achievement orientation essay

Achievement orientation: the secret weapon for meaningful impact

April 15, 2019

Achievement orientation is a skill that shows your capacity for permanent improvement.

Setting high goals and working hard to achieve them, calculating – but ultimately taking – the appropriate risks in order for them to be accomplished, is the perfect definition of achievement orientation..

Achievement Orientation generates new interests and gives you a reason for continued learning and the expansion of your horizons.

But before we go on, let’s remember that any Emotional Intelligence skill refers to how you manage yourself, your relationships and the situations you encounter daily.

All the competencies, in fact, speak to us about a different way to be smart. Fortunately, Emotional Intelligence is something that we can all learn and improve.

Achievement Orientation, in particular, is one of the key features that can help us achieve our dreams. It falls under the umbrella of self-management and it is nurtured by a positive outlook.

Furthermore: it is a distinctive trait in successful entrepreneurs and leaders.

In this article, we will unveil all the secrets to mastering it.

What will be covered here?

Is your passion enough?

What is Achievement Orientation?

Motivation and Persistence

Main features

Note to Leaders

A coach can help

We all have dreams and want to pursue our passion in life. We know that following your passion is the secret fuel that keeps you going forward.

But, is it really?

I mean: when you find an obstacle along the way, when you experience a setback, or when you are challenged beyond your limits, is it your passion that keeps you going or there is something else?

The truth is that dreams and passion are not enough to achieve your goals: achievement orientation is the critical element for success.

What is Achievement Orientation exactly?

This key competence entails striving towards your goal whilst maintaining a standard of excellence, accepting the challenges you might encounter en route, not in a blind way but in a calculated way, and at the same time, improving performance to be ready to welcome the opportunities whenever they present themselves.

The importance of Motivation and Persistence

For this Emotional Intelligence competency to flourish, you need to welcome motivation and persistence in equal parts, especially in the face of setbacks.

Motivation is a powerful tool you can fuel on a daily basis by remembering your values and goals. Motivation, which belongs to the self-management area of Emotional Intelligence, is the hidden force that will drive you, relentlessly, to your final destination. If accompanied by enthusiasm, it is invincible.

External factors like prestige, money, power, are the typical external factors that ignite motivation, but the internal force of persistence is the one that makes the difference, converting a dream into an attainable goal.

For that, those who feel the need to get better and better by improving performance attaining more and moving up the ladder, are those who feed their motivation with their unquenched desire for, simply, achieving.

In short, an achievement-oriented person will look forward to

👉 learning the new and improving the old 👉 fostering excellence 👉 encouraging feedback 👉 accepting  challenges 👉 daring to explore 👉 knowing how to calculate risks 👉 going out of their comfort zone 👉 being open to innovation

One trait that every person with achievement drive distinctly presents is their unquenchable thirst for knowledge. Like the renaissance man, knowledge is seen as a mark of distinction, and being open to innovation; a passport to the future.

Leaders who have developed this skill are able to create an achievement culture within their organizations. They set the scene for performance orientation by encouraging each member of the team to feed their own personal achievement orientation and thereby enable the whole team to make good use of it. Great leaders coordinate actions and act with empathy, fostering relationship building and opening channels for better communication.

What steps should be taken to hone this skill? 

📍 Give yourself time to de-stress: a relaxed mind is a successful mind 📍 Develop the ability to gain (and regain) focus with ease 📍 Identify your next objective in the “big picture” of your plan 📍 Design a step-by-step strategy 📍 Calculate the risks 📍 Divide the next objective into small, attainable goals 📍 Identify the external resources you might need (people, materials, hard skills) 📍 Identify the internal resources you might need (motivation and balance, soft skills) 📍 Once each little goal is achieved, reward yourself 📍 Constantly monitor progress towards your ultimate goal

How can a coach help to develop, cultivate and improve this skill?

Mainly by working to identify your motivations and remove your internal blocks, help with self-esteem and challenge assumptions.

The right coaching can also support you throughout your strategy planning journey and help you create new spaces for growth and exploration.

Coaching can also help you to gain focus and create new habits; small actions performed in a consistent way can act as precursors.

But remember: you need to balance all the skills and work in tandem with them to really perform at your maximum potential. Isolated skills, even if at 100%, will leave you with a sense of imbalance and won’t add any real value to your work.

There is always room for improvement, so it doesn’t matter if you’re a “natural” and motivation is your daily fuel, or if you need a little push now and then.

Achievement drive can benefit us all: freelancers and entrepreneurs, leaders or employees.

By cultivating this skill we will be able to perform any task, to the best of our ability.

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Balance Your Need to Achieve

Having a strong achievement orientation helps balance a leader's desire to succeed with the goals of the organization, Daniel Goleman writes.

Contributor, Korn Ferry Institute

Emotional intelligence remains a key ingredient in the development of corporate leaders. In this series, best-selling author and Korn Ferry columnist Daniel Goleman reveals the 12 key skills behind EI. This is an edited excerpt from his book, Achievement Orientation: A Primer. 

When we’re strong in the Achievement Orientation competency, we strive to meet or exceed a standard of excellence and appreciate metrics for—and feedback on—our performance. We look for ways to learn how to do things better. We set challenging goals and take calculated risks. And we can balance our personal drive to achieve with the needs and goals of the organization.

We all know people who lack this competence—someone who is intellectually gifted but who flounders, unable to put that gift in the service of a focused goal. They could achieve great things if they had the motivation and drive, but they seem to lack this critical element in success.

On the other hand, there are those who fail because they have too much achievement drive and too little of other Emotional Intelligence competencies. I once heard about a highly successful lawyer whose strong drive to achieve meant that he rose to the top of his law school class, got hired by a high-profile law firm, and worked his way up through the ranks until he became a partner in his law firm.

Then something went wrong: As a leader, that lawyer’s same drive for achievement became a drawback. He didn't listen when others suggested their own good ideas about how to do things better. He didn't see the need for the team to work together. He didn’t focus his attention on working to meet the firm’s goals. Seen as aloof, he alienated his entire staff.

The lawyer had the Achievement Orientation competency in spades, but no sense of how to balance his personal drive with the needs of his organization. He lacked several other Emotional Intelligence competencies that balance the sheer drive to achieve, like Empathy, Inspiration, and Teamwork. As careers progress, Achievement Orientation helps people attain their goals, which matters greatly for the success of an individual contributor. But once a person becomes a leader, the Achievement Orientation competency needs to work in tandem with these other competencies.

Research shows that Achievement Orientation for personal goals matters crucially in early career jobs, while it morphs into a concern for the team or organization goals at higher levels.

If a leader fails to shift from personal to group goals, as with that lawyer, he or she can run roughshod over direct reports. The Harvard Business Review published an article about this called “Leadership Run Amok.” Decades of research at Harvard, Cornell, and other universities shows that the drive to achieve runs high in entrepreneurs who found highly successful businesses or who start innovative units within an organization. These entrepreneurs take smart risks. They're sure the risk is minimal, though to others it may seem like a very high risk and that it is unlikely they'll reach that goal.

The drive to achieve also predicts effectiveness in managers. Outstanding executives set goals and keep track of how they're doing, and they know the steps to attain them. Achievement predicts success in jobs like sales, where there's a clear numerical goal and continuous feedback so you can measure how you're doing and change accordingly.

When Achievement Orientation combines with two other competencies, Positive Outlook and Emotional Self-Control , the result resembles what’s called "grit," the tenacity that lets someone attain long-term goals despite obstacles and setbacks.

In the right balance, the Achievement Orientation competency is a critical skill for leaders at all levels of organizations. 

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Achievement orientation refers to how an individual interprets and reacts to tasks, resulting in different patterns of cognition, affect and behavior. Developed within a social-cognitive framework, achievement goal theory proposes that students’ motivation and achievement-related behaviors can be understood by considering the reasons or purposes they adopt while engaged in academic work. The focus is on how students think about themselves, their tasks, and their performance. In general, an individual can be said to be “mastery” or “performance” oriented, based on whether one's goal is to develop one's ability or to demonstrate one's ability, respectively. Achievement orientations have been shown to be associated with individuals’ academic achievement, adjustment, and well-being.

1. Brief History

Research on achievement motivation can be traced back to the 1940s following the seminal work of David McClelland and colleagues who established the link between achievement and motivations (see need for achievement). Students’ achievement orientations were shown to be predictive of academic performance, specifically, students with high achievement orientation tended to value competence, expect success and seek challenges, while students with low achievement motivation tended to expect failure and avoid challenges. [ 1 ]

In an effort to better understand the mechanisms underlying achievement, personality and social psychology researchers expanded McClelland's work by examining how cognitive representations shape social experiences. Personality researchers have explored aspects of achievement motivation as an aspect of identity, [ 2 ] whereas social psychologists have focused on the thought patterns that arise across various contexts. [ 3 ]

2. Two-factor Model

Significant research and a consistent pattern of results have demonstrated that an individual's achievement orientation in a particular domain can be characterized by one of two distinct achievement profiles: mastery orientation or performance orientation.

