based on the learner, but instead on the content being learned .  To explore the persistence of the belief in learning styles, CFT Assistant Director Nancy Chick interviewed Dr. Bill Cerbin, Professor of Psychology and Director of the Center for Advancing Teaching and Learning at the University of Wisconsin-La Crosse and former Carnegie Scholar with the Carnegie Academy for the Scholarship of Teaching and Learning.  He points out that the differences identified by the labels “visual, auditory, kinesthetic, and reading/writing” are more appropriately connected to the nature of the discipline:

“There may be evidence that indicates that there are some ways to teach some subjects that are just better than others , despite the learning styles of individuals…. If you’re thinking about teaching sculpture, I’m not sure that long tracts of verbal descriptions of statues or of sculptures would be a particularly effective way for individuals to learn about works of art. Naturally, these are physical objects and you need to take a look at them, you might even need to handle them.” (Cerbin, 2011, 7:45-8:30 )

Pashler and his colleagues agree: “An obvious point is that the optimal instructional method is likely to vary across disciplines” (p. 116). In other words, it makes disciplinary sense to include kinesthetic activities in sculpture and anatomy courses, reading/writing activities in literature and history courses, visual activities in geography and engineering courses, and auditory activities in music, foreign language, and speech courses.  Obvious or not, it aligns teaching and learning with the contours of the subject matter, without limiting the potential abilities of the learners.

• Askell-Williams, H., Lawson, M. & Murray, Harvey, R. (2007). ‘ What happens in my university classes that helps me to learn?’: Teacher education students’ instructional metacognitive knowledge. International Journal of the Scholarship of Teaching and Learning , 1. 1-21.
• Bransford, J. D., Brown, A. L. & Cocking, R. R., (Eds.). (2000). How people learn: Brain, mind, experience, and school (Expanded Edition). Washington, D.C.: National Academy Press.
• Butler, D. L., & Winne, P. H. (1995) Feedback and self-regulated learning: A theoretical synthesis . Review of Educational Research , 65, 245-281.
• Cerbin, William. (2011). Understanding learning styles: A conversation with Dr. Bill Cerbin .  Interview with Nancy Chick. UW Colleges Virtual Teaching and Learning Center .
• Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review . London: Learning and Skills Research Centre.
• Isaacson, R. M. & Fujita, F. (2006). Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning . Journal of the Scholarship of Teaching and Learning , 6, 39-55.
• Nelson, T.O. & Dunlosky, J. (1991). The delayed-JOL effect: When delaying your judgments of learning can improve the accuracy of your metacognitive monitoring. Psychological Science , 2, 267-270.
• Pashler, Harold, McDaniel, M., Rohrer, D., & Bjork, R.  (2008). Learning styles: Concepts and evidence . Psychological Science in the Public Interest . 9.3 103-119.
• Tobias, S., & Everson, H. (2002). Knowing what you know and what you don’t: Further research on metacognitive knowledge monitoring . College Board Report No. 2002-3 . College Board, NY.

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Discover Your Learning Style – Comprehensive Guide on Different Learning Styles

People differ in the way they absorb, process, and store new information and master new skills. Natural and habitual, this way does not change with teaching methods or learning content. This is known as the Learning Style.

By discovering and better understanding learning styles, one can employ techniques to improve the rate and quality of learning. Even if one has never heard the term “learning style” before, they are likely to have some idea of what their learning style is.

For instance, one may learn better through DIY videos instead of reading manuals or pick up things faster by listening to audiobooks instead of sitting down to read. These preferences point to one’s learning style.

How can learning style help in the classroom?

Students can have a single dominant learning style or a combination of styles, which could also vary based on circumstances. While no learning style (or a mix of them) is right or wrong, knowing one’s style can significantly enhance learning.

Research has shown that a mismatch between learning style and teaching can affect students’ learning and behavior quality in class. Studies have found that good learning depends on the teaching materials used, which must align with students’ learning styles.

In recent years, there has been a big push in education on how teachers can better meet students’ needs. Learning style has proven very effective in achieving this. It helps teachers understand how students absorb information and teach effectively.

One study found that over 90% of teachers believed in the learning style idea.

Often, teachers have a lot on their plates, and adjusting instruction to suit different learning styles can sound overwhelming. However, once they master how to appeal to all learners, life in the classroom becomes much easier.

This guide will help you understand various learning styles and how teachers can use them to alter instructions and help students learn more effectively.

This improves classroom management and makes for happier students. The chatty student who constantly interrupts will finally find a positive place in the classroom. The quiet girl who knows all the answers but never raises her hand will feel confident sharing her knowledge.

How can learning style help parents?

As a parent, knowing your child’s learning style helps you find activities and resources tailored to their specific learning styles. This allows you to better connect with them and provide the support they need, which also improves relationships.

Knowing learning styles is also helpful beyond educational settings. It helps you understand how those around you learn—at work, in families, in relationships, or in other settings.

Theory Of Learning Styles

The study of learning styles began in 1910 , and formal learning style assessment instruments were developed for academics in the 1970s. By the 1980s, the VAK model, which stands for Visual, Auditory, and Kinesthetic, had gained popularity in the mainstream media.

Thanks to the Internet, VAK became freely available to teachers for assessment by 2000. Later, another dominant style, reading/writing (R), was added to the VAK model, which expanded it to the VARK model.

The VARK model

The VARK learning style model has been adjusted to include four learning modes:

• Visual (spacial) learners learn best by seeing
• Auditory (aural) learners learn best by hearing
• Reading/writing learners learn best by reading and writing
• Kinesthetic (physical) learners learn best by moving and doing

A short questionnaire is used to identify what a learner prefers to use when taking in, processing, and outputting information.

VARK helps explain why it can sometimes be frustrating to sit in a classroom and not get what’s being taught. It also explains why some students learn well from one teacher but struggle to learn from another.

As a student, if you have experienced feelings like this, they are more likely to originate from an incompatibility with your learning style.

According to Neil Fleming and David Baume , who developed VARK, teachers should understand how students learn, but it’s even more important that students themselves know how they learn.

By identifying their own learning process, students can identify and test strategies that significantly improve learning efficiency. According to Fleming and Baume,

“VARK above all is designed to be a starting place for a conversation among teachers and learners about learning. It can also be a catalyst for staff development – thinking about strategies for teaching different groups can lead to more, and appropriate, variety of learning and teaching.”

This kind of thinking is known as metacognition , which refers to an awareness and understanding of one’s thought processes and how to regulate them. Discovering your own learning style without engaging in metacognition would be impossible.

Learning styles can also be multimodal —some have one dominant style, while others combine several learning styles.

Various learning theories, in addition to VAK and VARK, have been developed over time . While the labels used in each theory differ, the learning styles they define often overlap.

Memletics is another theory that was created in 2003 by Sean Whiteley . It expands upon the VAK model by introducing seven learning styles:

As shown, Memletics adds four more learning styles (Verbal, Logical, Social, and Solitary) to the three learning styles defined in the VAK model. However, it leaves out “Reading/Writing,” added when VAK expanded to VARK.

Due to the nature of these categories, there can be an overlap in learning styles defined in Memletics. Take two solitary learners, for instance. While both learn best in solitary situations, one may learn using logic, while the other may learn by seeing (Visual).

In a study on learning styles, Aranya Srijongjai noted that according to the Memletics model, everyone has a mix of learning styles, and learning styles are not fixed, so instructors should also accommodate other types of learning styles by providing diverse learning environments.

They should vary activities so that students learn in their preferred style and have a chance to develop other styles. Matching and mismatching learning styles and instructional methods will complement the student’s learning performance and create more flexible learners in the long run.

As Srijongjai suggests, students and teachers should not consider learning styles as boxes into which students can be placed. They are just one small piece of the overall puzzle in a student’s learning process.

No matter what learning style theory appeals to you the most, knowing your style helps make learning easier and more successful. Most learners will have at least one dominant style in the VARK theory.

This guide will offer information and advice to teachers, students, and parents to help them understand why and how people learn the way they do.

For each learning style, we have included suggestions for career choices, which in no way are meant to be limiting, but they can be helpful. If you are a visual learner but feel pulled toward one of the fields listed in the auditory learner section, by all means, pursue your passion.

These suggestions merely show what careers a person with a particular style might gravitate toward and where they are likely to excel with minimal effort.

Understanding your learning style is helpful, but again, you should also be careful not to put yourself in a box and define yourself by your learning style. The key is understanding how you learn and avoiding getting caught up in labels and classifications.

Take what insight you can, but don’t let it overcome your thoughts about yourself, as you may very well lie at the intersection of the “standard” learning styles:

Visual Learners

Do you ever remember taking a test in school and thinking, “I don’t remember the answer, but I remember I had it highlighted pink in my notes”? If the answer is yes, then you might be a visual learner.

Visual learners remember and learn best from what they see. This doesn’t necessarily have to be restricted to pictures and videos. They do well with spatial reasoning, charts, graphs, etc. Visual learners often “see” words as pictures or objects in their heads.

Visual learners use their right brain to process information. The human brain processes visual information much faster than plain text. Some reports claim that images are processed 60,000 times faster than text .

As a visual learner, you can quickly take in and retain a lot of information because you prefer this processing method that humans are already very good at.

Visual learners prefer using maps, outlines, diagrams, charts, graphs, designs, and patterns when studying and learning. They are more likely to organize their notes into visual patterns or separate their pages of notes into different sections. Many visual learners also do well by color-coding their notes.

Careers For Visual Learners

Visual learners are often drawn to and do well in STEM fields (Science, Technology, Engineering, and Math). Career options include Data Analytics & Visualization, Graphics Design, Photography, Architecture, Construction, Copy Editing, Interior Design, Physics, Advertising, Engineering, and Surgery.

A Note On Visual Learners For Teachers

Sometimes, students who are visual learners might stare out the window or doodle in their notes. If this is the case, let them do it. Locking their eyes constantly on you can be too visually stimulating for these students.

Sometimes, it’s the flower that they draw next to their notes that helps them remember the point by bringing out a visual connection.

It’s also easy for visual learners to get overwhelmed by a lot of visual input. If the classroom setting is chaotic, with many students moving around, it might be too much for these students to take in.

The design of a classroom is very important to visual learners. Clutter or too many posters adorning the walls can easily overwhelm their minds and processing.

Some visual learners may find it helpful to pay careful attention to your movements. They might even remember the silly hand motion you made or how you pointed to a country on the map. Keeping this in mind when delivering your lessons can be very effective.

Lesson Ideas To Help Visual Learners

Draw text and words.

Make it a habit to write new words and add a few quick context clues (e.g., putting the part of speech in brackets or underling the stressed syllable). Pick out a portion of the text with especially vivid imagery and instruct students to draw a picture of what the writing describes. This will help visual learners read and understand the text better.

Visual learners tend to color code things naturally. It can be helpful if you, as a teacher, also color code your notes as you write or post them. You could, for example, designate roles for certain areas of the board and use colors to organize information during the lesson.

Or, for homework or in-class assignments, you could have students annotate/read actively and use different colors for different things you want them to look for. For example, they could highlight dates in blue and names in yellow.

Use charts and graphs

Create charts and graphs to help students visualize information. While math and science subjects typically provide the ideal setting, they can be used in other disciplines as well.

For instance, in a social science class, students could track local election participation rates over ten years and create line graphs to visualize trends. This will give them a deeper understanding of civic involvement dynamics in their community.

Such assignments engage visual learners and allow them to recall information more easily, organize concepts, and articulate their thoughts more easily. Try:

• Venn Diagrams (that represent comparisons and contrasts)
• Timelines (to visually represent a series of events)
• Inverted Triangles (that go from broad topics to more specific ones).
• Story or Essay Planners (that guide students through the steps necessary to complete tasks)
• Charts to list word families – add columns for verbs, adjectives, adverbs, and nouns and fill them as words come up. (e.g., engage, engaging, engagingly, engagement)

Use posters and flashcards

As a project or class assignment, ask students to make posters illustrating key concepts. Students can even present their posters to the class – which would benefit auditory learners. Display these posters on the wall to help drive home important topics.

Flashcards also provide visual cues to young learners and can be used to teach various concepts. To build vocabulary, for example, the word “yummy” may be drawn as swirls of an ice cream cone that helps visual learners remember.

A number of classic games can also be designed using flashcards that help visual learners interact visually and learn better.

Draw reasoning

In math, teach students to draw out their reasoning. For example, instead of verbally explaining how to add 3 and 5, you could create a sketch that depicts two baskets with 3 and 5 apples each. Counting all the apples in your drawing visually demonstrates that 3 plus 5 equals 8, making it easier to understand.

Use gestures

Be aware of your body language when you teach. Including gestures and hand motions when you speak will help visual learners pay attention and make connections.

Auditory Learners

Do you sometimes talk to yourself when thinking hard, studying, or trying to organize something? If that sounds like you, you’re likely an auditory learner.

Auditory learners learn best by carefully hearing and listening. This can include listening to external sources and hearing themselves talk. They will likely volunteer to answer questions and actively participate in classroom discussions.

Auditory learners have a great advantage in the classroom because they are not afraid to speak their minds and easily get answers to their questions. Consequently, they process information very easily, right there in the classroom.

In contrast, reading/writing learners might not even realize they have a question until they’ve had time to go back and process their notes.

For auditory learners, any form of listening or speaking is the most efficient learning method. This can include lectures, audiobooks, discussions, and verbal processing. They are also typically good at storytelling and public speaking.

Many auditory learners prefer studying and working in groups because they prefer to talk through the information, which makes them “social learners,” as per Memletics.

Careers For Auditory Learners

Any job that requires a lot of listening and/or speaking will likely be an excellent fit for an auditory learner. Some career fields to consider include law, psychiatry or therapy, guidance counseling, customer service, sales, speech pathology, journalism, and teaching.

A Note On Auditory Learners For Teachers

Just like visual learners, even auditory learners might stare into space, but for a different reason. Since they process information best by listening, they don’t need to look at notes or PowerPoint very often. While this may seem like they are zoning out or not paying attention, it’s generally not the case.

If you’ve ever caught a student staring off into space and asked them a question, thinking you’ve caught them off guard, only to get the perfect answer, you’ve likely found a very auditory learner.

These students also tend to get chatty during class. This can be great when trying to get a lively class discussion or debate going but not when you need the class to listen intently.

Before this frustrates and angers you, remember that this is how their brain works and learns. As much as you can and as much as is practical based on the subject, try to facilitate discussions and play into this rather than squashing it.

Remember that auditory learners may really struggle with written and visual information.

These are the students who can answer every single question you ask in class and then score just 60% on an exam that tests the same information. If you suspect that a student who bombed a test actually knows much more, give them a chance to answer those questions verbally.

Lesson Ideas To Help Auditory Learners

For obvious reasons, audiobooks are perfect for auditory learners. Give these students the option to listen from an audiobook—this can be effective with both novels and textbooks.

Socratic Seminar

A Socratic seminar is a student-led discussion based on a text in which the teacher asks open-ended questions to begin with. Students listen closely to each other’s comments, think critically for themselves, and articulate their thoughts and responses to others’ thoughts.

They learn to work cooperatively and to question intelligently and civilly. Discussions usually occur in a circle, and the atmosphere is laid-back, encouraging every student to join the conversation.

Auditory learners often lead such discussions. It gives them a chance to shine and be rewarded for talking, which usually gets them in trouble otherwise.

Teacher Kelly Gallagher offers a great handout called Trace the Conversation that can help auditory and visual learners with Socratic seminars. There are many ways to conduct Socratic Seminars; the National Council of Teachers of English has a great explanation .

Speeches, the often hated but necessary school assignment many students dread, are a favorite of auditory learners. When it comes to speeches, auditory learners feel in their element. Speeches can be short and impromptu or long and planned, and they can be on any subject.

Recorded notes

You can either record yourself speaking or permit your students to record lectures so they can listen later. You can also encourage students to record themselves reading their notes.

Text to speech

Students can do this independently, but they might need your prompting or feel better about doing it if you permit them. Document processors like Microsoft Word and Google Docs have text-to-speech and speech-to-text embedded as standard.

Students can, for instance, use speech-to-text to capture their thoughts when writing essays. Text-to-speech can also be beneficial for proofreading and catching errors.

A structured debate is beneficial for auditory learners to get their ideas across. It can be done at all grade levels and in all disciplines. Here is a great resource for some debate ideas and different debate formats for different grade levels.

Reading/Writing Learners

Do you tend to zone out when people talk to you or when you hear a lecture? Would you instead read the transcript or get the information from a book? Then, you’re probably a reading/writing learner. You learn best by reading and writing.

Reading/writing learners often relate to the famous Flannery O’Connor’s quote: “I write because I don’t know what I think until I read what I say.”

For these learners, verbal input can often go into one ear and out from the other without much effect. Seeing notes on the board or a PowerPoint presentation is very important to them, as is taking notes.

