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Systematic review article, a critical review of research on student self-assessment.

• Educational Psychology and Methodology, University at Albany, Albany, NY, United States

This article is a review of research on student self-assessment conducted largely between 2013 and 2018. The purpose of the review is to provide an updated overview of theory and research. The treatment of theory involves articulating a refined definition and operationalization of self-assessment. The review of 76 empirical studies offers a critical perspective on what has been investigated, including the relationship between self-assessment and achievement, consistency of self-assessment and others' assessments, student perceptions of self-assessment, and the association between self-assessment and self-regulated learning. An argument is made for less research on consistency and summative self-assessment, and more on the cognitive and affective mechanisms of formative self-assessment.

This review of research on student self-assessment expands on a review published as a chapter in the Cambridge Handbook of Instructional Feedback ( Andrade, 2018 , reprinted with permission). The timespan for the original review was January 2013 to October 2016. A lot of research has been done on the subject since then, including at least two meta-analyses; hence this expanded review, in which I provide an updated overview of theory and research. The treatment of theory presented here involves articulating a refined definition and operationalization of self-assessment through a lens of feedback. My review of the growing body of empirical research offers a critical perspective, in the interest of provoking new investigations into neglected areas.

Defining and Operationalizing Student Self-Assessment

Without exception, reviews of self-assessment ( Sargeant, 2008 ; Brown and Harris, 2013 ; Panadero et al., 2016a ) call for clearer definitions: What is self-assessment, and what is not? This question is surprisingly difficult to answer, as the term self-assessment has been used to describe a diverse range of activities, such as assigning a happy or sad face to a story just told, estimating the number of correct answers on a math test, graphing scores for dart throwing, indicating understanding (or the lack thereof) of a science concept, using a rubric to identify strengths and weaknesses in one's persuasive essay, writing reflective journal entries, and so on. Each of those activities involves some kind of assessment of one's own functioning, but they are so different that distinctions among types of self-assessment are needed. I will draw those distinctions in terms of the purposes of self-assessment which, in turn, determine its features: a classic form-fits-function analysis.

What is Self-Assessment?

Brown and Harris (2013) defined self-assessment in the K-16 context as a “descriptive and evaluative act carried out by the student concerning his or her own work and academic abilities” (p. 368). Panadero et al. (2016a) defined it as a “wide variety of mechanisms and techniques through which students describe (i.e., assess) and possibly assign merit or worth to (i.e., evaluate) the qualities of their own learning processes and products” (p. 804). Referring to physicians, Epstein et al. (2008) defined “concurrent self-assessment” as “ongoing moment-to-moment self-monitoring” (p. 5). Self-monitoring “refers to the ability to notice our own actions, curiosity to examine the effects of those actions, and willingness to use those observations to improve behavior and thinking in the future” (p. 5). Taken together, these definitions include self-assessment of one's abilities, processes , and products —everything but the kitchen sink. This very broad conception might seem unwieldy, but it works because each object of assessment—competence, process, and product—is subject to the influence of feedback from oneself.

What is missing from each of these definitions, however, is the purpose of the act of self-assessment. Their authors might rightly point out that the purpose is implied, but a formal definition requires us to make it plain: Why do we ask students to self-assess? I have long held that self-assessment is feedback ( Andrade, 2010 ), and that the purpose of feedback is to inform adjustments to processes and products that deepen learning and enhance performance; hence the purpose of self-assessment is to generate feedback that promotes learning and improvements in performance. This learning-oriented purpose of self-assessment implies that it should be formative: if there is no opportunity for adjustment and correction, self-assessment is almost pointless.

Why Self-Assess?

Clarity about the purpose of self-assessment allows us to interpret what otherwise appear to be discordant findings from research, which has produced mixed results in terms of both the accuracy of students' self-assessments and their influence on learning and/or performance. I believe the source of the discord can be traced to the different ways in which self-assessment is carried out, such as whether it is summative and formative. This issue will be taken up again in the review of current research that follows this overview. For now, consider a study of the accuracy and validity of summative self-assessment in teacher education conducted by Tejeiro et al. (2012) , which showed that students' self-assigned marks tended to be higher than marks given by professors. All 122 students in the study assigned themselves a grade at the end of their course, but half of the students were told that their self-assigned grade would count toward 5% of their final grade. In both groups, students' self-assessments were higher than grades given by professors, especially for students with “poorer results” (p. 791) and those for whom self-assessment counted toward the final grade. In the group that was told their self-assessments would count toward their final grade, no relationship was found between the professor's and the students' assessments. Tejeiro et al. concluded that, although students' and professor's assessments tend to be highly similar when self-assessment did not count toward final grades, overestimations increased dramatically when students' self-assessments did count. Interviews of students who self-assigned highly discrepant grades revealed (as you might guess) that they were motivated by the desire to obtain the highest possible grades.