2.1. Mastery Orientation

A mastery orientation is characterized by the belief that success is the result of effort and use of the appropriate strategies. Mastery oriented individuals strive to develop their understanding and competence at a task by exerting a high level of effort. Across numerous studies, mastery orientation has been shown to promote adaptive patterns of learning, which ultimately lead to high academic achievement and adjustment. [ 4 ] For example, students with a mastery orientation are more intrinsically motivated to learn, use deeper cognitive strategies, and persist through challenge and failure. [ 5 ] [ 6 ] [ 7 ]

2.2.Performance Orientation

A performance orientation is characterized by the belief that success is the result of superior ability and of surpassing one's peers. [ 8 ] Performance oriented individuals desire to outperform others and demonstrate (validate) their ability. [ 8 ] Performance orientation is predictive of negative affect, avoidance of challenge and poor achievement outcomes. [ 5 ] [ 6 ] [ 7 ]

3. Four-factor Model

More recent conceptualizations of achievement orientation have added an additional element. The traditional mastery and performance orientations are broken down to include approach and avoidance components, [ 9 ] [ 10 ] resulting in four distinct achievement profiles: mastery-approach, mastery-avoidance, performance-approach, and performance-avoidance.

A mastery-approach orientation describes individuals who are focused on learning as much as possible, overcoming challenges through hard work, or increasing their competence at a task. [ 11 ]

A mastery-avoidance orientation describes individuals who want to avoid doing worse than they have done before or failing to learn as much as possible. [ 11 ]

A performance-approach orientation describes individuals who want to demonstrate and prove to others their high ability. [ 11 ]

A performance-avoidance orientation describes individuals who strive to avoid looking incompetent, or less able than their peers by cultivating an appearance of effortless achievement. [ 11 ] [ 12 ] [ 13 ]

4. Implicit Theories of Intelligence

Epistemological beliefs of intelligence refer to an individual's belief about the nature of intellectual ability, specifically, is intelligence a fixed characteristic, or is it a malleable quality? Individuals conceptions of intelligence have been shown to influence cognitive and motivational factors associated with achievement orientation and ultimately academic performance. [ 14 ]

4.1. Entity

If an individual has an entity (also referred to as “fixed”) view of intelligence, they believe that intelligence is an unchanging characteristic and are more likely to think effort plays little to no role in outcome. In other words, you are either smart, or you are not. This is particularly maladaptive in academia. Students believe that effort is unnecessary because if you are smart, everything should come easy, and if you are not smart, hard work cannot compensate for this deficiency. Students with an entity view of intelligence are more likely to develop a fear of failure, [ 14 ] [ 15 ] resulting in the avoidance of “intellectual tasks,” [ 6 ] and giving up in the face of difficulty. The rationale is that if you are smart, effort is unnecessary, and if you are not, there is nothing you can do to change this.

4.2. Incremental

In opposition to entity theory, individuals with an incremental (also referred to as “flexible,” and “malleable”) view of intelligence believe that intelligence is adjustable. The belief is that intelligence is the result of hard work and the use of the appropriate strategies. This is particularly adaptive because rather than giving up in the face of failure or challenge, those who endorse an incremental view of intelligence interpret these setbacks as inevitable for learning to take place. Because they are not worried that exertion of effort is a reflection of lack of intelligence, they are not afraid to work hard, resulting in an outperformance of their entity theory peers. Even after several years, the effect is consistent, such that students with an incremental view of intelligence academically outperform students who had an entity view of intelligence. [ 16 ]

5. Mindsets

Mindset refers to an individual's belief about oneself and one's most basic qualities, such as talent, intelligence, and personality. Although the majority of research on mindsets has focused primarily on how they affect educational achievement, [ 17 ] [ 18 ] mindsets have also been shown to be influential in athletics, health and well-being, business and relationships.

Fixed mindsets are characterized by the belief that one's basic qualities are fixed – as if genetically predetermined. Individuals with fixed mindsets believe that practice has no relationship to performance success, which has been shown to be maladaptive across domains. [ 17 ] [ 18 ]

5.2. Growth

Growth mindsets are characterized by the belief that talents and abilities are things that are developed through effort, practice and instruction. Individuals with growth mindsets feel that they control their success, rather than external forces, so they are better able to problem solve and persist through setbacks. Research has shown that growth mindsets foster a more positive attitude toward practice and learning, a desire for feedback, a greater ability to deal with setbacks, and significantly better performance over time. [ 17 ] [ 18 ]

Why foster a growth mindset in students?

Dweck (2010) explains, when students view intelligence as something that develops over time they view challenging work as an opportunity to learn and grow. These students value effort and realize that “even geniuses have to work hard to develop their abilities and make their contributions” (p. 16). Students with this type of attitude are able to respond to obstacles, try new strategies and continue to learn and grow in many situations, which leads to higher achievement. [ 18 ]

How to foster to a growth mindset in students

In order to foster a growth mindset, teachers need to encourage students to welcome challenges and view it as an opportunity to learn and grow . [ 17 ] The following are a list of ways a teacher can create a culture of risk taking:

• Provide encouragement : praise students for their perseverance, strategies and the choices they made, rather than being told they are ‘smart’ as it tells students that what they did has led them to success and can be used again to be successful in the future [ 17 ] [ 18 ]
• Emphasize that the deepest and best learning takes time : "…portray challenges as fun and exciting and easy tasks as boring and less useful for the brain" [ 17 ] (p. 17). Students who work hard and value effort in the learning process will be able to develop their abilities on a deeper level. [ 17 ]
• Illustrate growth : provide students with opportunities to write about, and share with one another, something that they used to struggle with and are now good at doing. [ 17 ] This allows students to notice their own successes, which motivates their learning. [ 18 ]

Long term success of growth mindset

Designing and presenting learning tasks that foster a growth mindset in students, leads to long-term success. [ 17 ] Growth mindsets promote a love of learning and highlight progress and effort. Teachers that illustrate meaningful work help students gain the tools they need to find confidence in their learning and be successful in future challenges. [ 18 ]

6. Influencing Factors

Achievement orientations have been shown to be influenced by a combination of cognitive-motivational and contextual factors.

6.1. Praise

One factor that has been shown to be influential in the development of achievement orientations is the type of praise given to individuals. [ 19 ] Type of praise not only affects behaviors, beliefs, emotions and outcomes immediately after it is imparted, but has also been shown to have long term consequences. Specifically, it affects how individuals deal with future difficulties and their willingness to apply effort to challenges that may come their way. [ 20 ] [ 21 ] [ 22 ] Verbal praise is often administered as a way to reinforce the performance or behavior of individuals and although there may be positive intentions, some types of praise can have debilitating implications for the recipient. The specific distinction lies in what the praise is directed towards.

Process praise is focused on the actions taken by the individual, especially their effort and problem solving strategies, such as “Great job! You’re working really hard.” Process praise reinforces the association between success and effort (or behavior) rather than a fixed ability, which cultivates the more adaptive mastery orientation and incremental view of intelligence. [ 14 ]

Person praise is focused on the individual themselves, similar to an affirmation of self-worth, such as, “Wow, you’re so smart.” Because it applauds the individual by applying a label or an unchangeable characteristic, person praise promotes a performance orientation and a fixed view of intelligence. Students are being rewarded, through praise, for their performance based on their ability. Children who are given person praise tend to have worse task performance, more low-ability attributions, report less task enjoyment and exhibit less task persistence, than children who are given process praise. [ 14 ] Additionally, person praise is more likely to promote helpless responses to subsequent failures than process praise. [ 15 ]

Although praise for intelligence is usually well-intentioned, and can be motivating when students are doing well, it backfires when students eventually face work that is difficult for them. When this happens, the failure is a threat to the person's sense of his or her own intelligence—a situation to avoid. Thus, praise for intelligence is a short-term strategy that makes successful students feel good at the moment, but one that is detrimental to students in the longer run.

Age is a significant factor in predicting an individual's achievement orientation, with younger individuals more likely to adopt a mastery orientation. [ 23 ] [ 24 ] Beginning at the transition to middle school, students tend to exhibit a performance orientation, along with an overall decline in academic motivation across adolescence. [ 25 ] This follows the developmental propensity to view intelligence as a fixed characteristic in adulthood. [ 26 ]

6.3. Gender

Supporting the gender disparities in STEM fields, previous research has suggested that females develop a motivational orientation that is maladaptive to high academic achievement, particularly in math and science. [ 27 ] However, overall, the research examining gender differences in achievement orientation has been conflicting. Research by Carol Dweck has shown gender differences with females being more extrinsic or performance oriented. On the other hand, other studies have found that females are more likely to be mastery oriented, while males are more likely to hold performance orientations. [ 28 ]

Despite the lack of uniformity among research findings, there is a general consensus that gender influences the development of different rationales and motivations for behavior, as a result of unique socialization expectations and experiences. [ 28 ] These differences then affect the way students approach learning situations, leading to gender-related differences in achievement orientations. [ 29 ] Although several studies have hypothesized this effect, there is a lack of conclusive evidence, which warrants further exploration into gender differences among individuals’ achievement orientations.