These students learn best from books, lists, notes, journals, dictionaries, etc. They can also intuitively help themselves learn by rewriting notes, using flashcards, adding notes to pictures or diagrams, choosing a physical book over an audiobook, and using closed video captions.

Careers For Reading/Writing Learners

Writing is a common and obvious career choice for reading/writing learners, but if this is your learning style, you’re definitely not limited to writing. Editing, journalism, public relations, law, teaching and education, marketing, advertising, researching, translating, and economic advising are all excellent career choices.

A Note On Reading/Writing Learners For Teachers

Reading/writing learners are often your typical “good students.” However, they can really struggle to learn from lectures or completely auditory methods. They may not respond well to class discussions and need more time to process what they hear.

Help them by giving them time to write down their thoughts before asking them to share out loud. This will reduce their stress and allow them to process their thoughts.

As a teacher, you will likely encounter students who need more time to understand a concept, even after you have finished explaining it. These students are most likely reading/writing learners.

Knowing their learning style makes it easier to be more patient and provide them with the necessary support. They sometimes struggle to take notes because they try to write down everything you say. Help them by working with them to pull out the most important parts of your lecture and paraphrase what they hear.

Lesson Ideas To Help Reading/Writing Learners

No matter what the lesson is about, providing handouts highlighting the most important information is one of the best things you can do to help reading/writing learners. It’s also important to give them enough time to write detailed notes.

Essays and reading assignments

These simple assignments work best for reading/writing learners. This is why they often thrive in the traditional classroom setting.

Vocabulary stories

Have students create stories or plays to make their vocabulary words more fun and exciting. This can be done in any subject area that has vocabulary words.

You can give students a topic or let them be creative, but all they have to do is write a story containing x number of vocabulary words. You can also extend this activity to help kinesthetic learners by having students act out their stories for the class.

Think, pair, share

Reading/writing learners often struggle with sharing their thoughts out loud. They ace their tests but freeze when you call on them in the class. Think-pair-share can help give them the confidence they need to verbalize their thoughts and is suitable for most age groups in almost any subject area.

First, ask students an open-ended question and give them time to think silently and write their answers. Then, have students pair up in small groups to share their answers. Then, open the discussion to the whole class.

When you ask a question and want students to respond right away, you’ll likely get answers only from the auditory learners—they are the quickest at verbal processing.

With think-pair-share, the reading/writing learners get the time they need to process. In that time, they develop the confidence to construct a verbal response and are very likely to respond.

Kinesthetic Learners

Are you the first to stand up and volunteer to demonstrate an experiment for everyone else? Do you need to change the oil rather than look at a diagram to learn how to do it? If the answer is yes, then you are most likely a kinesthetic learner.

The root word “kines” means motion and a kinesthetic learner learns best by “going through the motion” or doing the task. It’s much easier for them to internalize the information when they are actively moving their body and combining that with what they are learning.

These students tend to shine in demonstrations and experiments. They also learn best from seeing something firsthand, like watching live videos and going on field trips.

Combining a physical motion, such as fidgeting, with a piece of information can help them learn better. They are likelier to use active gesturing and “talk with their hands.”

Careers For Kinesthetic Learners

Any career that allows physical activity and requires movement is right up the alley of a kinesthetic learner. These are the ones who often use the phrase “I don’t sit well.”

Kinesthetic learners typically don’t thrive well at desk jobs. Good career options for such learners include physical or occupational therapy, nursing, dance, theatre, music, automotive technology, welding, on-site engineering jobs, carpentry, agriculture, environmental science, forestry, and marine biology.

A Note On Kinesthetic Learners For Teachers

Just because you see a student fidgeting or being antsy, it doesn’t mean they aren’t paying attention or are bored. Their brain craves that movement to help them make connections.

There’s no need to force such students to sit entirely still as long as they aren’t distracting others in the classroom. Try to connect movement to the concepts you’re teaching as much as possible. Kinesthetic learners need to move, and they can benefit from active brain breaks.

Do your best to keep them active and allow movement in your classroom. If you notice a student with a glazed-over look, take a 30-second break from the lesson and have the entire class stand up, stretch, or do some jumping jacks.

Or you could ask your kinesthetic learner to run a quick errand to the office.

As students, kinesthetic learners often get punished for trying to move and follow their natural learning style. The more you can find ways to reward them for their learning style, the more engaged they will become.

Lesson Ideas To Help Kinesthetic Learners

Labs and experiments.

While labs and experiments are standard in science classes, they can also be successfully implemented in the curriculum of other subjects to benefit kinesthetic learners.

For example, an elementary math lesson could involve measuring each student’s height and creating problems based on the measurements. Geometry, for instance, could be taught using hands-on activities and tangible objects, like clay or building blocks, for better comprehension.

Field trips

With tightening school budgets, it can be hard to plan educational field trips, and that’s understandable. However, field trips need not have to be major events.

An art project, for example, could involve taking students outside and having them draw or photograph what they see. An English lesson could include a nature walk during which students journal or write a story about their little field trip.

Physical props

Use practical and/or memorable props; for example, when teaching a history lesson, dressing in the attire of the era you are teaching about will greatly impact kinesthetic learners. If you’re an anatomy teacher, consider using a model skeleton or demonstrating with your body as a helpful visual aid.

Take a stand

This activity is easy to set up and appeals to both kinesthetic and auditory learners. It requires you to prepare a series of questions that students can agree or disagree with.

For instance, if your students read “To Kill A Mockingbird,” your questions could revolve around racism. (Note: when tackling a sensitive subject such as racism, make sure you know your students and their maturity level)

Have signs on either side of your classroom indicating “agree” and “disagree.” Read through each question and have students move to the side of the room that fits their beliefs. Once there, they can discuss their thoughts with the group that follows their beliefs, and then you can open the discussion to the whole class.

This works well for literature and history lessons. Instead of reading silently, assign students parts and have them act out the story.

Tableaux Vivants

Tableaux Vivants is a time-tested process drama technique that can enhance students’ engagement and comprehension of abstract learning material across the curriculum. It works well in literature and history classrooms and is a great review activity. It is very similar to charades .

It involves breaking students into groups and assigning each group a “scene” – this could be from a work of literature or a scene from history. Each group then works together to create a silent re-enactment consisting of “snapshots” of the scene.

Students pose and pause for 5-10 seconds before moving on to their next pose. Once they have moved through all their poses, the rest of the class guesses what scene they were re-enacting.

Demonstration speeches

Einstein once said: “If you can’t explain it simply, you don’t understand it well enough.” Demonstration speeches allow students to explain something they understand well to their peers.

Ask your students to pick a topic, such as how to make a peanut butter and jelly sandwich.  Ask them to give a speech explaining the process while simultaneously demonstrating it.

The demonstration part appeals most to kinesthetic learners. Since students can choose their topic, it also appeals to all other learners, creating an engaging learning experience.

Logical Learners

If a child is good with numbers and asks many questions, they might be a logical learner. A logical learner has a core need to understand what is being learned. For them, simply memorizing facts is not enough. They thrive on orderly and sequential processes.

Individuals who excel at math and possess strong logical reasoning skills are usually logical learners. They notice patterns quickly and have a keen ability to link information that would seem unrelated to others. Logical learners retain details better by drawing connections after organizing an assortment of information.

As a logical learner, you can maximize your ability to learn by seeking to understand the meaning and reasoning behind the subject you’re studying. Avoid rote memorization.

Explore the links between related subject matter and ensure you understand the details. Use ‘systems thinking’ to better understand the relationship between various parts of a system. This will not only help you understand the bigger picture but also help you understand why each component is important.

Social Learners

Social learners have excellent written and verbal communication skills. They are at ease speaking to others and adept at comprehending other people’s perspectives. For this reason, people frequently seek counsel from them.

Social learners learn best when working with groups and take opportunities to meet individually with teachers.

If you like bouncing ideas off others, prefer working through issues as a group, and thoroughly enjoy working with others, you may be a social learner. Seek opportunities to study with others. If your class doesn’t have formal groups, form one.

Solitary Learners

Solitary learners prefer working by themselves in private settings. They avoid relying on others for help when solving problems or studying and frequently analyze their learning preferences and methods.

Solitary learners tend to waste a lot of time on a complex problem before seeking assistance. If you are a solitary learner, you must consciously recognize this limitation and try to seek help more often/sooner when stuck.

Generally, solitary learning can be a very effective learning style for students.

Tips to Simultaneously Help Learners of All Types

Lessons tailored to suit multiple learning styles are often the most effective, as they reach and appeal to most students. Another reason they are best suited is because most people have a combination of learning styles.

The activities discussed in this article provide ample opportunities for all types of learners to benefit. As a teacher, if you try to be creative, you can make little tweaks in almost every lesson to reach different learning styles.

Following are some ideas and ways by which to reach all four VARK learning styles:

Split your space into multiple stations (or centers) spread throughout the classroom. Break your students into groups so there is a group at every station.

Then, assign activities for each station that focus on a learning style. Have the students rotate with their groups from one station to another.

While the obvious benefit of rotation is that it ensures the activities cover each type of learner’s needs, there is more to it.

Even if you don’t have a center that caters to kinesthetic learners, the simple act of getting up and moving around different stations in the classroom helps them. The same goes for auditory learners; being in small groups and rotating throughout the room encourages discussion.

Give options

Irrespective of what subjects you teach your students, give them options as far as possible. For instance, instead of assigning an essay at the end of a unit, assign a project that can be completed with multiple activities.

Don’t mention which choices align with which learning style—let the students decide. Here is an example of 4 different options for a homework project:

• Write an essay (appeals to reading/writing learners)
• Record a podcast or TED talk (appeals to auditory learners)
• Film a video (appeals to kinesthetic learners)
• Create a poster or multimedia project (appeals to visual learners)

Quite often, students will naturally gravitate toward the option that best suits their learning style.

Allow students to use headphones when working independently in class. This helps cut out distractions for most learning types. Particularly for auditory learners, it can help make connections between what they hear and what they’re learning, which can be very helpful for them when they need to work silently.

Technology has made great strides and deep inroads into education. Several apps and websites can help students in various ways. Here is a list of apps for elementary math that could appeal to all four learning styles.

Games that include pictures and sound can help visual and auditory learners. Reading explanations and lessons on apps helps reading/writing learners. Physically manipulating and touching a device helps kinesthetic learners.

A quick online search will reveal several beneficial websites and apps for almost any discipline.

Final Words

There is no right or wrong when it comes to learning styles; they are simply names and categories assigned to how people’s brains process information.

It is generally easier for those with a dominant reading/writing style to succeed in a traditional academic setting, thus securing the “good student” label. However, education has come a long way, and schools and teachers can cater to various styles.

As a teacher, it’s important to remember that every student is unique. Even two visual learners might differ significantly in terms of what works for them. The best approach is to learn about and understand each student’s unique educational requirements.

After all, students are human beings with unique needs and feelings; teachers who remember this can approach them empathetically.

If you are a student interested in knowing about your learning style, you can begin by taking the VARK questionnaire . Having your students take the questionnaire is a good idea if you’re a teacher. Not only will you discover your student’s learning styles, but they will also be able to identify which techniques work best for them.

Remember, learning style is only a partial explanation of a student’s preferred way of learning. It is never the complete picture. These styles change over time, and every student can have differing degrees of inclination toward a given style.

However, regardless of your position in education, recognizing both your own learning styles and those of others around you can be highly beneficial.

Similar Posts:

• 35 of the BEST Educational Apps for Teachers (Updated 2024)
• 20 Huge Benefits of Using Technology in the Classroom
• 15 Learning Theories in Education (A Complete Summary)

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Home — Essay Samples — Education — Learning Styles — Importance Of Learning Styles In Teaching

Importance of Learning Styles in Teaching

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What Are Learning Styles, and How Should Teachers Use Them?

Encourage every student to explore material in a variety of ways.

Teachers are often told to make sure their lessons include a variety of activities to cover all learning styles. But what exactly does that mean?

FAQ: What Are Learning Styles?

Imagine you’re teaching a lesson on the presidency of John Adams. You’ll be giving a quiz on Friday, and you ask your students how they’ll be preparing. Some might say they’ll reread the text, then write down the answers to the review questions you’ve given them. Others might plan to watch a video on John Adams’ life, then talk over what they learned with a study partner. Another could plan to take the timeline handout you provided, cut it up into sections, and practice putting those sections in the proper order.

Each of these students is using different ways of learning in an effort to retain and understand the same information. Some like written words, some prefer to hear it and talk about it. Others need to do something with their hands, or see images and diagrams. These are all various learning styles.

What is VARK?

Source: Nnenna Walters

In the mid-1980s, teacher Neil Fleming introduced the VARK model of learning styles. He theorized that students learned in these four general ways, known as styles or modalities:

• Visual: Seeing images, diagrams, videos, etc.
• Auditory: Hearing lectures and having discussions
• Read/Write: Reading the written word and writing things down
• Kinesthetic: Movement and hands-on activities

Fleming developed a questionnaire ( try a version of it here ) that a student could take to see which learning styles they preferred. The results indicate how their learning preferences spread across the spectrum.

One important detail Fleming noted was that just because you prefer or are good at a certain learning activity, that doesn’t mean it’s actually the style that helps you learn the most. “It is possible to like something (preference) and not be good at it (skill or ability). Similarly it is possible to be very skilled at using strategies aligned with one of the VARK modalities but not use that for learning,” he explained . “As an example, a learner who was very skilled at freehand drawing did not use it for her learning and did not enjoy doing it.”

Are learning styles a proven theory?

There’s a lot of argument over the validity of learning styles. Some studies claim to have debunked the theory entirely , stating that everyone uses each of the various styles at one time or another. Critics worry that pigeonholing a student as an “auditory learner” or a “visual learner” might cause them to limit the ways they approach new material.

That said, learning styles are accepted and used in most education programs. Fleming himself published an article in 2012 defending his theory , explaining that it has been misunderstood and misused. His intention was never to limit the ways in which individual students were encouraged to learn. Rather, he wanted teachers to better understand all the different ways they could reach their students.

Does a person have just one learning style?

Source: VARK Research

Absolutely not, says Fleming. “Because learners have different preferences between the four modes, it is unlikely that any particular mode will be dominant. That is why multimodality is the most common profile for two-thirds of learners.”

In other words, while some people lean strongly toward one learning type (modality), most are more evenly spread across the spectrum. The way they learn best will depend on the material and situation. The key isn’t to find a single best way for a student to learn. It’s to discover  all the ways all students can learn.

How should teachers use learning styles in the classroom?

The biggest takeaway for teachers is that they should design their curriculum with activities that fit all four learning styles. If you’re teaching students about earthquakes, plan to provide reading material, watch videos, explore diagrams, and do some hands-on classroom experiments. This array of activities allows all students to find a way of learning that suits them best.

Avoid classifying particular students as one type of learner, even if you note a particular preference. Rather than thinking of “visual learn ers ,” think of “visual learn ing .” Encourage every student to explore material in a variety of ways, finding the methods that help them the most.

If a student is having trouble mastering material one way, offer alternatives. For instance, one student can’t seem to memorize their math facts. They spend time doing flash cards with their parents every day at home, but it’s just not sticking. Encourage the student to try something different. Maybe they can watch a video that sets the math facts to music. Or they might benefit from playing a matching game with the flash cards, or even hopping along the floor from one card to the next as they practice. Engage different senses in the process until you find one that brings the learning home.

The Four Learning Styles in the Classroom

Take a closer look at what the various learning styles look like in the classroom, and discover activities that support those styles.

Visual Learning

Source: TeachThought

When you’re learning visually, you’re engaging your eyes. These activities involve bright colors, graphic representations of concepts or materials, and lots of visual aids.

What It Looks Like:

• Diagrams, charts, graphs, and maps
• Videos, photos, and other images
• Drawing and doodling
• Interesting layouts and formats of written material
• Colors and shapes

Classroom Activities

• Math: Represent numbers and problems in a variety of ways, including pictures , 10-frames , videos, and diagrams
• Language Arts: Add images to support the text, like photos, charts, and maps; encourage students to make drawings representing what they read
• Science: Watch videos, look at pictures, analyze charts and diagrams
• Social Studies: Explore maps in detail, look at pictures, provide charts and diagrams, watch videos

Auditory Learning

Source: Argo Prep

Auditory (also called aural) learning is all about sound, both listening and talking. The more students get a chance to hear, say, and discuss the information, the better.

• Videos with sound
• Text-to-speech translation
• Discussion and debate
• Asking and answering questions
• Teaching others
• Math: Sing songs to learn numbers or math facts, talk through problems out loud while solving them, repeat formulas out loud, watch math videos
• Language Arts: Read out loud (to self or others), listen to audiobooks , practice spelling words out loud
• Science: Watch videos with sound, learn songs about concepts, discuss ideas and experiments
• Social Studies: Debate or discuss a topic, read articles out loud, watch videos with sound

Read/Write Learning

Source: Kwik Pass

As the name suggests, this style focuses on the written word. Whether reading or writing, it all comes down to seeing things in words on the page. Some people lump this modality with visual learning, but this style relies much more on written text than images.