Studies like Tejeiro et al's. (2012) are interesting in terms of the information they provide about the relationship between consistency and honesty, but the purpose of the self-assessment, beyond addressing interesting research questions, is unclear. There is no feedback purpose. This is also true for another example of a study of summative self-assessment of competence, during which elementary-school children took the Test of Narrative Language and then were asked to self-evaluate “how you did in making up stories today” by pointing to one of five pictures, from a “very happy face” (rating of five) to a “very sad face” (rating of one) ( Kaderavek et al., 2004 . p. 37). The usual results were reported: Older children and good narrators were more accurate than younger children and poor narrators, and males tended to more frequently overestimate their ability.

Typical of clinical studies of accuracy in self-evaluation, this study rests on a definition and operationalization of self-assessment with no value in terms of instructional feedback. If those children were asked to rate their stories and then revise or, better yet, if they assessed their stories according to clear, developmentally appropriate criteria before revising, the valence of their self-assessments in terms of instructional feedback would skyrocket. I speculate that their accuracy would too. In contrast, studies of formative self-assessment suggest that when the act of self-assessing is given a learning-oriented purpose, students' self-assessments are relatively consistent with those of external evaluators, including professors ( Lopez and Kossack, 2007 ; Barney et al., 2012 ; Leach, 2012 ), teachers ( Bol et al., 2012 ; Chang et al., 2012 , 2013 ), researchers ( Panadero and Romero, 2014 ; Fitzpatrick and Schulz, 2016 ), and expert medical assessors ( Hawkins et al., 2012 ).

My commitment to keeping self-assessment formative is firm. However, Gavin Brown (personal communication, April 2011) reminded me that summative self-assessment exists and we cannot ignore it; any definition of self-assessment must acknowledge and distinguish between formative and summative forms of it. Thus, the taxonomy in Table 1 , which depicts self-assessment as serving formative and/or summative purposes, and focuses on competence, processes, and/or products.

Table 1 . A taxonomy of self-assessment.

Fortunately, a formative view of self-assessment seems to be taking hold in various educational contexts. For instance, Sargeant (2008) noted that all seven authors in a special issue of the Journal of Continuing Education in the Health Professions “conceptualize self-assessment within a formative, educational perspective, and see it as an activity that draws upon both external and internal data, standards, and resources to inform and make decisions about one's performance” (p. 1). Sargeant also stresses the point that self-assessment should be guided by evaluative criteria: “Multiple external sources can and should inform self-assessment, perhaps most important among them performance standards” (p. 1). Now we are talking about the how of self-assessment, which demands an operationalization of self-assessment practice. Let us examine each object of self-assessment (competence, processes, and/or products) with an eye for what is assessed and why.

What is Self-Assessed?

Monitoring and self-assessing processes are practically synonymous with self-regulated learning (SRL), or at least central components of it such as goal-setting and monitoring, or metacognition. Research on SRL has clearly shown that self-generated feedback on one's approach to learning is associated with academic gains ( Zimmerman and Schunk, 2011 ). Self-assessment of the products , such as papers and presentations, are the easiest to defend as feedback, especially when those self-assessments are grounded in explicit, relevant, evaluative criteria and followed by opportunities to relearn and/or revise ( Andrade, 2010 ).

Including the self-assessment of competence in this definition is a little trickier. I hesitated to include it because of the risk of sneaking in global assessments of one's overall ability, self-esteem, and self-concept (“I'm good enough, I'm smart enough, and doggone it, people like me,” Franken, 1992 ), which do not seem relevant to a discussion of feedback in the context of learning. Research on global self-assessment, or self-perception, is popular in the medical education literature, but even there, scholars have begun to question its usefulness in terms of influencing learning and professional growth (e.g., see Sargeant et al., 2008 ). Eva and Regehr (2008) seem to agree in the following passage, which states the case in a way that makes it worthy of a long quotation:

Self-assessment is often (implicitly or otherwise) conceptualized as a personal, unguided reflection on performance for the purposes of generating an individually derived summary of one's own level of knowledge, skill, and understanding in a particular area. For example, this conceptualization would appear to be the only reasonable basis for studies that fit into what Colliver et al. (2005) has described as the “guess your grade” model of self-assessment research, the results of which form the core foundation for the recurring conclusion that self-assessment is generally poor. This unguided, internally generated construction of self-assessment stands in stark contrast to the model put forward by Boud (1999) , who argued that the phrase self-assessment should not imply an isolated or individualistic activity; it should commonly involve peers, teachers, and other sources of information. The conceptualization of self-assessment as enunciated in Boud's description would appear to involve a process by which one takes personal responsibility for looking outward, explicitly seeking feedback, and information from external sources, then using these externally generated sources of assessment data to direct performance improvements. In this construction, self-assessment is more of a pedagogical strategy than an ability to judge for oneself; it is a habit that one needs to acquire and enact rather than an ability that one needs to master (p. 15).

As in the K-16 context, self-assessment is coming to be seen as having value as much or more so in terms of pedagogy as in assessment ( Silver et al., 2008 ; Brown and Harris, 2014 ). In the end, however, I decided that self-assessing one's competence to successfully learn a particular concept or complete a particular task (which sounds a lot like self-efficacy—more on that later) might be useful feedback because it can inform decisions about how to proceed, such as the amount of time to invest in learning how to play the flute, or whether or not to seek help learning the steps of the jitterbug. An important caveat, however, is that self-assessments of competence are only useful if students have opportunities to do something about their perceived low competence—that is, it serves the purpose of formative feedback for the learner.

How to Self-Assess?

Panadero et al. (2016a) summarized five very different taxonomies of self-assessment and called for the development of a comprehensive typology that considers, among other things, its purpose, the presence or absence of criteria, and the method. In response, I propose the taxonomy depicted in Table 1 , which focuses on the what (competence, process, or product), the why (formative or summative), and the how (methods, including whether or not they include standards, e.g., criteria) of self-assessment. The collections of examples of methods in the table is inexhaustive.

I put the methods in Table 1 where I think they belong, but many of them could be placed in more than one cell. Take self-efficacy , for instance, which is essentially a self-assessment of one's competence to successfully undertake a particular task ( Bandura, 1997 ). Summative judgments of self-efficacy are certainly possible but they seem like a silly thing to do—what is the point, from a learning perspective? Formative self-efficacy judgments, on the other hand, can inform next steps in learning and skill building. There is reason to believe that monitoring and making adjustments to one's self-efficacy (e.g., by setting goals or attributing success to effort) can be productive ( Zimmerman, 2000 ), so I placed self-efficacy in the formative row.

It is important to emphasize that self-efficacy is task-specific, more or less ( Bandura, 1997 ). This taxonomy does not include general, holistic evaluations of one's abilities, for example, “I am good at math.” Global assessment of competence does not provide the leverage, in terms of feedback, that is provided by task-specific assessments of competence, that is, self-efficacy. Eva and Regehr (2008) provided an illustrative example: “We suspect most people are prompted to open a dictionary as a result of encountering a word for which they are uncertain of the meaning rather than out of a broader assessment that their vocabulary could be improved” (p. 16). The exclusion of global evaluations of oneself resonates with research that clearly shows that feedback that focuses on aspects of a task (e.g., “I did not solve most of the algebra problems”) is more effective than feedback that focuses on the self (e.g., “I am bad at math”) ( Kluger and DeNisi, 1996 ; Dweck, 2006 ; Hattie and Timperley, 2007 ). Hence, global self-evaluations of ability or competence do not appear in Table 1 .

Another approach to student self-assessment that could be placed in more than one cell is traffic lights . The term traffic lights refers to asking students to use green, yellow, or red objects (or thumbs up, sideways, or down—anything will do) to indicate whether they think they have good, partial, or little understanding ( Black et al., 2003 ). It would be appropriate for traffic lights to appear in multiple places in Table 1 , depending on how they are used. Traffic lights seem to be most effective at supporting students' reflections on how well they understand a concept or have mastered a skill, which is line with their creators' original intent, so they are categorized as formative self-assessments of one's learning—which sounds like metacognition.

In fact, several of the methods included in Table 1 come from research on metacognition, including self-monitoring , such as checking one's reading comprehension, and self-testing , e.g., checking one's performance on test items. These last two methods have been excluded from some taxonomies of self-assessment (e.g., Boud and Brew, 1995 ) because they do not engage students in explicitly considering relevant standards or criteria. However, new conceptions of self-assessment are grounded in theories of the self- and co-regulation of learning ( Andrade and Brookhart, 2016 ), which includes self-monitoring of learning processes with and without explicit standards.