6.4. Parents and Peers

Social influences, particularly parents and peers, affect the achievement orientation of students. During early and middle childhood, the achievement beliefs, attitudes and expectations of a child's parents carries significant weight in determining his or her achievement orientation. [ 30 ] [ 31 ] As children transition to middle school, fitting in with one's peers becomes high priority. Peers influence achievement orientation because children adopt academic goals and beliefs consistent with the dominant social norms. Adolescents with friends having high academic aspirations tend to have fewer problems academically. [ 31 ]

7. Implications

Achievement orientations play a critical role in explaining academic performance. An individual's achievement orientation has a significant impact on his or her cultivation of new skills, and thus has important implications for educators. Classroom environments that foster comparison between students lead those students to develop performance-oriented attitudes toward education. Specifically, learning in a competitive environment leads students to become more performance oriented and more likely to sacrifice learning opportunities to be positively evaluated. Conversely, a non-competitive, collaborative environment allows students to value learning rather than immediate performance success. [ 32 ]

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Achievement Goal Theory: Definition and Examples

Achievment Goal Theory is a theory that argues a person’s degree of motivation to achieve a goal is influenced by their goal orientation .

Goal orientation refers to the rationale underpinning our goals. We can either approach a goal in two broad ways:

• Mastery goals: Setting a goal with the intention of mastering a skill or task (known as mastery goals), or
• Performance goals : Setting a goal with the intention of achieving an extrinsic outcome such as a trophy (known as performance goals).

Generally, scholars argue that mastery goal setting leads to greater intrinsic motivation, resilience, and determination to complete the goal. Performance goals , on the other hand, tend to lead to waning motivation over time due to their reliance on extrinsic rewards.

The theory’s value is in helping us to reflect upon how we are formulating and conceptualizing goals, with the understanding that the ways goals are framed will affect how motivated we are to strive for them.

Achievement Goal Theory Overview

Two key concepts in achievement goal theory are goal orientation and goal structure .

1. Goal Orientation

Early theorists of Achievement Goal Theory posited that goals tend to be based on achievement or mastery, as outlined above.

However, based on the research of A. J. Elliot (Elliot & McGregor, 2001), the 2×2 model of goal setting was developed. This model splits mastery and performance goals into two parts based on approach (seeking reward or growth) and avoidance (avoiding punishment or failure):

• Mastery-approach: Wanting to complete a task for the purpose of self-improvement and learning as much as possible.
• Mastery-avoidance: Wanting to avoid a task because they feel they won’t learn as much as they need to in order to complete the task.
• Performance-approach: The desire to complete a task in order to outperform a peer group, achieve the appearance of superiority, and receive an extrinsic reward .
• Performance-avoidance: The desire to avoid the task to evade embarrassment, shame, self-doubt, or public failure (Wolters, 2004).

Dweck (1999), famous for her work on mindsets, argues that mastery-avoidance and performance-avoidance correspond with a fixed mindset . This is a mindset where people avoid a task because they don’t believe they have the capacity for improvement or success.

By contrast, mastery-approach and performance-approach correlate with a growth mindset , where a person’s focus is primarily on what they’re capable of, if they put in the effort.

2. Goal Structure

Goal structure (also known as goal climate) refers to the institutional environment in which goals are assigned.

An institution (such as a workplace or school) sets in place a culture that can influence a person’s goal orientation.

• Mastery goal structure: If the institution sends signals that it values mastery (e.g. doing a task for the sake of being good at the task), it is considered to have a mastery goal structure. This sort of a goal climate will encourage people to set mastery goals.
• Performance goal structure: If the institution sends signals that it most highly values rewards and outcomes (e.g. a school focused on standardized test scores or workplace renumeration based on commissions), it is considered to have a performance goal structure. This sort of a goal climate will encourage people to set performance goals.

Furthermore, an institution that punishes failure may encourage an avoidance mindset. By contrast, an institution that embraces failure as a natural part of growth may encourage an approach mindset (with recognition that failure isn’t a big deal and doesn’t represent loss of face).

The Four Goal Orientations Explained

1. mastery-approach.

A mastery-approach goal orientation has self-improvement and learning as its core focus. The motivation to complete tasks is intrinsic (the pleasure of the task) rather than extrinsic (reward or recognition).

This mindset encourages people to concentrate on their own progress rather than comparing themselves to others or seeking praise (Elliot & Murayama, 2008). As a result, their focus is on enjoying the learning process rather than achieving extrinsic outcomes.

The mastery-approach tends to stem from a genuine interest in the subject matter, curiosity, or the satisfaction derived from learning and unlocking new skills (Elliot & McGregor, 2001).

A core benefit of the mastery approach is that learners are intrinsically motivated, and intrinsic motivation tends to be more sustainable and can lead to long-term commitment to personal growth and development, which are considered the intrinsic rewards (Wolters, 2004). Another key advantage is that it encourages a growth mindset, which refers to the belief that you can improve through your own hard work and effort.

This mindset can lead to increased resilience, adaptability, and a positive attitude toward learning , ultimately fostering greater personal and professional success.

One potential drawback of the mastery-approach orientation is that it tends to require effort, time, and commitment (Elliot & McGregor, 2001). The focus is on deep and authentic understanding and expertise rather than simply passing a test. This makes it very challenging to apply, particularly in schools where we are incentivized by grades and speed.

Example of Mastery-Approach: An example of a mastery-approach orientation would be a retiree who wants to learn a new language out of desire to be competent in multiple languages. In this hypothetical, the language learner would find the task personally fulfilling. They would get joy from learning new words and language structures, not from praise or from passing language tests.

2. Mastery-Avoidance

A mastery-avoidance goal orientation revolves around preventing failure in acquiring new skills rather than seeking self-improvement .

The motivation to engage in tasks (or to avoid engaging in tasks) arises from a fear of inadequacy or a desire to prevent negative self-evaluation (Wolters, 2004). We may avoid a task because we’re afraid of revealing to ourselves our own fear of inadequacy.

This mindset can lead individuals to become overly cautious or reluctant to take on new learning opportunities, potentially hindering their growth and development.

Despite its drawbacks, mastery-avoidance can have some positive aspects. For instance, it may motivate individuals to be more thorough, accurate, and cautious in their work, ultimately reducing the likelihood of making careless errors (Elliot & McGregor, 2001).

The fear of not learning enough can serve as a driving force, pushing them to be more diligent and attentive (Grant & Dweck, 2003).

However, the primary disadvantage of the mastery-avoidance orientation is that it can lead to procrastination, avoidance, or risk-averse behavior.

Note: Many scholars (see, for example, Lufteneggera et al., 2017) have argued that mastery-avoidance is undertheorized and therefore choose not to avoid it in their research. When we use the other three goal orientations but not mastery-avoidance, we call it the trichotomous achievement goal model.

Example of Mastery-Avoidance: An example of mastery-avoidance would be a student who hesitates to sign up for an advanced course out of fear that they will not be good enough to do it. The student has decided that the task is beyond their stretch ability level or is afraid that it will be. The desire to avoid disappointment has outweighed the desire to develop new skills and knowledge.

3. Performance-Approach

The performance-approach orientation’s focus is on achieving a set extrinsic goal such as a grade on an exam or a top ranking among peers.

The focus is often achieving a visible, public reward, often in the form of social status . People who are highly driven by performance-approach may, for example, only participate in a test or competition if the reward at the end is of sufficient social value.

Within this goal orientation, motivation is extrinsic, meaning the thing driving people to complete a task is not personal satisfaction but an externalized reward such as a trophy or money (Elliot & Murayama, 2008).

Furthermore, unlike the mastery goal orientations, the focus is not on the process of learning, but on the outcome of learning (Wolters, 2004). This often leads people to fail to focus on the hard work required to do the task and can often end with people taking shortcuts.

While the performance-approach orientation can motivate people to get started on a task, in many people, it is believed to rapidly wane. If the effort required begins to outweigh the reward at the end, people may quit.

Interestingly, however, Pintrich (2000) found that combining performance-approach (rewards, social satisfaction, recognition) and mastery-approach (personal fulfillment and enjoyment) can lead to better outcomes than mastery-approach alone.

Example of Performance-Approach: In education policy, the performance-approach orientation is currently dominant. Schools are ranked based on highly-flawed standardized test scores, leading teachers to focus on lessons that lead to higher test results rather than lessons that may instil love of learning, inquiry, or pursuing a student’s personal interests in a topic.