• Textbooks, articles, and handouts
• Video with subtitles turned on
• Essays and papers
• Speech-to-text translation and transcripts
• Making lists, with subheads, bullet points, etc.
• Taking detailed notes
• Writing answers to questions instead of answering out loud
• Math: Complete worksheets, write down math facts and formulas, turn numerical problems into word problems
• Language Arts: Read independently, write essays or answers to questions, take notes during discussions or lectures
• Science: Read texts and articles, watch videos with subtitles turned on, take detailed notes during hands-on experiments and activities, write down concepts in your own words during lectures
• Social Studies: Read texts and articles, watch videos with subtitles turned on, write answers to review questions, take detailed notes during lectures, provide transcripts of videos

Kinesthetic Learning

Source: Open Library

When you tie movement to learning, that’s the kinesthetic style. This modality involves lots of hands-on activities and the ability to try things for yourself. Dancing and even exercise can reinforce this type of learning.

• Hands-on practice
• Educational craft projects
• Experiments and demonstrations
• Trial and error
• Exploring real-life examples of concepts and ideas
• Moving while learning
• Math: Play active math games ( see a list here ), practice counting while jumping or performing other movements, show how concepts apply in real-life situations, use lots of math manipulatives
• Language Arts: Read nonfiction and autobiographies, write letters in the air while spelling the words, perform a skit that summarizes a story, use active reading games ( see a list here )
• Science: Complete hands-on demonstrations, design and perform experiments , apply the concepts to real life
• Social Studies: Visit museums and historic sites, draw timelines, reenact historical events, try the dances and arts of a culture

Next, try these 21 Differentiated Instruction Strategies Every Teacher Can Use .

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10 Types of Learning and How to Teach Them: A Complete Guide to Learning Styles

If you have been a part of the education sector, you probably already know how different each child is. Every student has their own set of strengths and weaknesses which make them unique. Similarly, every student has a preferred way of learning and retaining the material. These preferences are called “learning styles”. 

Each learning style describes how a learner best receives information, interprets it, organizes it, and stores it. A majority of the learners today even have multiple or overlapping learning styles, also called multimodal learning . As an educator, it is extremely important to know the different types of learning – since this will help your students build on their strengths and retain information better. The original model of VARK only describes four of these - visual, auditory, reading/writing, and kinesthetic. However, in this article we explore 10 types of learning and how to teach them. 

Another factor that contributes to learning and retaining information is the subject of the information. It is a well known fact that some people excel better in creative fields like design, fine arts, photography, while others might excel in practical or calculative fields like mathematics, engineering, science, and so on.

Thus, different people have different interests and these interests contribute to different learning areas or subjects as well. 

1. Physical (Kinesthetic) Learning

Physical or kinesthetic learners prefer a hands-on experience rather than listening to lectures or sitting in a class. They like interacting physically with things that are tangible in nature. These learners could see the idea of studying for hours as a daunting experience but are better with actually doing things themselves. They possess qualities like being restless, preferring to get their hands “dirty”, outgoing and energetic.

Ways to engage physical learners:

• Encourage movement within lessons. Example: role play
• Give them well-spaced breaks between lessons to move around
• Use props and interactive models
• Declutter desks to promote better focus

2. Visual (Spatial) Learning

Visual or spatial learners learn best with the help of visual cues like charts, images, diagrams, graphs, etc. These learners respond best to colours and mind maps . They use their visual memory to retain information for longer periods of time. Many visual learners possess characteristics like frequent planning and doodling, they have a good attention span and are extremely observant, and they prefer visual directions.

Ways to engage visual learners:

• Use maps, diagrams, imagery
• Include technology like projectors
• Use colour coding techniques
• Encourage mind maps and flowcharts

3. Auditory Learning

People who tend to understand and retain information by hearing it or saying it out loud (oral) are called auditory learners. These types of learners can quickly notice the change in someone’s pitch, tone, and other voice qualities. They usually prefer discussing topics, participating in debates, and conversing about things to remember them. Most auditory learners are easy to distract and might even hum, sing, or talk to self frequently.

Ways to engage auditory learners:

• Try using different pitches and tones while reading the material
• Record voice lessons
• Encourage class presentations, group discussions, debates
• Ask them to teach others verbally

4. Verbal (Read/Write) Learning

These types of learners prefer traditional methods like using multiple written resources for learning. Verbal learners learn best through written material or by writing the material themselves. They usually possess a broad vocabulary and might even like using tools like acronyms, rhymes, tongue twisters, among others. Verbal learners are known to be bookworms.

Ways to engage verbal learners:

• Make use of mnemonics while teaching (song, rhyme, acronym, phrase)
• Inculcate scripts
• Encourage students to jot down and voice their ideas
• Include word games like crossword

5. Logical (Mathematical) Learning

Logical or mathematical learners tend to categorize information into groups to learn them better. They have a knack for quickly recognizing patterns and sequences; and understand equations, numbers, and relationships easily. These learners love structure and logic to things. Naturally, mathematics comes easy to them.

Ways to engage logical learners:

• Create an easy to navigate system to your lessons
• Try and inculcate statistics to subjects other than mathematics
• Classify concepts into groups or categories
• Generate cause-effect relationships between variables throughout all subject areas

6. Musical Learning

Where music or background noise is a distraction to most of us, musical learners prefer them. They tend to learn better with music, beats, and rhythm. Like logical learners, they too find patterns and relationships, but between different sounds. Some sources say they even think in sounds and rhythms instead of words and pictures.

Clearly, these learners often grow up to be musicians or instrumentalists. More often than never, some people are a combination of auditory and musical learners. This is why strategies to engage these two kinds aren’t too different.

Ways to engage musical learners:

• Encourage listening to soft background music
• Promote podcasts

7. Naturalist Learners

Naturalist learners learn best through experimentation and practical experiences. They like making observations of the world around them. Just like the name suggests, naturalistic learners are also said to be one with nature. They retain information best when they are outdoors, around plants, animals, among others. 

These types can also be somewhat related to kinesthetic learners since they appreciate tactile sensations. All-in-all, they apply scientific reasoning to the world around them and are highly interested in nature, as well as the things created by man.

Ways to engage naturalistic learners:

• Take students out for a field trip 
• Give lessons in outdoor spaces 
• Promote journaling, drawing, sketching, photographing or natural phenomena
• Encourage work that involves getting into nature (especially in subjects like biology)

8. Linguistic Learners 

Linguistic learners are the combination of auditory and verbal learners. They absorb knowledge best by writing, reading, and sounding the material out. These learners can use the traditional methods of learning just like verbal learners and also prefer listening to the information. Linguistic learners also make their own notes while studying. 

Ways to engage linguistic learners:

• Read out to them and have them read it back to you
• Include written projects and assignments
• Avoid using too many diagrams; use verbal methods of engaging them 
• Avoid using a monotonous voice; use different pitches, voices, and characters

9. Interpersonal (Social) Learners

Social or interpersonal learners learn best while working in groups or with other people. They often make good leaders and others even come for advice to them. Social learners learn by relating their ideas and thoughts to the lives of other people. These learners are usually empaths and possess qualities like sensitivity to others, excellent communication, leadership skills, and  problem-solving skills. This type of learning can fall adjacent to one or more types mentioned above. 

Ways to engage social learners:

• Figure out their adjacent learning style and inculcate those strategies 
• Encourage role-playing
• Assign group activities and projects

10. Intrapersonal (Solitary) Learners

In a complete contrast to interpersonal or social learners, intrapersonal or solitary learners prefer solitude while studying. They are more independent and introspective by nature and prefer to be with their own thoughts and ideas without too much external interference. Usually, you can find these types sitting at the back of the class or you might refer to them as the “quiet kid” but they may end up acing the exam. Solitary learning too can fall adjacent with other learning styles. 

Ways to engage solitary learners:

• Designate a quiet area
• Check in with them every once in a while 
• Define a specific time for collaboration so they feel prepared enough

To summarize, remember to not put your students in a box. It is not necessary and probably unlikely that a student is only one type of learner. There can exist multiple variations and combinations between their learning styles. This is also called a multimodal approach. According to statistics, more than 60% of people are multimodal learners. 

Acknowledge that each child is different and you might have the need to find varying ways to approach their education. Again, these differences can also exist with regards to the subject area. Certain people are simply better suited and more interested in some, while others might have completely opposing interests. These learning styles only provide you with a framework to follow.  

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What are the 7 different learning styles and do they work?

You may have heard of the idea that we all respond best to different styles of learning. That is exactly what the seven learning styles theory supports. All of the styles capture an individual strength that likely helps a person retain information more effectively. They each focus on one of the five senses or involve a social aspect. This theory is popular because, by finding an individual learner’s style and tailoring teaching to it, it was thought their efficiency could be improved. The 7 styles of the theory are:

• kinaesthetic

However, more recent studies have debunked this theory as an effective way of teaching and highlighted it as a neuromyth. This Guardian article says, ‘Such neuromyths create a false impression of individuals’ abilities, leading to expectations and excuses that are detrimental to learning in general, which is a cost in the long term.’

In other words, attempting to put learners into boxes and trying to only give them material that matches their “style” isn’t going to make them retain information any better. Most people benefit from a range of teaching techniques, and utilising different learning methods can actually improve learners’ adaptability.

Nevertheless, it’s certainly true that there are a variety of learning methods people respond to. So, just for fun, we’ve produced 7 different explanations of the 7 styles, each using techniques that learners of that style should find most useful.

Have a look through each one, and ask yourself: do you find them all equally engaging? Is there one (or more) that you prefer above the others? Maybe you have your own learning techniques that aren’t covered by any of the learning styles. Or perhaps you find one style more useful for this exercise, but when learning German verbs or mathematical formulae you know you prefer another? How effectively we learn isn’t just affected by the medium, but the content too.

While the 7 styles theory isn’t going to give you your one definitive style, you might still pick up a few useful techniques.

Visual or spatial learners supposedly retain information best by viewing pictures or images and respond well to colours and mind maps. These logos represent the main aspect of each learning style. Do you like to learn by remembering symbols and images?

Fill in the form to view a free, full sized, printable version.

Kinaesthetic

According to the theory, kinaesthetic learners are all about doing things physically. Role playing, using things like flashcards or carrying out the action physically can help them learn things better. Print and build this seven-sided die to see whether a hands-on approach could help you retain information.

Aural or auditory-musical learners should retain the most information after hearing it. Click below to listen to this recital of the different learning styles: do you tune out or find yourself remembering more than if you read the transcript?

Fill in the form to download and listen to the aural learning style.

Social, or interpersonal learners are meant to work best when they participate in study activities with other people such as quizzing each other or having a study group. Print and use these Top Trumps style cards with a group of friends.

Solitary, or intrapersonal learners supposedly work best alone. Making notes and reciting them back are useful activities when studying by yourself. Most of us will have to do some solitary revision at some point in our lives, so download and complete this worksheet to see if it works for you.

Verbal, or linguistic learners are supposed to respond well to written or spoken words, using tools like rhymes and acronyms. Download and complete this worksheet to figure out if these could be techniques that work for you.

Logical, or mathematical learners use logic and structures in order to learn effectively. If you’re good with numbers and statistics, you might find the logical style in this essay helpful. Have a read below:

What are the 7 Different Learning Styles?

Learning styles is the theory that learners can be categorised depending on how they take in information. Therefore, teaching students according to their specific learning styles will result in improved learning. While there is no concrete evidence to support the success of these learning styles, a 2012 study revealed that 93% of teachers in the UK agree that students learn better when they receive information in their preferred learning style.

These learning styles derived from Howard Gardner’s 1960s theory of Multiple Intelligences. This theory states that: “we are all able to know the world through language, logical-mathematical analysis, spatial representation, musical thinking, the use of the body to solve problems or to make things, an understanding of other individuals, and an understanding of ourselves.” This essay plans to outline the seven different learning styles while categorising them into three main categories: personal, sensory and informational. It will then recommend study methods for each type of learner.

1. Personal Learning Styles

The personal category links learning styles which depend on other persons to be present or absent. These are different from other learning styles which focus on how the learner takes in information, instead they depend greatly on the learners’ surroundings and whether they are studying with or without people. These types of styles split into Interpersonal learners or intrapersonal learners.

1.1 Interpersonal Learners

Interpersonal learners work best in groups and social elements help improve their concentration. Debates, group study and interactions are the best methods. Interestingly, while they work best in groups, they also have the most empathy when it comes to others. “Interpersonal intelligence builds on a core capacity to notice distinctions among others – in particular, contrasts in their moods, temperaments, motivations and intentions.”

1.2 Intrapersonal Learners

Intrapersonal learners are also known as solitary learners. Unlike interpersonal learners they work best when studying alone. They are known to be interested in philosophy, psychology and theology because of their proficiency in self-reflection.

“They’re in tune with their inner feelings; they have wisdom, intuition and motivation, as well as a strong will, confidence and opinions,” said a 2008 study on bridging educational divides. Unsurprisingly, these are the most independent learners from all the seven styles. Recommended study methods for intrapersonal learners include keeping a journal and finding a personal interest in the topics being studied.

2. Sensory Learning Styles

The sensory category links learning styles which use the senses. These are split into spatial/visual learners, auditory-musical learners and kinaesthetic learners. According to various studies of the sensory learning styles, roughly 65 percent of the population are visual learners, 30 percent are auditory learners and 5 percent are kinaesthetic learners. However, many students show traits of multiple learning styles.

2.1 Spatial Learners

Spatial learners are visualisers, which is why they’re also known as ‘visual learners’. As educational writer Stacy Mantle describes , these types of learners are good at working with colours and pictures and using the “mind’s eye.” Visual learners use spatial understanding; thus Gardner discusses that their problem solving is useful for navigation and map reading. This type of learning is also helpful for visualising an object from different angles and in playing chess.

2.2 Auditory-Musical Learners

Auditory-musical learners take in information through their sensitivity to rhythm and sound.  They have the capacity to discern pitch, rhythm, timbre, and tone . “Good or bad, their response to any music they hear is immediate, and they tend to be more in tune with nature sounds, and the sounds of their environment than their counterparts,” said Gilam in a study about multiple kinds of intelligence . The best methods for auditory-musical learners are to study with music in the background or to turn their notes into rhymes.

2.3 Kinaesthetic Learners

Kinaesthetic learners take in information through the use of their body and touch. Obvious kinaesthetic learners include dancers or surgeons. For these physical learners, a hands-on education and carrying out the activity themselves is more effective than listening to an explanation. According to Mantle, many of these kinaesthetic learners are often misdiagnosed as having Attention Deficit Hyper Activity Disorder, usually because they often have more energy than other types of learners.

3. Informational Learning Styles

The last category for the learning styles is informational, which refers simply to how the brain parses information, many in form of language or data. These learning styles do not depend on the senses or the learner’s social surroundings. Informational learners can be split into linguistic learners or mathematical learners.

3.1 Linguistic Learners

Linguistic learners, which are also known as verbal learners, work best with words. Whether information is spoken or written, these learners memorise information through language use. Gardner states “the linguistic intelligence is activated when individuals encounter the sounds of a language or when they wish to communicate something verbally to another person.” However, this learning style doesn’t correlate exclusively with the spoken word. For example, deaf people could demonstrate linguistic intelligence through the use of signs, according to Gardner.

For linguistic learners, recommended approaches include reading writing and telling stories. So taking notes while reading is a successful method of study.

3.2 Mathematical Learners

As the name implies, mathematical learners work best using numbers, structures and reasoning, this is why they are also referred to as logical learners. According to Mantle, these learners make the best engineers and work by categorising and classifying abstract patterns or relationships. Gardner notes a similarity between mathematical and musical learners, because both are drawn to structural patterns, which can often exist in music.

To summarise, despite the lack of substantial evidence supporting the success of these learning styles, they remain widely popular and are still used in schools throughout the country. According to this Wired article “Parents, understandably, like to think that their children are receiving a tailored education. Teachers, also understandably, like to think that they are sensitive to each child’s needs and many are clearly motivated to find out more about how to fulfil this ideal.” However, while there is still value in tailoring teaching methods based on the content and intended audience, attempting to strictly organise individuals into specific styles is not likely to be helpful, and could even prevent them from developing more rounded learning skills.

Jarrett goes on to describe how “learning is improved (for most everyone) by combining different activities – such as drawing alongside more passive study.” While it is not as useful as once thought for categorising learners, the 7 learning styles theory may still be of some use in making both teachers and learners alike aware of a greater variety of learning techniques and methods.