4. Performance-Avoidance

A person with a performance-avoidance goal orientation will be motivated by the desire to evade embarrassment or public failure.

Individuals with this orientation tend to avoid competitions, public performances, or other scenarios where they are at risk of public failure or publicly looking incompetent.

The motivation for completing tasks or avoiding tasks is driven primarily by the fear of the criticism or judgment of others, especially those in their peer group.

Sadly, performance-avoidance is often the reason people don’t pursue goals that are of intrinsic value to them (Wolters, 2004). For example, you might choose not to start a business that you would love to start because you worry people will judge you if you fail.

In performance-avoidance, the focus is on minimizing risk. It’s often based on the deeper concept of fixed mindsets , where people don’t think they have what it takes to succeed, improve, or grow.

This mindset can lead individuals to become excessively cautious, hesitant to take risks, or reluctant to engage in challenging tasks.

One potential benefit of the performance-avoidance orientation is that it may prompt people to be very diligent, mindful, and careful.

However, the primary drawback of the performance-avoidance orientation is that it can cause people to avoid any and all situations where they can achieve personal development. By focusing on avoiding negative evaluation, we may miss out on invaluable learning experiences . Indeed, failure itself is an important learning experience.

Example of Performance-Avoidance: An employee opts not to share their innovative idea during a team meeting, out of fear that it might be criticized or rejected. Instead of focusing on the potential benefits or growth that could come from presenting the idea, the employee’s primary concern is avoiding any negative consequences or judgments from their peers or superiors.

Achievement goal theory helps to explain how people set goals and what motivates them to strive for the goals. With the categories of goal setting put in place, we can compare and contrast how each goal orientation will affect resilience, sense of competence, long-term motivation, and a host of related factors.

Anseel, F., Van Yperen, N. W., Janssen, O., & Duyck, W. (2011). Feedback type as a moderator of the relationship between achievement goals and feedback reactions.  Journal of Occupational and Organizational Psychology ,  84 (4), 703–722.  https://doi.org/10.1348/096317910×516372

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Chazan, D., Pelletier, G., & Daniels, L. M. (2021). Achievement Goal Theory Review: An Application to    School Psychology. Canadian Journal of School Psychology , 37 (1), 40–56. https://doi.org/10.1177/08295735211058319

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Elliot, A. J., & McGregor, H. (2001). A 2×2 achievement goal framework. Journal of Personality and Social Psychology, 80 , 501–519.

Elliot, A. J., & Murayama, K. (2008) . On the measurement of achievement goals: critique, illustration, and application.  Journal of educational psychology ,  100 (3), 613.

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Harwood, C., Spray, C. M., & Keegan, R. (2006). Achievement Goal Theories in Sport: Approaching Changes and Avoiding Plateaus. In  Advances in Sport and Exercise Pychology . Human Kinetics.

Lufteneggera, M., et al. (2017). Measuring a Classroom Mastery Goal Structure using the TARGET dimensions: Development and validation of a classroom goal structure scale. Zeitschrift für Psychologie, 225 (1), 64-75. doi:10.1027/2151- 2604/a000277

C. Measuring a mastery goal structure using the TARGET framework: Development and validation of a classroom goal structure questionnaire.

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Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement.  Journal of educational psychology ,  96 (2), 236. Doi: https://psycnet.apa.org/doi/10.1037/0022-0663.96.2.236

Gregory Paul C. (MA)

Gregory Paul C. is a licensed social studies educator, and has been teaching the social sciences in some capacity for 13 years. He currently works at university in an international liberal arts department teaching cross-cultural studies in the Chuugoku Region of Japan. Additionally, he manages semester study abroad programs for Japanese students, and prepares them for the challenges they may face living in various countries short term.

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Chris Drew (PhD)

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Academic self-concept, achievement, and goal orientations in different learning environments

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• Published: 25 March 2024

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• Olga Steinberg   ORCID: orcid.org/0000-0002-6031-3100 1 , 2 ,
• Stefan Kulakow   ORCID: orcid.org/0000-0001-8105-6855 1 , 3 &
• Diana Raufelder   ORCID: orcid.org/0000-0003-2977-4459 1 , 3  

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Stage-Environment Fit Theory underlines the role of learning environments and their match with students’ needs as crucial for students’ motivation and learning. This study explores the mediation role of goal orientations in the interplay of academic self-concept and achievement in mathematics and verbal domains in student-directed and teacher-directed learning environments. The sample consists of 1153 adolescent students ( M age t1  = 13.97; SD  = 1.37, 49% girls) from Germany. Multi-group cross-lagged panel analyses confirm the Reciprocal Effects Model for the student-directed learning environment only, as reciprocal relation of academic self-concept and grades over time has been found. The extension of the Reciprocal Effects Model with goal orientations as mediators could not be confirmed for any learning environment.

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Theoretical background

Adolescence presents a particularly challenging developmental period for all students. It is notorious for declines in academic self-concept (e.g., Nagy et al., 2010 ; Pesu et al., 2016 ), academic motivation (e.g., Gnambs & Hanfstingl, 2016 ), and reduced performance in educational settings (e.g., Wijsman et al., 2016 ). The Stage-Environment Fit Theory (Eccles & Midgey, 1989 ) attributes the origins of these negative trends to a mismatch between students’ developmental needs and their learning environments. In other words, the changes in classrooms settings and new grading standards, as well as increased academic demands from multiple teachers, do not go together with adolescent students’ neurobiological, cognitive, and social needs (Steinberg & Morris, 2001 ). Empirical research on the interplay of adolescent students’ self-concepts, achievement, and motivation has traditionally been focused on teacher-directed learning (TDL) environments (e.g., Bakadorova & Raufelder, 2020 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ), while student-directed learning environments (SDL) remain less explored so far. While in TDL a teacher is responsible for students’ progress, SDL persuades with an individually tailored program, when a student takes over the responsibility for the learning goals, educational needs, and outcomes, while teachers facilitate the progress. As in general, individualized instruction is most beneficial for each particular learner (Watts-Taffe et al., 2012 ), student-directed learning per se is designed to better meet students’ needs. At the same time, SDL may theoretically be challenging at this age, as adolescents may not yet have fully developed the cognition-, emotion-, and behavior-regulating skills (Smith et al., 2013 ; Steinberg, 2005 ). The existing empirical research however taps at positive associations of SDL and motivation, mastery goals, and positive emotions in secondary school settings (Schweder & Raufelder, 2023 ,  2024 ).

The present study explores the interplay between academic self-concept, achievement, and goal orientations in both TDL and SDL. The overarching goal is to test (a) whether there are reciprocal associations between domain-specific academic self-concepts and grades over time in both learning environments, in line with the Reciprocal Effects Model (Marsh, 1990 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ), and (b) whether mastery and performance goal orientations would mediate these associations, extending the Reciprocal Effects Model.

Academic self-concepts, achievement, and goal orientations

Academic self-concept refers to a student’s perception of their own school-related abilities (Shavelson et al., 1976 ). It is a complex domain-specific construct with multiple dimensions for comparison (e.g., individual or criterial (Spinath et al., 2012 )). In this study, criterion-unsensitive, domain-specific academic self-concepts have been explored that depict students’ perceptions of their own competence in academic settings. Empirical studies show that academic self-concept is a significant predictor of educational outcomes, including goal orientations, academic achievement, and career aspirations (e.g., Barker et al., 2005 ; Denissen et al., 2007 ; Marsh & Craven, 2006 ). While in pre-adolescence, academic self-concept and achievement-related outcomes exhibit weak correlations, during adolescence, these correlations strengthen and become more stable (Marsh, 1989 ). Age-related differences are relevant not only for the association between these variables but also for their magnitude (Marsh, 1989 ; Perinelli et al., 2022 ). Gender-related findings tap at domain-specific effects: girls tend to report higher verbal self-concepts, whereas boys tend to report higher mathematical self-concepts (Heyder et al., 2017 ; Mejía-Rodríguez et al., 2020 ; Skaalvik & Skaalvik, 2004 ).

The Reciprocal Effects Model (Marsh, 1990 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ) suggests that students’ academic self-concept and achievement are interdependent. In other words, the higher a student’s academic self-concept, the higher their achievement is, and vice versa. This model is supported by empirical research across all stages of schooling process in TDL environments (e.g., Guay et al., 2003 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ) in mathematic and verbal domains (Jacobs et al., 2002 ; Pesu et al., 2016 ). While the mathematics domain is characterized by a student’s beliefs about their own abilities in mathematics (Opacic & Kadijevic, 1997 ), verbal self-concept refers to a student’s beliefs about their own written and oral language skills, including reading comprehension (Locher et al., 2021 ). The domain-specific research within the internal/external frame of reference model (Marsh, 1986 ) suggests that positive effects between academic self-concept and achievement could be confirmed within, but not across the verbal and mathematic domains; and therefore, the domains should be studied separately.

The existing domain-specific findings on the associations of academic self-concepts and achievement within the domains suggest differentiated results for verbal and mathematic models: while Retelsdorf and colleagues (Retelsdorf et al., 2014 ) found stronger effects from reading achievement to reading self-concept that vice versa, the longitudinal five-wave model for mathematics (Arens et al., 2017 ) showed robustness of the reciprocal effects in both directions. A meta-analysis of longitudinal studies (Wu et al., 2021 ) suggests students’ age is a statistically significant moderator of the effect of achievement on academic self-concept, while both age, achievement level, and type of achievement measurements are important to consider for the effect of academic self-concept on achievement.

Various approaches have been employed to integrate academic motivation into the Reciprocal Effects Model (see Green et al., 2006 ). It has been directly integrated (see Green et al., 2006 ), tested as a mediator (e.g., Bakadorova & Raufelder, 2020 ), or considered a moderator variable (Valentine & DuBois, 2005 ). Despite the encouragement by Valentine and DuBois ( 2005 ) almost 20 years ago to “include these [motivation] variables more frequently when studying the relationship between self-beliefs and achievement” (p. 72), only a few longitudinal studies follow this recommendation. This may be attributed to theoretical diversity, differing operationalizations of motivational constructs (see Green et al., 2006 ), and the distinction of domain-specific and domain-unspecific studies. Building on a recent domain-unspecific study that demonstrated mastery goal orientation partially mediates the association between academic self-concept and achievement (Bakadorova & Raufelder, 2020 ), the present study aims to replicate this finding in a sample of secondary school students, taking a domain-specific approach and exploring potential differences between TDL and SDL.

In academic settings, achievement goals are often regarded as tendencies for approach or avoidance orientations within achievement motivation (Weiner, 1990 ). As such, goal orientation theory is a social-cognitive theory of achievement motivation that investigates why students engage in their academic work. There exist several frameworks that differentiate between approach and avoidance goal orientations. Thus, Elliot and MacGregor ( 2001 ) differentiate between mastery approach, mastery avoidance, performance approach, and performance avoidance goal orientations. Niemivirta et al. ( 2019 ), in turn, regard mastery goal orientation as either extrinsic or intrinsic, and add work avoidance orientation. While differentiation between different orientations of mastery goals should be treated carefully and needs further exploration (see Bong, 2009 ), decades of research agree on the differentiation between performance approach and performance avoidance orientation. Following the trichotomous framework of achievement goal theory (Elliott & Church, 1997 ), empirically supported by many works (e.g., Hulleman et al., 2010 ; Pekrun et al., 2006 ), this study differentiates among mastery, and performance-approach and performance-avoidance goal orientations. In a school setting, mastery goals aim at increasing competence (Ames, 1992 ) and directly relate to self-regulated learning strategies (Pintrich, 2000 ). Performance goals, in turn, foster comparison and competition to increase individual ability perception (Covington, 2000 ). While performance-approach goal orientations aim to demonstrate competence and outperform others, performance-avoidance goal orientations relate to the wish to perform not worse than others and hide incompetence (e.g., Darnon et al., 2007 ; Pintrich, 2000 ).

A variety of studies shows that academic self-concept and goal orientations are highly interdependent constructs that are rooted in social comparison processes (e.g., Niepel et al., 2014 ; Huguet et al., 2009 ; Middleton & Midgey, 1997 ). However, academic self-concept presents the more stable variable that further defines students’ motivational orientations (Yeung et al., 2012 ). In the present study, the assumption by Barker et al. ( 2005 ) has been followed, that “variables drawn from self-concept and goal theories taken together will provide a fuller explanation of academic achievement than is possible with either self-concept or motivational goal variables alone” (p. 1).

The existing findings on the associations of verbal and mathematics academic self-concepts, goal orientations, and achievement in adolescence are inconsistent: Niepel et al. ( 2014 ) examine the relationship between academic self-concept and goals by cross-lagged panel models with achievement as an outcome variable. The results suggest academic self-concept in mathematics (grade 8) is positively predicted by performance-approach goals (grade 6), and is negatively predicted by performance-avoidance goals (grade 6), but not mastery goals. In addition, no goal orientation reveals a direct association with achievement. A study of Preckel and Brunner ( 2015 ), in turn, shows that mathematic self-concept and performance goals are reciprocally related; however, only mastery goals reciprocally associate with academic achievement. In contrast, the research of Seaton et al. ( 2014 ) reports positive reciprocal relations between performance approach goals and achievement in mathematics. The mastery goal orientation was reinforced by mathematic achievement, but not at every time point. Yet another study by Paulick and colleagues (Paulick et al., 2013 ) finds positive longitudinal reciprocal relationships between mastery approach goals and achievement. In addition, a domain-specific study on causality among academic self-concept, achievement, and goal orientations in mathematics and verbal domains shows that the causal relation may be domain-specific (Barker et al., 2005 ). Specifically, in verbal domain, goals affect achievement through the perception of self, while in mathematics domain, self-concept affects achievement through goal orientations.

Overall, the existing research shows no unity on the causal ordering of academic self-concepts, goal orientations, and achievement, and suggests possible domain-specific differences. There are more studies in the domain of mathematics than in the verbal domain. In addition, all studies so far have been conducted in TDL, while students’ academic self-concepts and goal orientations in TDL and SDL may differ.

Learning environment as a potential moderator

While some research focuses on differential roles of learning environments, as linked to teachers’ instruction (e.g., Trautwein et al., 2006 ), there are only few studies (e.g., Ryser et al., 1995 ) that contrast academic self-concepts of students from different learning settings. This may be due to the fact that TDL approach is widely practiced in schools. TDL is traditionally characterized by teacher-directed systematic instruction of students, aimed at transmission of basic skills, facts, and information (Rosenshine & Stevens, 1984 ).

However, nowadays SDL gains in importance in school practice, as either a supplement or a substitute to TDL. The term SDL stems from self-directed learning, originally used in adult education (Knowles, 1975 ), and refers to self-directed learning in school-related contexts. The central aim of SDL is the development of learner autonomy (Armstrong, 2010 ); and therefore, SDL embraces students’ both self-regulated and self-determined learning within and beyond a certain educational situation. SDL is also a broader construct in terms of a learner’s degree of control and ability to choose a learning activity, even though in some papers the terms “SDL” and “self-regulated learning” are used interchangeably (for an overview, see e.g., Saks & Leijen, 2014 ). Several studies show that SDL is associated with academic achievement (Chou & Chen, 2008 ; Lounsbury et al., 2009 ), and students who use and choose activities that match their interest and abilities (Hussain et al., 2011 ) tend to show higher achievement scores as compared to students in traditional TDL settings, even though SDL rather focuses mastery of competencies rather than grade improvement.

In school practice, SDL is often addressed by use of competency grids (Kulakow,  2020 ). Competency grids help students self-diagnose their learning needs, identify their goals and resources for learning, set own learning strategies, and evaluate the results. Empirical studies with adolescents that contrast SDL and TDL show higher autonomy, mastery goals, and academic self-concepts among SDL students (Kulakow, 2020 ; Schweder et al., 2019 , Schweder & Raufelder, 2021 ), and also higher achievement (Orawiwatnakul & Wichadee, 2017 ). Longitudinal research shows that students from SDL have more positive motivation development in secondary school (Raufelder & Kulakow, 2021 ) due to individual control of own learning progress, continuous self-reflection, individualized feedback from teachers, less social comparison to peers, and, generally, better need satisfaction as stated in the Stage-Environment Fit Theory (e.g., O’Mara et al., 2006 ; Schweder et al., 2019 ; Schweder & Raufelder, 2021 ).

By combining the Stage-Environment Fit Theory and Reciprocal Effects Model, the present study has the goal of testing whether (a) consistent with the Reciprocal Effects Model (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Marsh, 1990 ) there is a reciprocal association between domain-specific academic self-concepts and grades over time in both verbal and mathematics domains in both SDL and TDL, and whether (b) Reciprocal Effects Model may be extended by goal orientations as mediators.

Previous studies revealed higher academic self-concept (Kulakow, 2020 ), mastery goal orientation (Schweder, 2020 ; Schweder et al., 2019 ), and achievement results (Orawiwatnakul & Wichadee, 2017 ) for students in SDL as compared to students in TDL. As such, learning environments have shown to be a distinctive factor not only in mean differences of variables, but also in the associations of variables. Accordingly, it was hypothesized (H1a), that there are substantial differences in the longitudinal associations of mathematic and verbal self-concepts and grades between students from TDL and SDL environments. In detail, we expect students in SDL to score higher on all variables. We also expect mastery goals to mediate the relation between academic self-concepts and grades in SDL as compared to TDL. The Reciprocal Effects are expected to be stronger in mathematics than in verbal model. In extension of the Reciprocal Effects Model, (H1b) goal orientations were tested as possible mediators. Since there is only one study so far (Bakadorova & Raufelder, 2020 ) that regards mastery goals as a mediator in the Reciprocal Effects Model, and, according to other studies, a different arrangement of these variables may be possible (e.g., Valentine & Dubois, 2005 ), H1b is exploratory in nature. As there might be domain-specific differences in the interplay of academic self-concept, goal orientations, and achievement (Barker et al., 2005 ), the hypotheses are tested for the mathematics and verbal domains separately.