Our courses are designed to embed these fundamental learning styles so that our learners can cover the curriculum in a way that suits them. You can find our CIPD courses here, where you can request a brochure or start online today.

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Did you find any of these techniques helpful? You probably don’t fit neatly into one “style” in the way people used to think, but we hope this has helped you to figure out some of your preferred learning methods so you can plan for future study sessions!

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Learning Styles and Their Importance Research Paper

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Auditory learning style, visual learning style, tactile/kinesthetic, combination of learning styles.

• Learning styles are cognitive, affective and physiological factors that clearly define how a learner copes in a given environment.
• Auditory learning style entails using hearing senses to learn. It involves a teacher talking to learners. In other words, the learner requires information to be read aloud.
• This learning style requires a learner to use visuals to learn. These visuals include diagrams, charts, pictures and films. In other words, visual learners make use of their eyes to learn.
• Kinesthetic learners learn best by touching, feeling and experiencing things and material at hand.
• Learning styles are important in that they allow people to know and gauge forms of mental representations.

According to Keefe (1979), learning styles are cognitive, affective, and physiological factors that clearly define how a learner copes in a given environment. In other words, they are composite features that gauge whether a learner perceives, responds, and works well in a given learning environment. Learning styles can also be defined as educational conditions that favor learning (Felicetti and Stewart, 1992). It is, therefore, necessary to note that learning styles focus on how learners prefer to learn. This means learning styles should not be used to categorize people into learning divisions because people can learn in any of the learning styles. However, people prefer one style to the other.

The auditory learning style entails using hearing senses to learn. Auditory learners prefer the traditional mode of teaching, which takes a lecture form of teaching. It involves a teacher talking to learners. In other words, the learner requires information to be read aloud. In addition, when verbally presenting information, learners learn best when oral communication techniques are employed. These include voice tone variation to create different meanings.

Auditory learners exhibit unique characteristics. For example, when reading, they prefer to read aloud to themselves. During such a time, they perform two tasks; reading and, at the same time, listen to themselves. In a classroom setup, the auditory learners are not afraid to speak. They participate by asking and answering questions. Moreover, they portray expert explaining skills and, therefore, always want to study in groups. Their other learning strengths are that they are capable learners of foreign languages and grammar. They also follow spoken instructions well. Outside the classroom, an auditory learner likes listening to music, watching movies, and enjoys stage performance, such as acting. They also recall the names of other people and things, including those they hear in movies (Coffield et al. 2004).

They also employ techniques for succeeding in their studies. These include recording lectures as well as taping notes taken in order to listen to them. They also try to recall facts, and they do so by repeating aloud several times and using word association. These learners use audiotapes and watch videos while practicing the language. They attend discussion groups in which they participate actively.

This learning style requires a learner to use visuals to learn. These visuals include diagrams, charts, pictures, and films. In other words, visual learners make use of their eyes to learn. They prefer seeing things and internalizing them rather than hearing them. In the classroom setup, the visual learners prefer written notes and assignments that take to-do lists of nature. It is a reliable method of teaching because the majority of learners are capable of seeing. The learners under this category prefer to watch the teacher demonstrate things and videotapes.

Teachers of visual learners should emphasize visuals such as showing and demonstrating, as clearly as possible. That is, every step of the demonstration should follow all procedures and avoid short cuts. Just like the monkey concept, the teacher should ensure that the learners observe and do exactly as he demonstrates (Vincent, 2001).

Visual learners’ characteristics include learning or studying with charts and diagrams. In class, they prefer reading in a quiet environment. They are good at grammar and especially spelling. They exhibit a taste for colors and fashion as well as an interest in sign language. Since they don’t learn much in lectures, they prefer looking at what they did in class at their own time so as to fully understand.

To succeed in their studies, visual learners watch videos, take notes, outline reading, use highlighters, circle, or underline words. In addition, these learners prefer diagrams, map drawings, and flashcards. They also copy everything that the teacher demonstrates.

Kinesthetic learners learn best by touching, feeling, and experiencing things and material at hand. Good examples of these types of learners are kindergarten learners. They prefer touching and moving everything they come across. It is important to note that this learning style is maintained by the majority of learners, even as they become adult learners. This is not the case with other types of learning, such as visual learners who drop the style after elementary year and adopt auditory (Hayman-Abello and Warriner, 2002).

According to scholars, most of the learners prefer this learning style. This is because the learners are involved fully in the learning activity. Examples of learning activities that require active participation are science lab, theatre performance, and field trips. Teachers of kinesthetic learners should always give their learners a tactile sense of what to do. Repetition of what is being performed also helps the students to learn well.

Kinesthetic learners are said to be good in sports and other field activities such as science lab in which they play a role. In the classroom setup, these learners are not good at spelling and taking notes. Instead, they prefer studying in noisy environments and do not concentrate for long hours. They also prefer building models when learning. In addition, they like studying with others, such as in discussion groups. They also employ memory games and utilize flashcards when studying.

There is no learning style that can be said to be effective than the other. For this reason, the strong points of each style should be incorporated in learning to enhance learning. A combination of various learning styles is important because it results in a style that is innovative, and that involves diverse learning styles of learners. Merrill (2000) says that before using any learning style, it is important to understand the goals and objectives of learning. This helps one to come up with the best learning style.

There is successful learning when two or more learning styles are combined. For instance, when visual and auditory learning styles are combined, the learner employs listening skills such as noting the tonal variation to get different meanings and, at the same time, observe body movements to get extra information (Marzano, 1998).

Kinesthetic learning style is the most incorporated in other learning styles. Many teachers are now emphasizing a hands-on approach to learning. This means that education is not based on a physical approach, but it is taking a language arts approach. It is combined with other learning styles because apart from meeting its learner’s needs, it addresses other diverse needs, including those of auditory and visual learners.

Learning styles are important in that they allow people to know and gauge forms of mental representations. However, the styles should be perceived as preferences by the learners and not a way of dividing the learners according to their learning capabilities. Learning styles should be used to enlighten the learners about their weaknesses and strengths and those of others. Learning styles should be used hand in hand so as to complement each other.

Coffield, F. et al. (2004). Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review: Learning and Skills Research Centre. Web.

Hayman-Abello, S.E. & Warriner, E.M. (2002). Child Clinical/Pediatric Neuropsychology: Some Recent Advances. Annual Review of Psychology , 53: 309-339.

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Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

1 Department of Linguistics, School of International Studies, Zhejiang University, Hangzhou City, Zhejiang Province, China

2 Center for College Foreign Language Teaching, Zhejiang University, Hangzhou City, Zhejiang Province, China

3 Institute of Asian Civilizations, Zhejiang University, Hangzhou City, Zhejiang Province, China

Xueliang Chen

Associated data.

All relevant data are within the paper and its Supporting Information files.

Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan’s taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively determined students’ discipline-specific learning styles in a college blended learning setting.

Introduction

Research background

Learning style, as an integral and vital part of a student’s learning process, has been constantly discussed in the field of education and pedagogy. Originally developed from the field of psychology, psychological classification, and cognitive research several decades ago [ 1 ], the term “learning style” is generally defined as the learner’s innate and individualized preference for ways of participation in learning practice [ 2 ]. Theoretically, learning style provides a window into students’ learning processes [ 3 , 4 ], predicts students’ learning outcomes [ 5 , 6 ], and plays a critical role in designing individualized instruction [ 7 ]. Knowing a student’s learning style and personalizing instruction to students’ learning style could enhance their satisfaction [ 8 ], improve their academic performance [ 9 ], and even reduce the time necessary to learn [ 10 ].

Researchers in recent years have explored students’ learning styles from various perspectives [ 11 – 13 ]. However, knowledge of the learning styles of students from different disciplines in blended learning environments is limited. In an effort to address this gap, this study aims to achieve two major objectives. First, it investigates how disciplinary background impacts students’ learning styles in a blended learning environment based on data collected in a compulsory college English course. Students across 46 disciplines were enrolled in this course, providing numerous disciplinary factor resources for investigating learning styles. Second, it introduces a novel machine learning method named the SVM to the field of education to identify an optimal set of factors that can simultaneously differentiate students of different academic disciplines. Based on data for students from 46 disciplines, this research delves into the effects of a massive quantity of variables related to students’ learning styles with the help of a powerful machine learning algorithm. Considering the convergence of a wide range of academic disciplines and the detection of latent interactions between a large number of variables, this study aims to provide a clear picture of the relationship between disciplinary factors and students’ learning styles in a blended learning setting.

Literature review

Theories of learning styles

Learning style is broadly defined as the inherent preferences of individuals as to how they engage in the learning process [ 2 ], and the “cognitive, affective and physiological traits” of students have received special attention [ 14 ]. To date, there has been a proliferation of learning style definitions proposed to explain people’s learning preferences, each focusing on different aspects. Efforts to dissect learning style have been contested, with some highlighting the dynamic process of the learner’s interaction with the learning environment [ 14 ] and others underlining the individualized ways of information processing [ 15 ]. One vivid explication involved the metaphor of an onion, pointing out the multilayer nature of learning styles. It was proposed that the outermost layer of the learning style could change in accordance with the external environment, while the inner layer is relatively stable [ 16 , 17 ]. In addition, a strong concern in this field during the last three decades has led to a proliferation of models that are germane to learning styles, including the Kolb model [ 18 ], the Myers-Briggs Type Indicator model [ 19 ] and the Felder-Silverman learning style model (FSLSM) [ 20 ]. These learning style models have provided useful analytical lenses for analyzing students’ learning styles. The Kolb model focuses on learners’ thinking processes and identifies four types of learning, namely, diverging, assimilating, converging, and accommodating [ 18 ]. The Myers-Briggs Type Indicator model classifies learners into extraversion and introversion types, with the former preferring to learn from interpersonal communication and the latter inclining to benefit from personal experience [ 19 ]. As the most popular available model, the FSLSM identifies eight categories of learners according to the four dimensions of perception, input, processing and understanding [ 20 ]. In contrast to other learning style models that divided students into only a few groups, the FSLSM describes students’ learning styles in a more detailed manner. The four paired dimensions delicately distinguish students’ engagement in the learning process, providing a solid basis for a steady and reliable learning style analysis [ 21 ]. In addition, it has been argued that the FSLSM is the most appropriate model for a technology-enhanced learning environment because it involves important theories of cognitive learning behaviors [ 22 , 23 ]. Therefore, a large number of scholars have based their investigations of students’ learning styles in the e-learning/computer-aided learning environment on FSLSM [ 24 – 28 ].

Learning styles and FSLSM

Different students receive, process, and respond to information with different learning styles. A theoretical model of learning style can be used to categorize people according to their idiosyncratic learning styles. In this study, the FSLSM was adopted as a theoretical framework to address the collective impacts of differences in students’ learning styles across different disciplines (see Fig 1 ).

This model specifies the four dimensions of the construct of learning style: visual/verbal, sensing/intuitive, active/reflective, and sequential/global. These four dimensions correspond to four psychological processes: input, perception, processing, and understanding.

The FSLSM includes learning styles scattered among four dimensions

Visual learners process information best when it is presented as graphs, pictures, etc., while verbal learners prefer spoken cues and remember best what they hear. Sensory learners like working with facts, data, and experimentation, while intuitive learners prefer abstract principles and theories. Active learners like to try things and learn through experimentation, while reflective learners prefer to think things through before taking action. Sequential learners absorb knowledge in a linear fashion and make progress step by step, while global learners tend to grasp the big picture before filling in all the details.

Learning styles and academic disciplines

Learning styles vary depending on a series of factors, including but not limited to age [ 29 ], gender [ 30 ], personality [ 2 , 31 ], learning environment [ 32 ] and learning experience [ 33 ]. In the higher education context, the academic discipline seems to be an important variable that influences students’ distinctive learning styles, which echoes a multitude of investigations [ 29 , 34 – 41 ]. One notable study explored the learning styles of students from 4 clusters of disciplines in an academic English language course and proposed that the academic discipline is a significant predictor of students’ learning styles, with students from the soft-pure, soft-applied, hard-pure and hard-applied disciplines each favoring different learning modes [ 42 ]. In particular, researchers used the Inventory of Learning Styles (ILS) questionnaire and found prominent disparities in learning styles between students from four different disciplinary backgrounds in the special educational field of vocational training [ 43 ]. These studies have found significant differences between the learning styles of students from different academic disciplines, thus supporting the concept that learning style could be domain dependent.

Learning styles in an online/blended learning environment

Individuals’ learning styles reflect their adaptive orientation to learning and are not fixed personality traits. Consequently, learning styles can vary among diverse contexts, and related research in different contexts is vital to understanding learning styles in greater depth. Web-based technologies eliminate barriers of space and time and have become integrated in individuals’ daily lives and learning habits. Online and blended learning have begun to pervade virtually every aspect of the education landscape [ 40 ], and this warrants close attention. In addition to a series of studies that reflected upon the application of information and communication technology in the learning process [ 44 , 45 ], recent studies have found a mixed picture of whether students in a web-based/blended learning environment have a typical preference for learning.

Online learning makes it possible for students to set their goals and develop an individualized study plan, equipping them with more learning autonomy [ 46 ]. Generally, students with a more independent learning style, greater self-regulating behavior and stronger self-efficacy are found to be more successful in an online environment [ 47 ]. For now, researchers have made substantial contributions to the identification and prediction of learning styles in an online learning environment [ 27 , 48 – 51 ]. For instance, an inspiring study focused on the manifestation of college students’ learning styles in a purely computer-based learning environment to evaluate the different learning styles of web-learners in the online courses, indicating that students’ learning styles were significantly related to online participation [ 49 ]. Students’ learning styles in interactive E-learning have also been meticulously investigated, from which online tutorials have been found to be contributive to students’ academic performance regardless of their learning styles [ 51 ].

As a flexible learning method, blended courses have combined the advantages of both online learning and traditional teaching methods [ 52 ]. Researchers have investigated students’ learning styles within this context and have identified a series of prominent factors, including perceived satisfaction and technology acceptance [ 53 ], the dynamics of the online/face-to-face environment [ 54 ], and curriculum design [ 55 ]. Based on the Visual, Aural, Reading or Write and Kinesthetic model, a comprehensive study scrutinized the learning styles of K12 students in a blended learning environment, elucidating the effect of the relationship between personality, learning style and satisfaction on educational outcomes [ 56 ]. A recent study underscored the negative effects of kinesthetic learning style, whereas the positive effects of visual or auditory learning styles on students’ academic performance, were also marked in the context of blended learning [ 57 ].

Considering that academic disciplines and learning environment are generally regarded as essential predictors of students’ learning styles, some studies have also concentrated on the effects of academic discipline in a blended learning environment. Focusing on college students’ learning styles in a computer-based learning environment, an inspiring study evaluated the different learning styles of web learners, namely, visual, sensing, global and sequential learners, in online courses. According to the analysis, compared with students from other colleges, liberal arts students, are more susceptible to the uneasiness that may result from remote teaching because of their learning styles [ 11 ]. A similar effort was made with the help of the CMS tool usage logs and course evaluations to explore the learning styles of disciplinary quadrants in the online learning environment. The results indicated that there were noticeable differences in tool preferences between students from different domains [ 12 ]. In comparison, within the context of blended learning, a comprehensive study employed chi-square statistics on the basis of the Community of Inquiry (CoI) presences framework, arguing that soft-applied discipline learners in the blended learning environment prefer the kinesthetic learning style, while no correlations between the learning style of soft-pure and hard-pure discipline students and the CoI presences were identified. However, it is noted that students’ blended learning experience depends heavily on academic discipline, especially for students in hard-pure disciplines [ 13 ].

Research gaps and research questions

Overall, the research seems to be gaining traction, and new perspectives are continually introduced. The recent literature on learning styles mostly focuses on the exploration of the disciplinary effects on the variation in learning styles, and some of these studies were conducted within the blended environment. However, most of the studies focused only on several discrete disciplines or included only a small group of student samples [ 34 – 41 ]. Data in these studies were gathered through specialized courses such as academic English language [ 42 ] rather than the compulsory courses available to students from all disciplines. Even though certain investigations indeed boasted a large number of samples [ 49 ], the role of teaching was emphasized rather than students’ learning style. In addition, what is often overlooked is that a large number of variables related to learning styles could distinguish students from different academic disciplines in a blended learning environment, whereas a more comprehensive analysis that takes into consideration the effects of a great quantity of variables related to learning styles has remained absent. Therefore, one goal of the present study is to fill this gap and shed light on this topic.