Participants and procedure

In total, 1153 students ( M age  = 13.97; SD  = 1.37, 49% girls; grades 7–10) participated in the current study. The students attended 57 classrooms in six secondary schools in the north of Germany (place of data collection was removed in the review process for anonymization). They were first surveyed during the winter term 2015/2016 (t1) and approached again 0.5 years later (t2). Of the six schools, three used SDL, based on competency grids; the other three followed TDL. The schools of the TDL group were randomly sampled. The SDL schools were chosen according to the following criteria: (1) use of competency matrices comprised part of the curriculum and was consequently applied; and (2) practice of SDL for at least 6 years, so that, besides occasional school changes, students would know only this instructional approach in secondary school. To ensure the ethical standards of the 1964 Declaration of Helsinki and of the German Psychological Society, all participants and their parents provided written informed consent.

After all necessary permissions were signed, two trained research assistants approached the students, distributed the questionnaires and reiterated the goal of the study and the anonymity of data collection, and explained the use of the Likert scales. They remained present throughout the data collection.

Domain-specific academic self-concepts

Self-concepts for mathematics (mathematics domain) and German (native language, verbal domain) were assessed with two scales from the PISA 2000 questionnaire (Artelt et al., 2004 ). Both scales consist of three items, each ranging from “1” (“not true at all”) to “4” (“completely true”). The mathematics self-concept scale (e.g., “I have always been good at math”) achieved good reliability at both measurement points ( ω T1  = 0.87, ω T2  = 0.87). The verbal self-concept scale (e.g., “I learn fast in my German class”) achieved adequate reliability ( ω T1  = 0.67, ω T2  = 0.73).

Goal orientations

To capture students’ goal orientations, the scales by Spinath and colleagues (Spinath et al., 2012 ) have been used. Answers were rated on a 5-point Likert scale, from “1” (“not true at all”) to “5” (“completely true”). The subscale mastery goal orientation featured eight items (e.g., “At school, I like to learn as much as possible”), with reliability of ω T1  = 0.82. The subscale approach-performance goal orientation consisted of seven items (e.g., “In school, I want to get better grades and feedback than others”) and achieved proper internal consistency ( ω T1  = 0.79). The avoidance-performance goals subscale consisted of eight items (e.g., “In school, I try to avoid that other students think that I was stupid”) and exhibited good reliability ( ω T1  = 0.84). Goal orientations were tested as potential mediators, so only students’ ratings on these variables at t1 have been used.

At both waves, students’ self-reported grades in the respective subjects from their certificates at the end of the previous school term have been collected. German school grades range from “1” (best possible outcome) to “6” (worst possible outcome). Accordingly, in the Math model the corresponding Math grade was utilized, whereas the German grade was used in the German model. The grades were reverse recoded in the process of data analyses for better interpretation of the results, so that higher scores reflect greater achievement. Additionally, the German grades with the LGVT 6–12 (Schneider et al., 2007 ) have been considered in the verbal model as covariates to address potential bias of self-report.

The existing studies show that girls tend to report higher verbal self-concepts, whereas boys tend to report higher mathematical self-concepts (Heyder et al., 2017 ; Mejía-Rodríguez et al., 2020 ; Skaalvik & Skaalvik, 2004 ). Therefore, students’ gender was included as a covariate. Students’ age was also incorporated into the model as a control variable, as the existing studies show age-related differences in the development of academic self-concepts, goal orientations, and achievement (Marsh, 1989 ; Perinelli et al., 2022 ).

Missing data

The present study was subject to a certain degree of missingness. Across all observed values, 6.94% were missing, as a result of 427 incomplete cases. Missingness across all 41 observed variables varied between 0 and 33.22%. To investigate whether missingness would lead to a substantial bias, missing data patterns were examined. The most prominent missing data pattern was caused by dropping out of the study ( n  = 306) at t2.

A series of Bonferroni corrected t tests was run on the manifest variables to examine whether the students who dropped out of the study differed significantly in the t1 variables from those who remained. Students who dropped out had significantly worse grades in mathematics ( t (693.23) = –4.54, p adj  < 0.01). In the models, full information maximum likelihood (FIML) estimation was used to compensate all missing data. In the presence of missingness, FIML estimation has proved to be superior to other missing data techniques (e.g., listwise deletion), and leads to less biased estimates while retaining statistical power (Schafer & Graham, 2002 ).

Statistical analyses

To examine the hypotheses, multi-group cross-lagged panel analyses with additional indirect effects in Mplus 8.5 (Muthén & Muthén, 1998 – 2017 ) have been conducted. The models examine reciprocal effects from one construct at one point of measurement on another construct (i.e., cross-lagged effect) and simultaneously to the same construct at a later point of measurement (i.e., autoregressive effect) (Geiser, 2013 ; Kearney, 2017 ). The (1) verbal model and (2) mathematics model included autoregressive paths (i.e., stability of self-concept and grades) and cross-lagged paths (i.e., reciprocal effects of self-concept on grades). Additionally, the t1 achievement goal orientations were included as mediators. The indirect effects were estimated using the delta method with symmetric confidence intervals (MacKinnon, 2008 ). The effects were considered as significant if the 95% confidence interval did not include 0. For direct effects, the conventional p  < 0.05 criterion was applied. The cross-lagged effect sizes were estimated as recommended by Orth and colleagues (Orth et al., 2022 ).

A multiple-group approach was used to determine whether there are any significant differences in the autoregressive and lagged regression coefficients between students from SDL and TDL. With this method, group variations in regression coefficients can be identified as moderating or interaction effects (Mulder & Hamaker, 2021 ). More specifically, a model in which all regression coefficients and covariates are constrained to be the same across the groups is contrasted with a multiple group CLPM with no constraints across the groups. It is possible to determine whether or not (some of) the lagged coefficients differ between the groups using the χ 2 -difference test (Self & Liang, 1987 ).

Model fit was evaluated by χ 2 , RMSEA, SRMR, and CFI (Hu & Bentler, 1999 ). Typically, a good fit to the data is indicated by CFI higher than 0.90, RMSEA less than 0.05, and SRMR less than 0.08. The prevailing consensus is that CFI values higher than 0.95 and RMSEA and SRMR values lower than 0.05 or 0.06 indicate a strong fit to the data.

All models were specified using the MLR estimator. In order to address the multilevel nature of the dataset (i.e., students nested in classes: n classes SDL  = 36; n classes TDL  = 21; average number of students in class = 18.14), type is complex that adjusts standard errors by adding sampling weights to the estimates in relation to the clusters has been used (Asparouhov, 2005 ).

Descriptive statistics and measurement invariance

Table 1 shows the descriptive statistics, while Table  2 demonstrates bivariate correlations between all variables.

Before the scales were summarized into variables, we conducted confirmatory factor analyses to examine whether the underlying constructs were adequately measured by the scales indicators. Subsequently, we added parameter restrictions in a step-wise manner following the approach by Little ( 2013 ) to ensure measurement invariance across groups and time. The corresponding analyses are provided in the Electronic Supplement 1 .

Mathematic self-concept

A multi-group cross-lagged panel model was run for both learning environments. This model fit the data well ( χ 2 (12) = 24.071, p  = 0.02, CFI = 0.99, RMSEA [90% CI] = 0.042 [0.016–0.066], SRMR = 0.02). Subsequently, a model in which all paths were invariant across groups was run, which also showed acceptable fit indices: χ 2 (40) = 63.96, p  < 0.05; CFI = 0.99, RMSEA = 0.032 (0.016–0.046); SRMR = 0.05. The χ 2 -difference test of these two nested models yields Δχ 2 (28) = 41.69, p  < 0.05, which implies that the autoregressive and lagged effects of self-concept and achievement as well as the covariances appear not to be the same for students from TDL and SDL.

SDL—direct and indirect effects

Figure  1 depicts direct and indirect effects and covariates between the variables in SDL. The Reciprocal Effects Model was confirmed, as academic self-concept and achievement in math were reciprocally interwoven over time. The effect sizes can be interpreted as large as standardized coefficients are 0.12 and higher (Orth et al., 2022 ). However, no statistically significant indirect path could be found. The model explained 43.8% of variance in grades ( R 2  = 0.49) and 53.3% of variance in academic self-concept ( R 2  = 0.53) at t2. Interestingly, mathematic academic self-concept at t1 was significantly associated with performance-approach goals, whereas grades in general were negatively associated with performance-avoidance goals; and grades at t1 positively associated with mastery goals. Boys reported higher academic self-concept at t1 and t2, whereas older students reported lower academic self-concept at t1.