Another issue addressed in this study is the selection of an optimal measurement that can effectively identify and differentiate individual learning styles [ 58 ]. The effective identification and differentiation of individual learning styles can not only help students develop greater awareness of their learning but also provide teachers with the necessary input to design tailor-made instructions in pedagogical practice. Currently, there are two general approaches to identify learning styles: a literature-based approach and a data-driven approach. The literature-based approach tends to borrow established rules from the existing literature, while the data-driven approach tends to construct statistical models using algorithms from fields such as machine learning, artificial intelligence, and data mining [ 59 ]. Research related to learning styles has been performed using predominantly traditional instruments, such as descriptive statistics, Spearman’s rank correlation, coefficient R [ 39 ], multivariate analysis of variance [ 56 ] and analysis of variance (ANOVA) [ 38 , 43 , 49 , 57 ]. Admittedly, these instruments have been applied and validated in numerous studies, in different disciplines, and across multiple timescales. Nevertheless, some of the studies using these statistical tools did not identify significant results [ 36 , 53 , 54 ] or reached only loose conclusions [ 60 ]; this might be because of the inability of these methods to probe into the synergistic effects of variables. However, the limited functions of comparison, correlation, prediction, etc. are being complemented by a new generation of technological innovations that promise more varied approaches to addressing social and scientific issues. Machine learning is one such approach that has received much attention both in academia and beyond. As a subset of artificial intelligence, machine learning deals with algorithms and statistical models on computer systems, performing tasks based on patterns and inference instead of explicit instruction. As such, it can deal with high volumes of data at the same time, perform tasks automatically and independently, and continuously improve its performance based on past experience [ 54 ]. Similar machine learning approaches have been proposed and tested by different scholars to identify students’ learning styles, with varying results regarding the classification of learning styles. For instance, a study that examined the precision levels of four computational intelligence approaches, i.e., artificial neural network, genetic algorithm, ant colony system and particle swarm optimization, found that the average precision of learning style differentiation ranged between 66% and 77% [ 61 ]. Another study that classified learning styles through SVM reported accuracy levels ranging from 53% to 84% [ 62 ]. A comparison of the prediction performance of SVM and artificial neural networks found that SVM has higher prediction accuracy than the latter [ 63 ]. This was further supported by another study, which yielded a similar result between SVM and the particle swarm optimization algorithm [ 64 ]. Moreover, when complemented by a genetic algorithm [ 65 ] and ant colony system [ 66 ], SVM has also shown improved results. These findings across different fields point to the reliability of SVM as an effective statistical tool for identification and differentiation analysis.

Therefore, a comprehensive investigation across the four general disciplines in Biglan’s taxonomy using a strong machine learning approach is needed. Given the existence of the research gaps discussed above, this exploratory study seeks to address the following questions:

• Can students’ learning styles be applied to differentiate various academic disciplines in the blended learning setting? If so, what are the differentiability levels among different academic disciplines based on students’ learning styles?
• What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?
• What are the collective impacts of optimal feature sets?

Materials and methods

This study adopted a quantitative approach for the analysis. First, a modified and translated version of the original ILS questionnaire was administered to collect scores for students’ learning styles. Then, two alternate data analyses were performed separately. One analysis involved a traditional ANOVA, which tested the main effect of discipline on students’ learning styles in each ILS dimension. The other analysis involved the support vector machine (SVM) technique to test its performance in classifying students’ learning styles in the blended learning course among 46 specializations. Then, SVM-based recursive feature elimination (SVM-RFE) was employed to specify the impact of students’ disciplinary backgrounds on their learning styles in blended learning. By referencing the 44 questions (operationalized as features in this study) in the ILS questionnaire, SVM-RFE could rank these features based on their relative importance in differentiating different disciplines and identify the key features that collectively differentiate the students’ learning style. These steps are intended to not only identify students’ learning style differences but also explain such differences in relation to their academic disciplinary backgrounds.

Participants

The participants included 790 sophomores taking the blended English language course from 46 majors at Z University. Sophomore students were selected for this study for two reasons. First, sophomores are one of the only two groups of students (the other group being college freshmen) who take a compulsory English language course, namely, the College English language course. Second, of these two groups of students, sophomores have received academic discipline-related education, while their freshmen counterparts have not had disciplinary training during the first year of college. In the College English language course, online activities, representing 55% of the whole course, include e-course teaching designed by qualified course teachers or professors, courseware usage for online tutorials, forum discussion and essay writing, and two online quizzes. Offline activities, which represent 45% of the whole course, include role-playing, ice-breaker activities, group presentations, an oral examination, and a final examination. Therefore, the effects of the academic discipline on sophomores’ learning styles might be sufficiently salient to warrant a comparison in a blended learning setting [ 67 ]. Among the participants, 420 were male, and 370 were female. Most participants were aged 18 to 19 years and had taken English language courses for at least 6 years. Based on Biglan’s typology of disciplinary fields, the students’ specializations were classified into the four broad disciplines of hard-applied (HA, 289/37.00%), hard-pure (HP, 150/19.00%), soft-applied (SA, 162/20.00%), and soft-pure (SP, 189/24.00%).

Biglan’s classification scheme of academic disciplines (hard (H) vs. soft (S) disciplines and pure (P) vs. applied (A) disciplines) has been credited as the most cited organizational system of academic disciplines in tertiary education [ 68 – 70 ]. Many studies have also provided evidence supporting the validity of this classification [ 69 ]. Over the years, research has indicated that Biglan’s typology is correlated with differences in many other properties and serves as an appropriate mechanism to organize discipline-specific knowledge or epistemologies [ 38 ] and design and deliver courses for students with different learning style preferences [ 41 ]. Therefore, this classification provides a convenient framework to explore differences across disciplinary boundaries. In general, HA disciplines include engineering, HP disciplines include the so-called natural sciences, SA disciplines include the social sciences, and SP disciplines include the humanities [ 41 , 68 , 71 ].

In learning style research, it is difficult to select an instrument to measure the subjects’ learning styles [ 72 ]. The criteria used for the selection of a learning style instrument in this study include the following: 1) successful use of the instrument in previous studies, 2) demonstrated validity and reliability, 3) a match between the purpose of the instrument and the aim of this study and 4) open access to the questionnaire.

The Felder and Soloman’s ILS questionnaire, which was built based on the FSLSM, was adopted in the present study to investigate students’ learning styles across different disciplines. First, the FSLSM is recognized as the most commonly used model for measuring individual learning styles on a general scale [ 73 ] in higher education [ 74 ] and has remained popular for many years across different disciplines in university settings and beyond. In the age of personalized instruction, this model has breathed new life into areas such as blended learning [ 75 ], online distance learning [ 76 ], courseware design [ 56 ], and intelligent tutoring systems [ 77 , 78 ]. Second, the FSLSM is based on previous learning style models; the FSLSM integrates all their advantages and is, thus, more comprehensive in delineating students’ learning styles [ 79 , 80 ]. Third, the FSLSM has a good predictive ability with independent testing sets (i.e., unknown learning style objects) [ 17 ], which has been repeatedly proven to be a more accurate, reliable, and valid model than most other models for predicting students’ learning performance [ 10 , 80 ]. Fourth, the ILS is a free instrument that can be openly accessed online (URL: https://www.webtools.ncsu.edu/learningstyles/ ) and has been widely used in the research context [ 81 , 82 ].

The modified and translated version of the original ILS questionnaire includes 44 questions in total, and 11 questions correspond to each dimension of the Felder-Silverman model as follows: questions 1–11 correspond to dimension 1 (active vs. reflective), questions 12–22 correspond to dimension 2 (sensing vs. intuitive), questions 23–33 correspond to dimension 3 (visual vs. verbal), and questions correspond 34–44 to dimension 4 (sequential vs. global). Each question is followed by five choices on a five-point Likert scale ranging from “strongly agree with A (1)”, “agree with A (2)”, “neutral (3)”, “agree with B (4)” and “strongly agree with B (5)”. Option A and option B represent the two choices offered in the original ILS questionnaire.

Ethics statements

The free questionnaires were administered in a single session by specialized staff who collaborated on the investigation. The participants completed all questionnaires individually. The study procedures were in accordance with the ethical standards of the Helsinki Declaration and were approved by the Ethics Committee of the School of International Studies, Zhejiang University. All participants signed written informed consent to authorize their participation in this research. After completion of the informed consent form, each participant was provided a gift (a pen) in gratitude for their contribution and participation.

Data collection procedure

Before the questionnaires were distributed, the researchers involved in this study contacted faculty members from various departments and requested their help. After permission was given, the printed questionnaires were administered to students under the supervision of their teachers at the end of their English language course. The students were informed of the purpose and importance of the study and asked to carefully complete the questionnaires. The students were also assured that their personal information would be used for research purposes only. All students provided written informed consent (see S2 File ). After the questionnaires were completed and returned, they were thoroughly examined by the researchers such that problematic questionnaires could be identified and excluded from further analysis. All questionnaires eligible for the data analysis had to meet the following two standards: first, all questions must be answered, and second, the answered questions must reflect a reasonable logic. Regarding the few missing values, the median number of a given individual’s responses on 11 questions per dimension included in the ILS questionnaire was used to fill the void in each case. In statistics, using the median number to impute missing values is common and acceptable because missing values represent only a small minority of the entire dataset and are assumed to not have a large impact on the final results [ 83 , 84 ].

In total, 850 questionnaires were administered to the students, and 823 of these questionnaires were retrieved. Of the retrieved questionnaires, the remaining 790 questionnaires were identified as appropriate for further use. After data screening, these questionnaires were organized, and their respective results were translated into an Excel format.

Data analysis method

During the data analysis, as a library of the SVM, the free package LIBSVM ( https://www.csie.ntu.edu.tw/~cjlin/libsvm/ ) was first applied as an alternative method of data analysis. Then, a traditional ANOVA was performed to examine whether there was a main effect of academic discipline on Chinese students’ learning styles. ANOVA could be performed using SPSS, a strong data analysis software that supports a series of statistical analyses. In regard to the examination of the effect of a single or few independent variables, SPSS ANOVA can produce satisfactory results. However, SVM, a classic data mining algorithm, outperforms ANOVA for dataset in which a large number of variables with multidimensions are intertwined and their combined/collective effects influence the classification results. In this study, the research objective was to efficiently differentiate and detect the key features among the 44 factors. Alone, a single factor or few factors might not be significant enough to discriminate the learning styles among the different disciplines. Selected by the SVM, the effects of multiple features may collectively enhance the classification performance. Therefore, the reason for selecting SVM over ANOVA is that in the latter case, the responses on all questions in a single dimension are summed instead of treated as individual scores; thus, the by-item variation is concealed. In addition, the SVM is especially suitable for statistical analysis with high-dimensional factors (usually > 10; 44-dimensional factors were included in this study) and can detect the effects collectively imposed by a feature set [ 85 ].

Originally proposed in 1992 [ 86 ], the SVM is a supervised learning model related to machine learning algorithms that can be used for classification, data analysis, pattern recognition, and regression analysis. The SVM is an efficient classification model that optimally divides data into two categories and is ranked among the top methods in statistical theory due to its originality and practicality [ 85 ]. Due to its robustness, accurate classification, and prediction performance [ 87 – 89 ], the SVM has high reproducibility [ 90 , 91 ]. Due to the lack of visualization of the computing process of the SVM, the SVM has been described as a “black box” method [ 92 ]; however, future studies in the emerging field of explainable artificial intelligence can help solve this problem and convert this approach to a “glass box” method [ 67 ]. This algorithm has proven to have a solid theoretical foundation and excellent empirical application in the social sciences, including education [ 93 ] and natural language processing [ 94 ]. The mechanism underlying the SVM is also presented in Fig 2 .

Hyperplanes 1 and 2 are two regression lines that divide the data into two groups. Hyperplane 1 is considered the best fitting line because it maximizes the distance between the two groups.

The SVM contains the following two modules: one module is a general-purpose machine learning method, and the other module is a domain-specific kernel function. The SVM training algorithm is used to build a training model that is then used to predict the category to which a new sample instance belongs [ 95 ]. When a set of training samples is given, each sample is given the label of one of two categories. To evaluate the performance of SVM models, a confusion matrix, which is a table describing the performance of a classifier on a set of test data for which the true values are known, is used (see Table 1 ).

Note . Positive: Observation is positive (e.g., the students belong to this discipline); Negative: Observation is negative (e.g., the students do not belong to this discipline); True Positive (TP): Observation is positive and is predicted to be positive; False Negative (FN): Observation is positive but is predicted to be negative; True Negative (TN): Observation is negative and is predicted to be negative; False Positive (FP): Observation is negative but is predicted to be positive.

Based on the confusion matrix, several indicators were developed to measure the performance of SVM models; of these indicators, the five most common indicators include accuracy (ACC), specificity (SPE), sensitivity (SEN) (also known as ‘recall’), area under the receiver operating characteristic curve (AUC), and F-measure. All five values were used in this study as performance evaluators of the SVM models and generally have a value ranging from 0 to 1. The mathematical formulae used to produce these values are provided as follows, along with a brief explanation of their functions:

ACC represents the proportion of true results, including both positive and negative results, in the selected population;

SPE represents the proportion of actual negatives that are correctly identified as such;

SEN represents the proportion of actual positives that are correctly identified as such;

AUC is a ranking-based measure of classification performance that can distinguish a randomly chosen positive example from a randomly chosen negative example; and

F-measure is the harmonic mean of precision (another performance indicator) and recall.

The ACC is a good metric frequently applied to indicate the measurement of classification performance, but the combination of the SPE, SEN, AUC, F-measure and ACC may be a measure of enhanced performance assessment and was frequently applied in current studies [ 96 ]. In particular, the AUC is a good metric frequently applied to validate the measurement of the general performance of models [ 97 ]. The advantage of this measure is that it is invariant to relative class distributions and class-specific error costs [ 98 , 99 ]. Moreover, to some extent, the AUC is statistically consistent and more discriminating than the ACC with balanced and imbalanced real-world data sets [ 100 ], which is especially suitable for unequal samples, such as the HA-HP model in this study. After all data preparations were completed, the data used for the comparisons were extracted separately. First, the processed data of the training set were run by using optimized parameters. Second, the constructed model was used to predict the test set, and the five indicators of the fivefold cross-validation and fivefold average were obtained. Cross-validation is a general validation procedure used to assess how well the results of a statistical analysis generalize to an independent data set, which is used to evaluate the stability of the statistical model. K-fold cross-validation is commonly used to search for the best hyperparameters of SVM to achieve the highest accuracy performance [ 101 ]. In particular, fivefold, tenfold, and leave-one-out cross-validation are typically used versions of k-fold cross-validation [ 102 , 103 ]. Fivefold cross-validation was selected because fivefold validation can generally achieve a good prediction performance [ 103 , 104 ] and has been commonly used as a popular rule of thumb supported by empirical evidence [ 105 ]. In this study, five folds (groups) of subsets were randomly divided from the entire set by the SVM, and four folds (training sample) of these subsets were randomly selected to develop a prediction model, while the remaining one fold (test sample) was used for validation. The above functions were all implemented with Python Programming Language version 3.7.0 (URL: https://www.python.org/ ).

Then, SVM-RFE, which is an embedded feature selection strategy that was first applied to identify differentially expressed genes between patients and healthy individuals [ 106 ], was adopted. SVM-RFE has proven to be more robust to data overfitting than other feature selection techniques and has shown its power in many fields [ 107 ]. This approach works by removing one feature each time with the smallest weight iteratively to a feature rank until a group of highly weighted features were selected. After this feature selection procedure, several SVM models were again constructed based on these selected features. The performance of the new models is compared to that of the original models with all features included. The experimental process is provided in Fig 3 for the ease of reference.

The classification results produced by SVM and the ranking of the top 20 features produced by SVM-RFE were listed in Table 2 . Twenty variables have been selected in this study for two reasons: a data-based reason and a literature-based reason. First, it is clear that models composed of 20 features generally have a better performance than the original models. The performance of models with more than 20 is negatively influenced. Second, SVM-based studies in the social sciences have identified 20 to 30 features as a good number for an optimal feature set [ 108 ], and 20 features were selected for inclusion in the optimal feature set [ 95 ]. Therefore, in this study, the top 20 features were selected for subsequent analysis, as proposed in previous analyses that yielded accepted measurement rates. These 20 features retained most of the useful information from all 44 factors but with fewer feature numbers, which showed satisfactory representation [ 96 ].

Note : Indicators in the upper row belong to original SVM models, while those in the lower row belong to the models with 20 features.

Results of RQ (1) What are the differentiability levels among different academic disciplines based on students’ learning styles?

To further measure the performance of the differentiability among students’ disciplines, the collected data were examined with the SVM algorithm. As shown in Table 2 , the five performance indicators, namely, the ACC, SPE, SEN, AUC and F-measure, were utilized to measure the SVM models. Regarding the two general performance indicators, i.e., the ACC value and AUC value, the HA-HP, HA-SA, and HA-SP-based models yielded a classification capacity of approximately 70.00%, indicating that the students in these disciplines showed a relatively large difference. In contrast, the models based on the H-S, A-P, HP-SA, HP-SP, and SA-SP disciplines only showed a moderate classification capacity (above 55.00%). This finding suggests that these five SVM models were not as effective as the other three models in differentiating students among these disciplines based on their learning styles. The highest ACC and AUC values were obtained in the model based on the HA-HP disciplines, while the lowest values were obtained in the model based on the HP-SA disciplines. As shown in Table 2 , the AUCs of the different models ranged from 57.76% (HP-SA) to 73.97% (HA-HP).