Multi-group cross-lagged panel model with indirect effects for students in SDL environments in mathematics. Note: Estimates are shown as unstandardized (first position) and standardized (second position) values; only significant effects are displayed; non-significant path are shown in dotted lines; * p  < 0.05, ** p  < 0.01, *** p  < 0.001

TDL—direct and indirect effects

Figure  2 demonstrates direct and indirect effects and covariates between the variables for TDL. The Reciprocal Effects Model was not confirmed, as academic self-concept is associated with achievement in mathematics over time but not vice versa. The effect size of academic self-concept and achievement over time can be interpreted as large (β  = 0.33) (Orth et al., 2022 ). However, no statistically significant indirect path could be found. Students with higher academic self-concept tend to follow a mastery or performance-approach goal orientation, whereas grades negatively associate with the performance-avoidance goal orientation. The model explained 45.0% of variance in grades ( R 2  = 0.45) and 52.7% of variance in academic self-concept ( R 2  = 0.53) at t2. Boys reported higher academic self-concept at t1, whereas older students reported lower academic self-concept at t2.

Multi-group cross-lagged panel model with indirect effects for students in TDL environments in mathematics. Note: Estimates are shown as unstandardized (first position) and standardized (second position) values; only significant effects are displayed; non-significant paths are shown in dotted lines; * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Verbal self-concept

A multi-group cross-lagged panel model was run and fit the data well: χ 2 (24) = 51.22, p  < 0.05; CFI = 0.98, RMSEA = 0.044 (0.027–0.061); SRMR = 0.26). Subsequently, a model in which all paths are invariant across groups was run, which also showed acceptable fit indices: ( χ 2 (64) = 92.192, p  < 0.05, CFI = 0.98, RMSEA [90% CI] = 0.028 [0.013–0.040], SRMR = 0.05). The χ 2 -difference test of these two nested models yields Δχ 2 (41) = 44.59, p  > 0.05, which implies that statistically both models fit equally well (Werner & Schermelleh-Engel, 2010 ). In the next step, all paths and covariances, which were statistically non-significant in both groups, have been set to be free across groups. This model with all statistically significant paths invariant across groups and all non-significant paths free across groups showed acceptable fit indices: χ 2 (51) = 89.32, p  < 0.05; CFI = 0.97, RMSEA = 0.036 (0.023–0.048); SRMR = 0.55). The χ 2 -difference test between this model and the prior one yields Δχ 2 (33) = 48.02, p  < 0.05, which implies that the statistically significant autoregressive and lagged effects of self-concept and achievement as well as the covariances appear not to be the same between students from TDL and SDL.

Figure  3 depicts all direct and indirect effects as well as the covariates between the variables for students in SDL. The Reciprocal Effects Model was confirmed for the verbal domain. The effect sizes can be interpreted as large ( β  = 0.16) (Orth et al., 2022 ). However, no statistically significant indirect paths could be found. Students with higher verbal self-concept tend to follow a mastery or performance-approach goal orientation, whereas grades negatively associated with the performance-avoidance goal orientation. The model explained 38.2% of variance in grades ( R 2  = 0.38) and 30.9% of variance in academic self-concept ( R 2  = 0.31) at t2. Boys reported lower academic self-concept and poorer grades at t2. The better the students’ results in the reading comprehension and speed test, the better their grades and the higher their academic self-concept at t1 were.

Multi-group cross-lagged panel model with indirect effects for students in SDL environments in German (verbal domain). Note: Estimates are shown as unstandardized (first position) and standardized (second position) values; only significant effects are displayed; non-significant paths are shown in dotted lines; * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Figure  4 depicts all direct and indirect effects as well as the covariates between the variables for students in TDL. The Reciprocal Effects Model could not be confirmed for students in TDL. The effect size from verbal self-concept to subsequent achievement can be estimated as large ( β  = 0.23) (Orth et al., 2022 ). Additionally, no statistically significant indirect path could be found. Students with higher academic self-concept tend to follow a mastery or performance-approach goal orientation, whereas students with better grades, are likely to follow a mastery goal orientation. The model explained 37.3% of variance in grades ( R 2  = 0.37) and 37.6% of variance in academic self-concept ( R 2  = 0.38) at t2. Boys reported lower academic self-concept at t1, whereas older students tend to report poorer grades and lower academic self-concept at t2. Interestingly, students’ results in the reading comprehension and speed test had no association to their grades or verbal self-concepts.

Multi-group cross-lagged panel model with indirect effects for students in TDL environments in German (verbal domain). Note: Estimates are shown as unstandardized (first position) and standardized (second position) values; only significant effects are displayed; non-significant paths are shown in dotted lines; * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Based on the Stage-Environment Fit Theory and the Reciprocal Effects Model, the major aim of the current study was to test whether (a) there is a reciprocal association between domain-specific academic self-concepts and grades over time in both SDL and TDL and whether (b)—in extension of the Reciprocal Effects Model—students’ goal orientations would mediate this association.

In line with H1a, substantial differences between students from TDL and SDL environments could be found for both mathematics and verbal domains. The Reciprocal Effects Model could only be confirmed for SDL environment for verbal and mathematics models alike. It may be explained by a suggestion, that in SDL, due to large degree of self-determination and self-regulation students may perceive their achievements as a part of their academic self-concepts, so these concepts are better interrelated over time (in this study: 0.5 years of secondary school). There may be several reasons why the Reciprocal Effects Model did not function in the TDL. First, in TDL grades are externally assigned by teachers and might be less associated with students’ academic self-concepts over time. Second, as students in the study attended to “intermediate” track (see Arens et al., 2018 for more details on German tracking), their academic self-concepts may be vulnerable and rather associated with previously obtained last years’ grades (e.g., Kastens & van Wickeren, 2023 ). This finding shows that more research on the longitudinal interplay of self-concepts and grades especially in “intermediate” track is needed, as previous research found domain-specific associations between academic self-concepts and grades in both academic and vocational tracks (however, not over time) (Arens et al., 2018 ). The findings of the study show in TDL/”intermediate” track, the longitudinal association between self-concept and achievement may be unidirectional, supporting the self-enhancement model (Jones & Grieneeks, 1970 ) that postulates that prior school self-concept leads to later academic achievement, for both verbal and mathematic domains. In addition, (a) there exist other studies (e.g., Skaalvik & Hagtvet, 1990 ) that do not confirm the reciprocal effects, possibly due to developmental changes and educational demands in secondary schools or statistical differences between the model(s) that different author(s) use (see more in Burns et al. ( 2020 )). In detail, the between-person differences in self-concept may have caused the reciprocal effects in “traditional” CLPM analyses (Burns et al., 2020 ; Hübner et al., 2023 ).

In contrast to H1b, none of the goal orientations functioned as a mediator between academic self-concept and achievement in both domains. This finding stands in line with some of the existing research (e.g., Seaton et al., 2014 ; Steinmayr et al., 2019 ) that presents academic self-concept as a more important predictor of academic success than goal orientations. The direct effects between the constructs in both models support previous research (e.g., Marsh et al., 2015 ; Retelsdorf et al., 2014 ), such as both verbal and mathematics self-concepts significantly associated with achievement in each subject within time at both measurement points. Even though goal orientations did not mediate the association between academic self-concept and achievement, both verbal and mathematics models for SDL and TDL showed domain-specific differences in the interplay of academic self-concepts, grades, and goal orientations. For SDL model in mathematics, the mathematic academic self-concept was positively associated with performance-approach, but not mastery or performance-avoidance goal orientations. In contrast, for the SDL verbal model, the verbal academic self-concept positively associated with both mastery and performance-approach goal orientations. While the result for the verbal model stands in line with the existing learning environment unspecific research (e.g., Jiang et al., 2014 ; Lee et al., 2014 ), and underline the importance of a positive academic self-concept in practical terms, the results for the mathematics model are somewhat surprising. According to the properties and characteristics of SDL, one would rather expect mastery than performance goal orientations to associate with the academic self-concept. This finding suggests that, first, social comparison processes (e.g., comparison of own results on competency grids to the progress of peers) in SDL may be underestimated. Second, it might mean that there might be substantial differences in SDL environments, depending on how they are established in practice (in thus study SDL in class was addressed by use of competency grids). Third, as the verbal model included reading speed and comprehension as covariates, and mathematics did not, that could indicate that more complex assessment of achievement, than grades, is warranted in future studies. In turn, the grades at t1 negatively associated in SDL with performance-avoidance goals in verbal and mathematics models alike. In other words, the better grades the students reported, the less performance-avoidance goals they revealed. One possible explanation in school environment could lie in the nature of feedback and the absence of expectation of grading for task the students get in SDL, as some studies (e.g., Pulfrey et al., 2011 ) show that in particular that students’ expectation of a grade for task accomplishment consistently led to greater adoption of performance-avoidance goal orientations.