To compare the results of the SVM model with another statistical analysis, an ANOVA was applied. Prior to the main analysis, the students’ responses in each ILS dimension were summed to obtain a composite score. All assumptions of ANOVA were checked, and no serious violations were observed. Then, an ANOVA was performed with academic discipline as the independent variable and the students’ learning styles as the dependent variable. The results of the ANOVA showed that there was no statistically significant difference in the group means of the students’ learning styles in Dimension 1, F(3, 786) = 2.56, p = .054, Dimension 2, F(3, 786) = 0.422, p = .74, or Dimension 3, F(3, 786) = 0.90, p = .443. However, in Dimension 4, a statistically significant difference was found in the group means of the students’ learning styles, F (3, 786) = 0.90, p = .005. As the samples in the four groups were unbalanced, post hoc comparisons using Scheffé’s method were performed, demonstrating that the means of the students’ learning styles significantly differed only between the HA (M = 31.04, SD = 4.986) and SP (M = 29.55, SD = 5.492) disciplines, 95.00% CI for MD [0.19, 2.78], p = .016, whereas the other disciplinary models showed no significant differences. When compared with the results obtained from the SVM models, the three models (HA-HP, HA-SA, and HA-SP models) presented satisfactory differentiability capability of approximately 70.00% based on the five indicators.

In the case of a significant result, it was difficult to determine which questions were representative of the significant difference. With a nonsignificant result, it was possible that certain questions might be relevant in differentiating the participants. However, this problem was circumvented in the SVM, where each individual question was treated as a variable and a value was assigned to indicate its relative importance in the questionnaire. Using SVM also circumvented the inherent problems with traditional significance testing, especially the reliance on p-values, which might become biased in the case of multiple comparisons [ 109 ].

Results of RQ (2) What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?

To examine whether the model performance improved as a result of this feature selection procedure, the 20 selected features were submitted to another round of SVM analysis. The same five performance indicators were used to measure the model performance (see Table 2 ). By comparing the performance of the SVM model and that of the SVM-RFE model presented in Table 2 , except for the HA-SP model, all other models presented a similar or improved performance after the feature selection process. In particular, the improvement in the HA-HP and HP-SA models was quite remarkable. For instance, in the HA-HP model, the ACC value increased from 69.32% in the SVM model to 82.59% in the SVM-RFE model, and the AUC score substantially increased from 73.97% in the SVM model to 89.13% in the SVM-RFE model. This finding suggests that the feature selection process refined the model’s classification accuracy and that the 20 features selected, out of all 44 factors, carry substantive information that might be informative for exploring disciplinary differences. Although results for the indicators of the 20 selected features were not very high, all five indicators above 65.00% showed that the model was still representative because only 20 of 44 factors could present the classification capability. Considering that there was a significant reduction in the number of questions used for the model construction in SVM-RFE (compared with those used for the SVM model), the newly identified top 20 features by SVM-RFE were effective enough to preserve the differential ability of all 44 questions. Thus, these newly identified top 20 factors could be recognized as key differential features for distinguishing two distinct disciplines.

To identify these top 20 features in eight models (see Table 2 ), SVM-RFE was applied to rank order all 44 features contained in the ILS questionnaire. To facilitate a detailed understanding of what these features represent, the questions related to the top 20 features in the HA-HP model are listed in Table 3 for ease of reference.

Note . Question descriptions and answer options were openly accessed online from the ILS (URL: https://www.webtools.ncsu.edu/learningstyles/ ).

Results of RQ (3) What are the collective impacts of optimal feature sets?

The collective impacts of optimal feature sets could be interpreted from four aspects, namely, the complexities of students’ learning styles, the appropriate choice of SVM, the ranking of SVM-RFE and multiple detailed comparisons between students from different disciplines. First, the FSLSM considers the fact that students’ learning styles are shaped by a series of factors during the growth process, which intertwine and interact with each other. Considering the complex dynamics of the learning style, selecting an approach that could detect the combined effects of a group of variables is needed. Second, recent years have witnessed the emergence of data mining approaches to explore students learning styles [ 28 , 48 – 50 , 110 ]. Specifically, as one of the top machine learning algorithms, the SVM excels in identifying the combined effects of high-order factors [ 87 ]. In this study, the SVM has proven to perform well in classifying students’ learning styles across different disciplines, with every indicator being acceptable. Third, the combination of SVM with RFE could enable the simultaneous discovery of multiple features that collectively determine classification. Notably, although SVM-FRE could rank the importance of the features, they should be regarded as an entire optimal feature set. In other words, the combination of these 20 features, rather than a single factor, could differentiate students’ learning styles across different academic disciplines. Last but not least, the multiple comparisons between different SVM models of discipline provide the most effective learning style factors, giving researchers clues to the nuanced differences between students’ learning styles. It can be seen that students from different academic disciplines understand, see and reflect things from individualized perspectives. The 20 most effective factors for all models scattered within 1 to 44, verifying students’ different learning styles in 4 dimensions. Therefore, the FSLSM provides a useful and effective tool for evaluating students’ learning styles from a rather comprehensive point of view.

The following discussions address the three research questions explored in the current study.

Levels of differentiability among various academic disciplines based on students’ learning styles with SVM

The results suggest that SVM is an effective approach for classification in the blended learning context in which students with diverse disciplinary backgrounds can be distinguished from each other according to their learning styles. All performance indicators presented in Tables ​ Tables2 2 and ​ and3 3 remain above the baseline of 50.00%, suggesting that between each two disciplines, students’ learning style differences can be identified. To some extent, these differences can be identified with a relatively satisfactory classification capability (e.g., 69.32% of the ACC and 73.97% of the AUC in the HA-HP model shown in Table 2 ). Further support for the SVM algorithm is obtained from the SVM-RFE constructed to assess the rank of the factors’ classification capacity, and all values also remained above the baseline value, while some values reached a relatively high classification capability (e.g., 82.59% of the ACC and 89.13% of the AUC in the HA-HP model shown in Table 2 ). While the results obtained mostly show a moderate ACC and AUC, they still provide some validity evidence supporting the role of SVM as an effective binary classifier in the educational context. However, while these differences are noteworthy, the similarities among students in different disciplines also deserve attention. The results reported above indicate that in some disciplines, the classification capacity is not relatively high; this was the case for the model based on the SA-SP disciplines.

Regarding low differentiability, one explanation might be the indistinct classification of some emerging “soft disciplines.” It was noted that psychology, for example, could be identified as “a discipline that can be considered predominantly ‘soft’ and slightly ‘purer’ than ‘applied’ in nature” [ 111 ] (p. 43–53), which could have blurred the line between the SA and SP disciplines. As there is now no impassable gulf separating the SA and SP disciplines, their disciplinary differences may have diminished in the common practice of lecturing in classrooms. Another reason comes from the different cultivation models of “soft disciplines” and “hard disciplines” for sample students. In their high school, sample students are generally divided into liberal art students and science students and are then trained in different environments of knowledge impartation. The two-year unrelenting and intensive training makes it possible for liberal art students to develop a similar thinking and cognitive pattern that is persistent. After the college entrance examination, most liberal art students select SA or SP majors. However, a year or more of study in university does not exert strong effects on their learning styles, which explains why a multitude of researchers have traditionally investigated the SA and SP disciplines together, calling them simply “social science” or “soft disciplines” compared with “natural science” or “hard disciplines”. There have been numerous contributions pointing out similarities in the learning styles of students from “soft disciplines” [ 37 , 112 – 114 ]. However, students majoring in natural science exhibit considerable differences in learning styles, demonstrating that the talent cultivation model of “hard disciplines” in universities is to some extent more influential on students’ learning styles than that of the “soft disciplines”. Further compelling interpretations of this phenomenon await only the development of a sufficient level of accumulated knowledge among scholars in this area.

In general, these results are consistent with those reported in many previous studies based on the Felder-Silverman model. These studies tested the precision of different computational approaches in identifying and differentiating the learning styles of students. For example, by means of a Bayesian network (BN), an investigation obtained an overall precision of 58.00% in the active/reflective dimension, 77.00% in the sensing/intuitive dimension and 63.00% in the sequential/global dimension (the visual/verbal dimension was not considered) [ 81 ]. With the help of the keyword attributes of learning objects selected by students, a precision of 70.00% in the active/reflective dimension, 73.30% in the sensing/intuitive dimension, 73.30% in the sequential/global dimension and 53.30% in the visual/verbal dimension was obtained [ 115 ].

These results add to a growing body of evidence expanding the scope of the application of the SVM algorithm. Currently, the applications of the SVM algorithm still reside largely in engineering or other hard disciplines despite some tentative trials in the humanities and social sciences [ 26 ]. In addition, as cross-disciplines increase in current higher education, it is essential to match the tailored learning styles of students and researchers studying interdisciplinary subjects, such as the HA, HP, SA and SP disciplines. Therefore, the current study is the first to incorporate such a machine learning algorithm into interdisciplinary blended learning and has broader relevance to further learning style-related theoretical or empirical investigations.

Verification of the features included in the optimal feature sets

Features included in the optimal feature sets provided mixed findings compared with previous studies. Some of the 20 identified features are verified and consistent with previous studies. A close examination of the individual questions included in the feature sets can offer some useful insights into the underlying psychological processes. For example, in six of the eight models constructed, Question 1 (“I understand something better after I try it out/think it through”) appears as the feature with the number 1 ranking, highlighting the great importance attached to this question. This question mainly reflects the dichotomy between experimentation and introspection. A possible revelation is that students across disciplines dramatically differ in how they process tasks, with the possible exception of the SA-SP disciplines. This difference has been supported by many previous studies. For example, it was found that technical students tended to be more tactile than those in the social sciences [ 116 ], and engineering students (known as HA in this study) were more inclined toward concrete and pragmatic learning styles [ 117 ]. Similarly, it was explored that engineering students prefer “a logical learning style over visual, verbal, aural, physical or solitary learning styles” [ 37 ] (p. 122), while social sciences (known as SA in this study) students prefer a social learning style to a logical learning style. Although these studies differ in their focus to a certain degree, they provide an approximate idea of the potential differences among students in their relative disciplines. In general, students in the applied disciplines show a tendency to experiment with tasks, while those in the pure disciplines are more inclined towards introspective practices, such as an obsession with theories. For instance, in Biglan’s taxonomy of academic disciplines, students in HP disciplines prefer abstract rules and theories, while students in SA disciplines favor application [ 67 ]. Additionally, Question 10 (“I find it easier to learn facts/to learn concepts”) is similar to Question 1, as both questions indicate a certain level of abstraction or concreteness. The difference between facts and concepts is closely related to the classification difference between declarative knowledge and procedural knowledge in cognitive psychology [ 35 , 38 ]. Declarative knowledge is static and similar to facts, while procedural knowledge is more dynamic and primarily concerned with operational steps. Students’ preferences for facts or concepts closely correspond to this psychological distinction.

In addition, Questions 2, 4, 7, and 9 also occur frequently in the 20 features selected for the different models. Question 2 (“I would rather be considered realistic/innovative”) concerns taking chances. This question reflects a difference in perspective, i.e., whether the focus should be on obtaining pragmatic results or seeking original solutions. This difference cannot be easily connected to the disciplinary factor. Instead, there are numerous factors, e.g., genetic, social and psychological factors, that may play a strong role in defining this trait. The academic discipline only serves to strengthen or diminish this difference. For instance, decades of research in psychology have shown that males are more inclined towards risk taking than females [ 118 – 121 ]. A careful examination of the current academic landscape reveals a gender difference; more females choose soft disciplines than males, and more males choose hard disciplines than females. This situation builds a disciplinary wall classifying students into specific categories, potentially strengthening the disciplinary effect. For example, Question 9 (“In a study group working on difficult material, I am more likely to jump in and contribute ideas/sit back and listen”) emphasizes the distinction between active participation and introspective thinking, reflecting an underlying psychological propensity in blended learning. Within this context, the significance of this question could also be explained by the psychological evaluation of “loss and gain”, as students’ different learning styles are associated with expected reward values and their internal motivational drives, which are determined by their personality traits [ 122 ]. When faced with the risk of “losing face”, whether students will express their ideas in front of a group of people depends largely on their risk and stress management capabilities and the presence of an appropriate motivation system.

The other two questions also convey similar messages regarding personality differences. Question 4 concerns how individuals perceive the world, while Question 7 concerns the preferred modality of information processing. Evidence of disciplinary differences in these respects was also reported [ 35 , 123 – 125 ]. The other questions, such as Questions 21, 27, and 39, show different aspects of potential personality differences and are mostly consistent with the previous discussion. This might also be a vivid reflection of the multi-faceted effects of blended learning, which may differ in their consonance with the features of each discipline. First, teachers from different domains use technology in different ways, and student from different disciplines may view blended learning differently. For instance, the characteristics of soft-applied fields entail specialized customization in blended courses, further broadening the gulf between different subjects [ 126 ]. Second, although blended learning is generally recognized as a stimulus to students’ innovation [ 127 ], some students who are used to an instructivist approach in which the educator acts as a ‘sage on the stage’ will find it difficult to adapt to a social constructivist approach in which the educator serves as a ‘guide on the side’ [ 128 ]. This difficulty might not only negatively affect students’ academic performance but also latently magnify the effects of different academic disciplines.

Interpretation of the collective impact of optimal feature sets

In each SVM model based on a two-discipline model, the 20 key features (collectively known as an optimal feature set) selected exert a concerted effect on students’ learning styles across different disciplines (see Table 2 ). A broad examination of the distribution of collective impact of each feature set with 20 features in the eight discipline models suggests that it is especially imperative considering the emerging cross-disciplines in academia. Current higher education often involves courses with crossed disciplines and students with diverse disciplinary backgrounds. In addition, with the rise of technology-enhanced learning, the design of personalized tutoring systems requires more nuanced information related to student attributes to provide greater adaptability [ 59 ]. By identifying these optimal feature sets, such information becomes accessible. Therefore, understanding such interdisciplinary factors and designing tailor-made instructions are essential for promoting learning success [ 9 ]. For example, in an English language classroom in which the students are a blend of HP and SP disciplines, instructors might consider integrating a guiding framework at the beginning of the course and stepwise guidelines during the process such that the needs of both groups are met. With the knowledge that visual style is dominant across disciplines, instructors might include more graphic presentations (e.g., Question 11) in language classrooms rather than continue to use slides or boards filled with words. Furthermore, to achieve effective communication with students and deliver effective teaching, instructors may target these students’ combined learning styles. While some methods are already practiced in real life, this study acts as a further reminder of the rationale underlying these practices and thus increases the confidence of both learners and teachers regarding these practices. Therefore, the practical implications of this study mainly concern classroom teachers and educational researchers, who may draw some inspiration for interdisciplinary curriculum design and the tailored application of learning styles to the instructional process.

Conclusions

This study investigated learning style differences among students with diverse disciplinary backgrounds in a blended English language course based on the Felder-Silverman model. By introducing a novel machine learning algorithm, namely, SVM, for the data analysis, the following conclusions can be reached. First, the multiple performance indicators used in this study confirm that it is feasible to apply learning styles to differentiate various disciplines in students’ blended learning processes. These disciplinary differences impact how students engage in their blended learning activities and affect students’ ultimate blended learning success. Second, some questions in the ILS questionnaire carry more substantive information about students’ learning styles than other questions, and certain underlying psychological processes can be derived. These psychological processes reflect students’ discipline-specific epistemologies and represent the possible interaction between the disciplinary background and learning style. In addition, the introduction of SVM in this study can provide inspiration for future studies of a similar type along with the theoretical significance of the above findings.

Despite the notable findings of this study, it is subject to some limitations that may be perfected in further research. First, the current analysis examined the learning styles without allowing for the effects of other personal or contextual factors. The educational productivity model proposed by Walberg underlines the significance of the collected influence of contextual factors on individuals’ learning [ 129 ]. For example, teachers from different backgrounds and academic disciplines are inclined to select various teaching methods and to create divergent learning environments [ 130 ], which should also be investigated thoroughly. The next step is therefore to take into account the effects of educational background, experience, personality and learning experience to gain a more comprehensive understanding of students’ learning process in the blended setting.