The TDL models in mathematics and verbal domains alike show academic self-concept is positively associated with mastery goals and performance-approach goals, but not performance-avoidance goals, which stands in line with the existing research (e.g., Jiang et al., 2014 ; Lee, et al., 2014 ). Interestingly, grades in both domains are positively associated with mastery goal orientations, which also some other existing studies report (e.g., Sparfeldt et al., 2015 ), but were not related to performance-approach goal orientations. This finding might indicate that while performance-approach goals relate to one’s wish to outperform others, grades might be not a reliable indicator of competence demonstration in school context. Thus, students might rather exhibit their skills in participating in group discussions or engaging in extracurricular activities that do not directly impact their grades.

Gender played an important role for both verbal and mathematical self-concepts for SDL and TDL students. This finding is supported by previous TDL-based studies that state that while girls report higher verbal self-concepts, boys tend to report higher mathematical self-concepts (e.g., Heyder et al., 2017 ; Mejía-Rodríguez et al., 2020 ; Skaalvik & Skaalvik, 2004 ). Age-related differences were, however, only relevant for TDL students, suggested by previous research: older students tend to have lower academic self-concepts and worse grades (Perinelli et al., 2022 ). This finding may suggest that academic self-concepts of students in SDL are less sensitive to (or exhibit slower changes) in the process of maturation, which means use of SDL practices among adolescents may present a working practical intervention aimed at diminishing the age-specific academic self-concept decline.

Overall, the results show further research on the reciprocal associations between domain-specific academic self-concepts and achievement in “intermediate track” is needed, using statistical models that consider between-person differences. In the “traditional” TDL setting, academic self-concept might be a stronger component than achievement. In contrast, to that, for students in SDL, the Reciprocal Effects Model was confirmed, i.e., that academic self-concept and grades were balanced variables. Goal orientations did not prove to be mediators for both learning environments alike.

Strengths and limitations

One of the limitations of the study is the use of self-reported data, even though criticism towards self-report data may as well address non-self-report data (Chan, 2009 ). Future studies should include multiple sources of data, such as teachers, parents, or peers to gather multiple perspectives. Also, the data was first collected in 2015, while school environments may have further developed since then. Furthermore, there is substantial criticism with regard to the use of self-reported grades as students’ achievement indicator (Kuncel et al., 2005 ) as self-reported grades tend to overestimate the actual grades. However, previous research has shown that self-report of grades (in the German school system) is sufficiently reliable (e.g., Dickhäuser & Plenter, 2005 ), and even though there are slight variations in longitudinal research within domains, self-reported grades are an adequate measure to address achievement in school context (Sticca et al., 2017 ). In addition, this study controlled for reading speed and comprehension in the verbal model. Unfortunately, no supplementary achievement tests in mathematics were conducted in this study.

Another limitation is that while the study uses a domain-specific approach, the achievement goal orientations were assessed domain-unspecifically. However, achievement goals have been found to be highly domain-unspecific (e.g., Hornstra et al., 2016 ). Recent research on the role of conceptualizing items that measure achievement motivation in data collection process (Michel et al., 2020 ) shows that domain-specific variance can be explained by self-concept and self-esteem, but not by domain-specific achievement motivation. In other words, goal orientations are either domain-unspecific in general or hard to grasp by domain-specification of items in the process of data collection, which should definitely be addressed in further studies. Furthermore, the independent and mediation variables were measured both at T1; and therefore, the directionality of the paths in the SEM is presumed to be conceptual rather than causal. Finally, the study design fails to disentangle trait from state variance, as recommended in the revised latent state trait theory (Steyer et al., 2015 ); and therefore, the findings should be treated with caution and further tested in a random-intercept cross-lagged model.

A clear strength of the study is the domain-specific exploration of the effects of mastery, and performance-approach and performance-avoidance goal orientations in the Reciprocal Effects Model in two learning environments, which has not been done before. Another important strength is clarity in definitions of academic self-concept and SDL, as the existing literature shows (a) considerable confusion and interchangeable use of the terms “self-concept” and “self-efficacy,” as well as (b) interchangeable use of the terms “self-directed learning” and “self-regulated learning,” even in definition of the term itself (for an overview, see Saks & Leijen, 2014 ). Considering this fact, future studies on both academic self-concept and SDL should focus on clear definitions of terms under research and interpret existing results with caution.

Conclusions

The present study tested an extension of the Reciprocal Effects Model by including the three different goal orientations as possible mediators, and explore possible differences from students from SDL and TDL. The Reciprocal Effects Model effects could only be confirmed for SDL, and the extension by goal orientations as mediators could not be confirmed. In practical terms, this means that while in SDL academic self-concept and achievement are interwoven, in TDL settings more attention should be paid to the formation of positive and realistic academic self-concepts of students through helpful teacher feedback (e.g., Burnett, 2003 ) rather than grades, as it would improve not only current but also future achievement.

In sum, the results expand the existing findings: while for general academic self-concept mastery goal orientation functions as a mediator in the Reciprocal Effects Model (Bakadorova & Raufelder, 2020 ), this finding could not be confirmed for the verbal/mathematics domains. In addition, the present findings uncover differences in the interplay of academic self-concepts, grades, and goal orientations between SDL and TDL which underlines the importance of learning environments for both theoretical and practical implications.

Data availability

Data will be made available on request.

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Olga Steinberg, Stefan Kulakow & Diana Raufelder

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Writing (introduction and discussion): Olga Steinberg.

Conceptualization, methodology, investigation, data collection: Stefan Kulakow.

Writing (introduction and discussion): Diana Raufelder.

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Olga Steinberg. Department of Psychology, Greifswald University, Franz-Mehring-Straße 47, 17489 Greifswald, Germany. E-mail: [email protected]

Current themes of research :

Academic self-concept in adolescence. Social and motivational relationships with peers and teachers in adolescence. Emergence and development of interest in the school context. Qualitative and quantitative methods in educational research.

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Stefan Kulakow. Department of Educational Science, Greifswald University, Ernst-Lohmeyer-Platz 3, 17489 Greifswald, Germany.

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Diana Raufelder . Department of Educational Science, Greifswald University, Ernst-Lohmeyer Platz 3, 17489 Greifswald, Germany.

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(Raufelder, D., & Kulakow, S. (2022). The role of social belonging and exclusion at school and the teacher-student relationship for the development of learned helplessness in adolescents . British Journal of Educational Psychology, 92(1), 59–81. https://doi.org/10.1111/bjep.12438 .

Raufelder*, D., Neumann*, N., Domin, M., Romund, L., Golde, S., Lorenz, R., Gleich, T., Beck, A., & Hoferichter, F. (2021). Do belonging and social exclusion at school affect structural brain development during adolescence? Child Development, 92(6), 2213–2223. https://doi.org/10.1111/cdev.13613 *shared first authorship.

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Steinberg, O., Kulakow, S. & Raufelder, D. Academic self-concept, achievement, and goal orientations in different learning environments. Eur J Psychol Educ (2024). https://doi.org/10.1007/s10212-024-00825-6

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Received : 22 September 2023

Revised : 19 February 2024

Accepted : 26 February 2024

Published : 25 March 2024

DOI : https://doi.org/10.1007/s10212-024-00825-6

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How Ambitious Should You Be?

• Ron Carucci

Too much or too little could damage your reputation.

Fostering a healthy level of ambition is not easy, and amidst so much uncertainty, it may seem like a low priority. But having the “pause button” hit — as it has been in most of our lives — makes this a wonderful time to step back and reflect on our professional aspirations. Striking a healthy degree of ambition can be achieved by using this framework, which structures ambition into three dimensions: performance, growth, and achievement. Your innate desires to perform at your best, to grow and become better, and to achieve rewards from your efforts, all reflect your unique identities. You just need to find a healthy balance between them.

Years ago, I was facilitating a board of directors’ succession committee to select the company’s next CEO. The slate was down to two candidates, each of whom had unique strengths and limitations. The committee chair offered a fascinating observation of them, saying, “One is too ambitious, and the other isn’t ambitious enough.” When I probed to better understand her concerns, she described a host of traits spanning each candidate’s degree of self-interest, achievement orientation, self-awareness, and concern for others. In short, the candidate labeled “too ambitious” had been overly assertive about the financial growth of the company and the candidate labeled “not ambitious enough” had spoken too much about their family and personal interests.

• Ron Carucci is co-founder and managing partner at  Navalent , working with CEOs and executives pursuing transformational change. He is the bestselling author of eight books, including To Be Honest and Rising to Power . Connect with him on Linked In at  RonCarucci , and download his free “How Honest is My Team?” assessment.

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