In conclusion, the findings of this research validate previous findings and offer new perspectives on students’ learning styles in a blended learning environment, which provides future implications for educational researchers, policy makers and educational practitioners (i.e., teachers and students). For educational researchers, this study not only highlights the merits of using machine learning algorithms to explore students’ learning styles but also provides valuable information on the delicate interactions between blended learning, academic disciplines and learning styles. For policy makers, this analysis provides evidence for a more inclusive but personalized educational policy. For instance, in addition to learning styles, the linkage among students’ education in different phases should be considered. For educational practitioners, this study plays a positive role in promoting student-centered and tailor-made teaching. The findings of this study can help learners of different disciplines develop a more profound understanding of their blended learning tendencies and assist teachers in determining how to bring students’ learning styles into full play pedagogically, especially in interdisciplinary courses [ 131 – 134 ].

Supporting information

Acknowledgments.

The authors would like to thank the anonymous reviewers for their constructive comments on this paper and Miss Ying Zhou for her suggestions during the revision on this paper.

Funding Statement

This research was supported by the Philosophical and Social Sciences Planning Project of Zhejiang Province in 2020 [grant number 20NDJC01Z] with the recipient Jie Hu, Second Batch of 2019 Industry-University Collaborative Education Project of Chinese Ministry of Education [grant number 201902016038] with the recipient Jie Hu, SUPERB College English Action Plan with the recipient Jie Hu, and the Fundamental Research Funds for the Central Universities of Zhejiang University with the recipient Jie Hu.

Data Availability

• PLoS One. 2021; 16(5): e0251545.

Decision Letter 0

PONE-D-20-14494

Differentiating the learning styles of EFL students with different disciplines in a blended learning setting: Insights from a machine learning-based algorithm

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Reviewer #2: No

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Reviewer #1: This study aims to introduce an innovative machine learning algorithm known as support vector machine (SVM) and specify the impact of students’ disciplinary background on their learning styles in EFL blended learning, which is interesting and investigation worthy. The method is inspiring and well-designed. The authors may further improve this paper in the following points.

1) There is no literature review in this paper, leading to the insufficient establishment of theoretical framework of this paper. Before leading in the research method, a few questions from three main aspects are supposed to be answered based on the literature: (a) “what is the definition of learning style?” “What are the major learning styles of EFL learning/blended learning/EFL blended learning as shown in the literature?” “From which aspects could learning styles influence EFL learning/blended learning/EFL blended learning?” (b) “How could disciplines influence learning styles?” “How could learning methods influence learning styles?” (c) “What are the explicit limitations of SPSS which results in the incapability of it to address the present research problems?” A rigid theoretical framework would also be helpful in the analysis of the research results so as to make the discussion section better-structured and more in-depth.

2) It is suggested to point out the research questions clearly.

3) It is suggested to add a section about the practical implications for future researchers and educators.

4) The paper is well-presented in general, in spite of a few points in need of further improvement. For example, in the 1st paragraph, the statement “Knowing a student’s learning style has many obvious benefits” may not be in an intelligible fashion as expected on a research paper. The authors are suggested to make revisions to the language.

Reviewer #2: This manuscript tested the precision of different computational approaches in identifying and differentiating college sophomore students’ learning styles based on the Felder–Silverman model. Results showed that although no difference was found in participants’ learn style across different disciplinary groups, SVM-RFE machine learning algorithm showed that learning styles can differentiate various disciplines in EFL students’ blended learning processes as confirmed by the multiple performance indicators and further, some questions in the ILS questionnaire carry more substantive information about students’ learning styles than others. Overall, the manuscript is clearly written and the statistics are robust; I have the following concerns regarding the theoretical assumption and data interpretation of the study before its acceptance for publication.

A major concern is that all the statistical comparisons of the current manuscript are based on the basic assumption that disciplinary differences predict learning style differences. Results showed that SVM-REF machine learning algorithm can be applied to show high percentage of correspondence between learning style and disciplinary backgrounds in some comparisons, such as HA-HP groups, whereas almost no such correspondence was found with ANOVA comparisons, which revealed no significant difference in learning styles across different participant groups. established on this, the manuscript argued that the SVM-REF is logically a better method because it is closer to reflect truth. However, the fallacy here is that the “assumed TRUTH” may not be “the truth”. No evidence showed such a correspondence between disciplinary background and learning style of ALL individuals. Learning styles have been shown to be related to a whole multitude of factors, such as individuals’ personality, leaning background, experience, etc. Disciplinary background is only one of them, maybe a minor one out of many. Thus, the “non-significant result” might be true here. While the “significant result” might not be the truth. This logic fallacy brings my biggest concern regarding the research design of the manuscript.

Hidden in the sophisticated descriptions of all statistical methods and algorithms, the manuscript did not provide literature to support such a clear route and corresponding relationship between disciplinary background and learning style. Ref 24 is related but not directly supporting it. Without consideration of all other factors and only entering the single disciplinary factor into the algorithm arbitrarily is not substantiated. Indeed the results did show non-significant(relatively low performance) in some backgrounds. In the discussion the authors attributed this to the fact that these students chose courses from other disciplines and that lecturing in EFL classrooms regardless of the students’ background information diminished their disciplinary differences. (Line 388-390). Since all students are in such a protocol, I am thinking why there are significant results in other discipline comparisons.

My other concern is that although the tile claims “learning styles of EFL students” explicitly, the current data is not in essence related specifically to blended EFL learning. The questionnaire adopted in the current study is tapping into learners’ general learning style. The only thing that is related to EFL is that questionnaires were collected in EFL class time period. Such items as “When I solve math problems …”(P12) in the questionnaire show that is not speicifically EFL learning style.

Line 63-65: Ref26 is emphasizing the importance of real world models instead of using machine learning blindly. However, this paragraph here the authors are highting the merits of machine learning. the logic is not consistent here.

Table titles can be shortened. For example, in table 2 such words as “Constructed in This Study” are redundant.

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Author response to Decision Letter 0

14 Nov 2020

We would like to express our sincere thanks for reviewing our manuscript entitled “Differentiating the learning styles of EFL students with different disciplines in a blended learning setting: Insights from a machine learning-based algorithm” and providing us an opportunity to make corrections in it. As per your valuable comments, the manuscript has been revised and all the amendments suggested by the reviewers have been incorporated. Due to limited space here, please kindly review the ‘Authors’ Responses to Reviewers’ Comments’ file for point-to-point responses in detail, which is more than 10 pages. Once again, we are thankful to all of the reviewers for promote the quality of our manuscript!

Submitted filename: Authors Responses to Reviewers Comments.docx

Decision Letter 1

22 Dec 2020

PONE-D-20-14494R1

Differentiating the learning styles of college students with different disciplines in a blended learning setting: Insights from a machine learning-based algorithm

Please submit your revised manuscript by Feb 05 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Please revise the manuscript according to review comments.

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

3. Has the statistical analysis been performed appropriately and rigorously?

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: The authors have carefully revised this paper and addressed all the comments. Remarkable improvements are spotted.

1. The title is very informative but can be shorter and catchier.

2. The introduction is a little bit mixed with the literature review. Since this paper involves several different topics, I suggest the authors divide the current “Introduction” into several sections so your readers can follow you more easily. I suggest a division as follows: (1) a short section for a catchy introduction briefly specifying the research background, research rationales and research questions; (2) a section for literature review, in which the descriptions of the theories on learning styles and blended learning are organized separately in different sub-sections and catchy sub-titles; (3) a section for research gaps and research questions.

3. The methodology is well written, with clear organization and sufficient justification.

4. The authors may double check the use of punctuation. For example, in line 564, “soft discipline”. to “soft discipline.”

5. The coherence and logics within the paragraphs may be further improved, especially in the discussion and implication sections. For example, in line 581-587, the authors used “However” in two successive sentences, causing logical incoherence.

6. The authors raised many examples in the discussions, which is good. I suggest the authors put more specific explanations for their arguments in the discussions. Examples may be used as evidence to SUPPORT the arguments and explanations, rather than REPLACE them.

Reviewer #2: The revised manuscript has addressed all my concerns. The revised manuscript is improved significantly and should　be a nice contribution to the literature. The abstract is a big heavy in sentence structure, the authors may consider condense it a little bit before publication.

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Author response to Decision Letter 1

Responses to Reviewer #1

The authors have carefully revised this paper and addressed all the comments. Remarkable improvements are spotted.

Response to comment 1

Many thanks to the reviewer for this positive comment.

The title is very informative but can be shorter and catchier.

Response to comment 2

Thanks to Reviewer 1 for this recommendation. We have shortened the title “Differentiating the learning styles of college students with different disciplines in a blended learning setting: Insights from a machine learning-based algorithm” to “Differentiating the learning styles of college students with different disciplines in a college English blended learning setting” to make it precise while still informative enough.

The introduction is a little bit mixed with the literature review. Since this paper involves several different topics, I suggest the authors divide the current “Introduction” into several sections so your readers can follow you more easily. I suggest a division as follows: (1) a short section for a catchy introduction briefly specifying the research background, research rationales and research questions; (2) a section for literature review, in which the descriptions of the theories on learning styles and blended learning are organized separately in different sub-sections and catchy sub-titles; (3) a section for research gaps and research questions.

Response to comment 3

Many thanks to the reviewer for this invaluable suggestion. Since the format of PLoS ONE suggests only five Level 1 headings, namely, introduction, materials and methods, results, discussions, and conclusions; thus, we took the effort to divide the Level 1 introduction section into three Level 2 sub-sections, namely, research background, literature review and research gaps and research questions according to the suggestions to make it clearly structured and friendly readable.

(1) In the research background sub-section, we have briefly introduced the research rationale and research significance. Modifications were made in the manuscript as a newly developed section in blue font, which is also provided here as follows for the ease of reference:

Learning style, as an integral and vital part of students’ learning process, has always been a long-lasting and hot-debated topic in the field of education and pedagogy……

However, knowledge of the learning styles of students from different disciplines in blended learning environments is limited [11-13]. In an effort to address this gap, the present study is designed to achieve two major objectives. First, it intends to gain insight on how disciplinary background impacts students’ learning styles in a blended learning environment based on data collected in a compulsory college English course in which students across 46 disciplines were enrolled. Second, it introduces a novel machine learning method named the support vector machine (SVM) to the field of education to identify an optimal set of factors that can simultaneously differentiate students of different academic disciplines. This research aims to provide a clearer picture of the relationship between disciplinary factors and the identification of students’ learning styles in a blended learning setting.

(2) In the literature review sub-section, we have divided the original mixed and confusing part into two sub-sections with clear sub-titles, where the theories of learning style and blended learning were examined successively. In the first Level 3 sub-section entitled “Theories of learning styles”, definitions, influencing factors and models of learning styles were investigated. The examination of the conceptual framework of the research, namely, Felder-Silverman learning style model (FSLSM) was also included. In the second Level 3 sub-section named “Learning styles in an online/blended learning environment”, scholarly works with regard to learning styles in both web-based environment and blended learning environment were examined. The revised part is marked in blue font and is presented here as follows:

Literature Review

The examination of a constellation of definitions, interpretations and constructions pertaining to the term learning style could provide a solid footing for the related studies, through which the understanding of it can be enriched and extended……

Learning styles vary according to a series of factors, including but not limited to age [22], gender [23], personality [2, 24], learning environment [25] and learning experience [26]. In the higher education context, the academic discipline prominently represents an omnipresent variable that influences students’ distinctive learning styles, which echoes a multitude of investigations [27-35]. One notable study explored the learning styles of students from 4 clusters of disciplines in an academic English language course, ……

Different students receive, process, and respond to information with different learning styles. A theoretical model of learning style can be used to categorize people according to their idiosyncratic learning styles.…….

The FSLSM includes learning styles scattered among four dimensions. Visual learners process information best when it is presented as graphs, pictures, etc.,…….

Individuals’ learning styles reflect their adaptive orientation to learning and are not fixed personality traits. Consequently, learning styles can vary among diverse contexts, and related research in different contexts is vital to understanding learning styles in greater depth. Web-based technologies eliminate barriers of space and time and have become integrated in individuals’ daily lives and learning habits.…...

Considering that academic disciplines and learning environment are both potent predictors of students’ learning styles, many studies have also concentrated on the effects of academic discipline in a blended learning environment.……

(3) In the sub-section for research gaps and research questions, we have briefly summarized the research gaps of the abovementioned scholarly works and elaborated on the two main objectives. This section is now listed below for the reviewer’s reference, and the modifications were highlighted in blue font here and in the revised manuscript:

Overall, the research seems to be gaining traction, and new perspectives are continually introduced ……. Therefore, one goal of the present study is to fill this gap and shed light on this topic.

Another issue addressed in this study is the selection of an optimal measurement that can effectively identify and differentiate individual learning styles [43]……These findings across different fields point to the reliability of SVM as an effective statistical tool for identification and differentiation analysis.

1) Can students’ learning styles be applied to differentiate various academic disciplines in the blended learning setting? If so, what are the levels of differentiability among different academic disciplines based on students’ learning styles?

2) What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?

3) What are collective impacts of optimal feature sets?

The methodology is well written, with clear organization and sufficient justification.

Response to comment 4

We would like to thank the reviewer very much for this positive comment.

The authors may double check the use of punctuation. For example, in line 564, “soft discipline”. to “soft discipline.”

Response to comment 5

Thanks to the reviewer for pointing out this problem. Indeed, according to the rules of punctuation, we should put the full stop in front of the quotation marks in Line 570. At the same time, similar problems were checked throughout the manuscript and were revised if necessary.

The coherence and logics within the paragraphs may be further improved, especially in the discussion and implication sections. For example, in line ‪581-587‬, the authors used “However” in two successive sentences, causing logical incoherence.‬‬‬‬‬‬‬‬‬‬‬‬‬

Response to comment 6

Many thanks to the reviewer for this constructive suggestion. We felt very sorry that we did not pay attention to the repeated use of the word “However” in the arguments in the original manuscript, which caused unnecessary misunderstandings. The first “However” in Line 587 was initially conceived to indicate a huge difference between two group of students, namely, students majoring in natural science and their liberal art counterparts. The second use of “However” was indicative of the insufficient knowledge about the causal effects of this interpretation. According to the suggestions, we have made revisions to this argument and tried to make it more applicable. We have kept the first “However” and changed the expression in Line 591. The revised sentence is provided below in blue font for reference:

However, students majoring in natural science exhibit considerable differences in learning styles, demonstrating that the talent cultivation model of “hard disciplines” in universities is to some extent more influential on students’ learning styles than that of the “soft disciplines”. Further compelling interpretations of this phenomenon await only the development of a sufficient level of accumulated knowledge among scholars in this area.

The authors raised many examples in the discussions, which is good. I suggest the authors put more specific explanations for their arguments in the discussions. Examples may be used as evidence to SUPPORT the arguments and explanations, rather than REPLACE them.

Response to comment 7

Our heartfelt thanks go to the reviewer for this invaluable and helpful suggestion. Following the reviewer’s suggestion, we did carefully add more specific explanations of the results, using examples as evidence to support our arguments. The revised part is provided below in blue font for ease of reference:

In addition, Questions 2, 4, 7, and 9 also occur frequently in the 20 features selected for the different models.…… Within this context, the significance of this question could also be explained by the psychological evaluation of “loss and gain”, as students’ different learning styles are associated with expected reward values and their internal motivational drives, which are determined by their personality traits [112]. When faced with the risk of “loosing face”, whether students will express their ideas in front of a group of people depends largely on their risk and stress management capabilities and the presence of an appropriate motivation system.

The other two questions also convey similar messages regarding personality differences.…... This might also be a vivid reflection of the multi-faceted effects of blended learning, which may differ in their consonance with the features of each discipline. First, teachers from different domains use technology in different ways, and student from different disciplines may view blended learning differently. For instance, the characteristics of soft-applied fields entail specialized customization in blended courses, further broadening the gulf between different subjects [116]. Second, although blended learning is generally recognized as a stimulus to students’ innovation [117], some students who are used to an instructivist approach in which the educator acts as a ‘sage on the stage’ will find it difficult to adapt to a social constructivist approach in which the educator serves as a ‘guide on the side’ [118]. This difficulty might not only negatively affect students’ academic performance but also latently magnify the effects of different academic disciplines.

Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Response to comment 8

Thank you very much for the reviewer’s valuable suggestion. During the revision, we carefully proof-read the entire manuscript and improved the language by asking our colleagues to review the main text and the supporting materials. The native language of our colleagues is English, and they are all from my research fields in my university. Base on their suggestions, necessary modifications and improvements to the manuscript were made, which were all highlighted in blue font in the revised manuscript.

In addition, this version of manuscript was sent to the professional proof-reading agency American Journal Experts (AJE) for English language editing, which is the official English language editing agency partner of PLOS. The proof-reading quality certificate (AJE Order NO.: MCF1YPS3) with the title of this manuscript was offered.

Responses to Reviewer #2

The revised manuscript has addressed all my concerns. The revised manuscript is improved significantly and should be a nice contribution to the literature.

The abstract is a big heavy in sentence structure, the authors may consider condense it a little bit before publication.

The reviewer’s valuable suggestion is greatly appreciated. According to the suggestion, we have condensed the abstract according to the suggestion and made the sentence structure more precise. The revised abstract is provided as below in blue font for the reviewer’s reference:

Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan’s taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively impacted the students’ learning style differences in a college English blended learning setting.

Decision Letter 2

PONE-D-20-14494R2

Differentiating the leanrning styles of college students with different disciplines in a college English blended learning setting

Please submit your revised manuscript by Mar 19 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please revise the paper according to review comments.

Reviewer #1: (No Response)

Reviewer #1: PONE-D-20-14494_R2

Differentiating the learning styles of college students with different disciplines in a college English blended learning setting

Overall, this article was well written and organized. Additionally, great revisions have been made based on the suggestions by previous reviewers, which was made this article more convincing. However, some details should be offered, and minor revisions should be made before publication. They are listed as follows:

1. Though this article has been proofread, some language concerns existed. The authors should carefully revise the language before re-submit the manuscript. (1) Inaccurate wording such as ‘impacted the students’ learning style differences (Abstract)’, ‘..a clearer picture.. (Line 66, what did the author compared with?)’, and ‘an omnipresent’. (2) Wordy sentences, such as ‘.. has always been a long-lasting and hot-debated topic….. (Line 48)’ and ‘the present study is designed to achieve two major objectives (Line 60)’. These two sentences might be re-wrote as ‘… has been constantly discussed’ and ‘this study aims to achieve …’. (3) Hard-to-read long sentences, such as ‘First, …. 46 disciplines were enrolled (Line 60-63)’. The authors are suggested to re-organize the long sentences into a few logically connected short sentences, which are easy for the readers to follow. (5) The authors are suggested to use hedge words instead of the words with a strong tongue (e.g., potent, omnipresent, a constellation of)

2. Literature review. (1) Why did the authors write ‘The examination of a constellation … and extended (Line 71-73)’ as the beginning of this paragraph? What is the connection between this sentence and the following contents? (2) Could the authors add some details about ‘analytical lenses (Line 88)’? (3) It might not be appropriate to use the conjunction word ‘Despite (Line 88)’, because I could not figure out the adversative relation. I guess that the author may intend to state ‘FSLSM was adopted most’. (4) I suggest that the author add a subheading (maybe: Learning style and FSLSM) before the second paragraph in the Literature review section to orient the readers. (5) The transition from the second paragraph to the third paragraph was not smooth; the authors are recommended to add one or two transitional sentences. (6) Any other reasons for the authors’ application of FSLSM? (7) The reviews on learning styles in an online/blended learning environment were not enough, the authors are suggested to add additional studies and summarize the main findings of them. Based on this, the authors could propose research gaps and questions.

3. Materials and methods. This part is organized with sufficient justification, but the authors should add some details. (1) Would it possible to draw a simple flow chart to describe the whole experiment process? (2) For the first time to use ‘ILS’, please write the full name.

4. Results. (1) It is a bit hard to follow the research results described by the authors, would it possible to generally summarize the main results in a table? (2) Why did the authors choose 20 features? (3) The authors are recommended to present the results as per the proposed questions. For example, subheading ‘Results of question one’.

5. Discussion. Try to write with a general-specific structure. Writing a summative sentence at the beginning of each paragraph to present the core idea, then explain it.

6. The authors had better avoid using one-sentence paragraphs in an academic article.

7. The language may be more concise and information-intensive. For example, phrases and sentences like “In the field of learning style research, one great difficulty is the selection of 282 proper instruments to measure the subjects’ learning styles [59].” and “Among the various inventories of learning styles” may be deleted.

8. It is observed that some sections begin with “First.” I suggest the authors add a topic sentence before “First,” briefly illustrating what will be presented. The titles could not replace topic sentences.

9. Based on the in-depth discussions and fruitful results, the authors may propose some implications for future researchers and educators.

Reviewer #2: All my previoius concerns and comments have been addressed satisfactorilly. I have no further comments.

Author response to Decision Letter 2

27 Mar 2021

Dear Esteemed Reviewer 1,

We would like to express our sincere gratitude for reviewing our manuscript entitled “Differentiating the learning styles of college students in different disciplines in a college English blended learning setting” and providing us an opportunity to make corrections in it. We have revised our manuscript according to the valuable comments of the reviewer. The following is an outline of our point-to-point responses and corresponding revisions made to our manuscript.

Once again, we are thankful to you for promoting the quality of our manuscript!

Best regards,

Decision Letter 3

29 Apr 2021

Differentiating the leanrning styles of college students in different disciplines in a college English blended learning setting

PONE-D-20-14494R3

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/ , click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at gro.solp@gnillibrohtua .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact gro.solp@sserpeno .

Additional Editor Comments (optional):

Reviewer #1: The authors have made great efforts, and the revised manuscript is ready for publication. One last comment: authors may add the justification for aiming learning style, which is a controversarial topic in education research community.

Acceptance letter

Dear Dr. Hu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact gro.solp@sserpeno .

If we can help with anything else, please email us at gro.solp@enosolp .

Thank you for submitting your work to PLOS ONE and supporting open access.

PLOS ONE Editorial Office Staff

on behalf of

Professor Haoran Xie

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11 Transforming Student Learning with Effective Study Techniques

https://unsplash.com/photos/person-writing-on-brown-wooden-table-near-white-ceramic-mug-s9CC2SKySJM

Effective study techniques can significantly enhance student learning and academic performance. In today’s fast-paced educational environment, students face numerous challenges, from managing multiple assignments and homework to balancing extracurricular activities. Developing strong study habits is essential for success in both school and college.

Understanding how to study efficiently can make a significant difference in a student’s academic journey. By implementing the right techniques, students can improve their comprehension, retention, and overall performance. This article explores various study methods and provides valuable tips for students looking to transform their learning experiences with paperwriter .

The Importance of Effective Study Techniques

Effective study techniques are crucial for students aiming to achieve their academic goals. These techniques help in better time management, reducing stress, and improving understanding of complex subjects. With the right approach, students can make their study sessions more productive and less overwhelming.

One of the biggest challenges students face is the sheer volume of information they need to learn and retain. Effective study techniques can help break down this information into manageable chunks, making it easier to digest and remember. Moreover, students who develop good study habits early on are more likely to succeed in their future academic and professional endeavors.

Creating a Conducive Study Environment

A conducive study environment is essential for effective learning. Students should choose a quiet, comfortable place with minimal distractions to focus on their studies. A well-organized study space can significantly enhance concentration and productivity.

Tips for Creating a Conducive Study Environment:

• Choose a quiet location: Find a place free from noise and interruptions.
• Ensure good lighting: Proper lighting reduces eye strain and improves focus.
• Organize your materials: Keep all necessary supplies within reach to avoid unnecessary distractions.
• Comfortable seating: Choose a chair and desk that provide good support to maintain good posture.

Time Management and Scheduling

Time management is a critical skill for students. Balancing school, college, assignments, and homework can be challenging. Effective scheduling ensures that students allocate sufficient time for each subject and activity.

Strategies for Better Time Management:

• Create a study schedule: Plan your study sessions and stick to the schedule.
• Set priorities: Focus on the most important tasks first.
• Break tasks into smaller steps: Divide large assignments into manageable parts.
• Use a planner: Keep track of deadlines, assignments, and exams.

Active Learning Techniques

Active learning involves engaging with the material actively rather than passively reading or listening. This approach enhances understanding and retention.

Effective Active Learning Techniques:

• Summarization: Summarize key points in your own words.
• Questioning: Ask questions about the material and seek answers.
• Discussion: Discuss topics with classmates to gain different perspectives.
• Application: Apply what you have learned to real-world scenarios.

The Role of Technology in Studying

Technology can be a powerful tool for enhancing learning. Various apps and online resources can help students manage their study time, organize notes, and access educational materials.

Useful Technological Tools for Students:

• Note-taking apps: Apps like Evernote and OneNote help organize and store notes efficiently.
• Study apps: Apps like Quizlet and Anki offer flashcards and quizzes for effective revision.
• Time management apps: Tools like Trello and Todoist help students plan and track their tasks.( or visit https://do-my-math.com/ )
• Online resources: Websites like Khan Academy and Coursera provide additional learning materials.

Enhancing Memory and Retention

Improving memory and retention is vital for academic success. Students can employ various techniques to boost their ability to remember and recall information.

Techniques to Enhance Memory and Retention:

• Mnemonics: Use mnemonic devices to remember complex information.
• Visualization: Create mental images to associate with the material.
• Repetition: Review material regularly to reinforce learning.
• Teaching others: Explaining concepts to others helps solidify understanding.

Staying Motivated and Managing Stress

Maintaining motivation and managing stress are essential components of effective studying. Students need to find ways to stay motivated and cope with academic pressures.

Tips for Staying Motivated and Managing Stress:

• Set realistic goals: Set achievable short-term and long-term goals.
• Take breaks: Regular breaks prevent burnout and improve focus.
• Stay positive: Maintain a positive attitude towards learning.
• Seek support: Reach out to teachers, peers, or counselors for help when needed.

Incorporating effective study techniques can transform the learning experience for students. By creating a conducive study environment, managing time efficiently, engaging in active learning, utilizing technology, enhancing memory, and staying motivated, students can achieve academic success. Remember, the key to effective studying lies in consistency and dedication. With the right approach, every student can improve their learning outcomes and reach their full potential.

Effective study habits not only help students excel in their current studies but also prepare them for future challenges. By implementing these strategies, students can turn studying into a more enjoyable and rewarding experience, ultimately leading to better academic performance and personal growth. Investing time in developing good study habits today will pay off in the long run, making the journey through school and college a successful one.

Education Copyright © by john44. All Rights Reserved.

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10 Ways to Detect AI Writing Without Technology

As more of my students have submitted AI-generated work, I’ve gotten better at recognizing it.

AI-generated papers have become regular but unwelcome guests in the undergraduate college courses I teach. I first noticed an AI paper submitted last summer, and in the months since I’ve come to expect to see several per assignment, at least in 100-level classes.

I’m far from the only teacher dealing with this. Turnitin recently announced that in the year since it debuted its AI detection tool, about 3 percent of papers it reviewed were at least 80 percent AI-generated.

Just as AI has improved and grown more sophisticated over the past 9 months, so have teachers. AI often has a distinct writing style with several tells that have become more and more apparent to me the more frequently I encounter any.

Before we get to these strategies, however, it’s important to remember that suspected AI use isn’t immediate grounds for disciplinary action. These cases should be used as conversation starters with students and even – forgive the cliché – as a teachable moment to explain the problems with using AI-generated work. 

To that end, I’ve written previously about how I handled these suspected AI cases , the troubling limitations and discriminatory tendencies of existing AI detectors , and about what happens when educators incorrectly accuse students of using AI . 

With those caveats firmly in place, here are the signs I look for to detect AI use from my students. 

1. How to Detect AI Writing: The Submission is Too Long 

When an assignment asks students for one paragraph and a student turns in more than a page, my spidey sense goes off. 

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Almost every class does have one overachieving student who will do this without AI, but that student usually sends 14 emails the first week and submits every assignment early, and most importantly, while too long, their assignment is often truly well written. A student who suddenly overproduces raises a red flag.

2. The Answer Misses The Mark While Also Being Too Long

Being long in and of itself isn’t enough to identify AI use, but it's often overlong assignments that have additional strange features that can make it suspicious. 

For instance, the assignment might be four times the required length yet doesn’t include the required citations or cover page. Or it goes on and on about something related to the topic but doesn’t quite get at the specifics of the actual question asked. 

3. AI Writing is Emotionless Even When Describing Emotions 

If ChatGPT was a musician it would be Kenny G or Muzak. As it stands now, AI writing is the equivalent of verbal smooth jazz or grey noise. ChatGPT, for instance, has this very peppy positive vibe that somehow doesn’t convey actual emotion. 

One assignment I have asks students to reflect on important memories or favorite hobbies. You immediately sense the hollowness of ChatGPT's response to this kind of prompt. For example, I just told ChatGPT I loved skateboarding as a kid and asked it for an essay describing that. Here’s how ChatGPT started: 

As a kid, there was nothing more exhilarating than the feeling of cruising on my skateboard. The rhythmic sound of wheels against pavement, the wind rushing through my hair, and the freedom to explore the world on four wheels – skateboarding was not just a hobby; it was a source of unbridled joy.

You get the point. It’s like an extended elevator jazz sax solo but with words.  

4. Cliché Overuse

Part of the reason AI writing is so emotionless is that its cliché use is, well, on steroids.

Take the skateboarding example in the previous entry. Even in the short sample, we see lines such as “the wind rushing through my hair, and the freedom to explore the world on four wheels.” Students, regardless of their writing abilities, always have more original thoughts and ways of seeing the world than that. If a student actually wrote something like that, we’d encourage them to be more authentic and truly descriptive.

Of course, with more prompt adjustments, ChatGPT and other AI’s tools can do better, but the students using AI for assignments rarely put in this extra time.

5. The Assignment Is Submitted Early

I don’t want to cast aspersions on those true overachievers who get their suitcases packed a week before vacation starts, finish winter holiday shopping in July, and have already started saving for retirement, but an early submission may be the first signal that I’m about to read some robot writing.

For example, several students this semester submitted an assignment the moment it became available. That is unusual, and in all of these cases, their writing also exhibited other stylistic points consistent with AI writing.

Warning: Use this tip with caution as it is also true that many of my best students have submitted assignments early over the years.

6. The Setting Is Out of Time

AI image generators frequently have little tells that signal the AI model that created it doesn’t understand what the world actually looks like — think extra fingers on human hands or buildings that don’t really follow the laws of physics.

When AI is asked to write fiction or describe something from a student’s life, similar mistakes often occur. Recently, a short story assignment in one of my classes resulted in several stories that took place in a nebulous time frame that jumped between modern times and the past with no clear purpose.

If done intentionally this could actually be pretty cool and give the stories a kind of magical realism vibe, but in these instances, it was just wonky and out-of-left-field, and felt kind of alien and strange. Or, you know, like a robot had written it.

7. Excessive Use of Lists and Bullet Points  

Here are some reasons that I suspect students are using AI if their papers have many lists or bullet points: 

1. ChatGPT and other AI generators frequently present information in list form even though human authors generally know that’s not an effective way to write an essay. 

2. Most human writers will not inherently write this way, especially new writers who often struggle with organizing information.

3. While lists can be a good way to organize information, presenting more complex ideas in this manner can be .…

4 … annoying. 

5. Do you see what I mean? 

6. (Yes, I know, it's ironic that I'm complaining about this here given that this story is also a list.)

8. It’s Mistake-Free 

I’ve criticized ChatGPT’s writing here yet in fairness it does produce very clean prose that is, on average, more error-free than what is submitted by many of my students. Even experienced writers miss commas, have long and awkward sentences, and make little mistakes – which is why we have editors. ChatGPT’s writing isn’t too “perfect” but it’s too clean.  

9. The Writing Doesn’t Match The Student’s Other Work  

Writing instructors know this inherently and have long been on the lookout for changes in voice that could be an indicator that a student is plagiarizing work. 

AI writing doesn't really change that. When a student submits new work that is wildly different from previous work, or when their discussion board comments are riddled with errors not found in their formal assignments, it's time to take a closer look. 

10. Something Is Just . . . Off 

The boundaries between these different AI writing tells blur together and sometimes it's a combination of a few things that gets me to suspect a piece of writing. Other times it’s harder to tell what is off about the writing, and I just get the sense that a human didn’t do the work in front of me. 

I’ve learned to trust these gut instincts to a point. When confronted with these more subtle cases, I will often ask a fellow instructor or my department chair to take a quick look (I eliminate identifying student information when necessary). Getting a second opinion helps ensure I’ve not gone down a paranoid “my students are all robots and nothing I read is real” rabbit hole. Once a colleague agrees something is likely up, I’m comfortable going forward with my AI hypothesis based on suspicion alone, in part, because as mentioned previously, I use suspected cases of AI as conversation starters rather than to make accusations. 

Again, it is difficult to prove students are using AI and accusing them of doing so is problematic. Even ChatGPT knows that. When I asked it why it is bad to accuse students of using AI to write papers, the chatbot answered: “Accusing students of using AI without proper evidence or understanding can be problematic for several reasons.” 

Then it launched into a list. 

• Best Free AI Detection Sites
• My Student Was Submitting AI Papers. Here's What I Did
• She Wrote A Book About AI in Education. Here’s How AI Helped

Erik Ofgang is a Tech & Learning contributor. A journalist,  author  and educator, his work has appeared in The New York Times, the Washington Post, the Smithsonian, The Atlantic, and Associated Press. He currently teaches at Western Connecticut State University’s MFA program. While a staff writer at Connecticut Magazine he won a Society of Professional Journalism Award for his education reporting. He is interested in how humans learn and how technology can make that more effective. 

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