However, my research favors self-assessment with regard to standards ( Andrade and Boulay, 2003 ; Andrade and Du, 2007 ; Andrade et al., 2008 , 2009 , 2010 ), as does related research by Panadero and his colleagues (see below). I have involved students in self-assessment of stories, essays, or mathematical word problems according to rubrics or checklists with criteria. For example, two studies investigated the relationship between elementary or middle school students' scores on a written assignment and a process that involved them in reading a model paper, co-creating criteria, self-assessing first drafts with a rubric, and revising ( Andrade et al., 2008 , 2010 ). The self-assessment was highly scaffolded: students were asked to underline key phrases in the rubric with colored pencils (e.g., underline “clearly states an opinion” in blue), then underline or circle in their drafts the evidence of having met the standard articulated by the phrase (e.g., his or her opinion) with the same blue pencil. If students found they had not met the standard, they were asked to write themselves a reminder to make improvements when they wrote their final drafts. This process was followed for each criterion on the rubric. There were main effects on scores for every self-assessed criterion on the rubric, suggesting that guided self-assessment according to the co-created criteria helped students produce more effective writing.

Panadero and his colleagues have also done quasi-experimental and experimental research on standards-referenced self-assessment, using rubrics or lists of assessment criteria that are presented in the form of questions ( Panadero et al., 2012 , 2013 , 2014 ; Panadero and Romero, 2014 ). Panadero calls the list of assessment criteria a script because his work is grounded in research on scaffolding (e.g., Kollar et al., 2006 ): I call it a checklist because that is the term used in classroom assessment contexts. Either way, the list provides standards for the task. Here is a script for a written summary that Panadero et al. (2014) used with college students in a psychology class:

• Does my summary transmit the main idea from the text? Is it at the beginning of my summary?

• Are the important ideas also in my summary?

• Have I selected the main ideas from the text to make them explicit in my summary?

• Have I thought about my purpose for the summary? What is my goal?

Taken together, the results of the studies cited above suggest that students who engaged in self-assessment using scripts or rubrics were more self-regulated, as measured by self-report questionnaires and/or think aloud protocols, than were students in the comparison or control groups. Effect sizes were very small to moderate (η 2 = 0.06–0.42), and statistically significant. Most interesting, perhaps, is one study ( Panadero and Romero, 2014 ) that demonstrated an association between rubric-referenced self-assessment activities and all three phases of SRL; forethought, performance, and reflection.

There are surely many other methods of self-assessment to include in Table 1 , as well as interesting conversations to be had about which method goes where and why. In the meantime, I offer the taxonomy in Table 1 as a way to define and operationalize self-assessment in instructional contexts and as a framework for the following overview of current research on the subject.

An Overview of Current Research on Self-Assessment

Several recent reviews of self-assessment are available ( Brown and Harris, 2013 ; Brown et al., 2015 ; Panadero et al., 2017 ), so I will not summarize the entire body of research here. Instead, I chose to take a birds-eye view of the field, with goal of reporting on what has been sufficiently researched and what remains to be done. I used the references lists from reviews, as well as other relevant sources, as a starting point. In order to update the list of sources, I directed two new searches 1 , the first of the ERIC database, and the second of both ERIC and PsychINFO. Both searches included two search terms, “self-assessment” OR “self-evaluation.” Advanced search options had four delimiters: (1) peer-reviewed, (2) January, 2013–October, 2016 and then October 2016–March 2019, (3) English, and (4) full-text. Because the focus was on K-20 educational contexts, sources were excluded if they were about early childhood education or professional development.

The first search yielded 347 hits; the second 1,163. Research that was unrelated to instructional feedback was excluded, such as studies limited to self-estimates of performance before or after taking a test, guesses about whether a test item was answered correctly, and estimates of how many tasks could be completed in a certain amount of time. Although some of the excluded studies might be thought of as useful investigations of self-monitoring, as a group they seemed too unrelated to theories of self-generated feedback to be appropriate for this review. Seventy-six studies were selected for inclusion in Table S1 (Supplementary Material), which also contains a few studies published before 2013 that were not included in key reviews, as well as studies solicited directly from authors.

The Table S1 in the Supplementary Material contains a complete list of studies included in this review, organized by the focus or topic of the study, as well as brief descriptions of each. The “type” column Table S1 (Supplementary Material) indicates whether the study focused on formative or summative self-assessment. This distinction was often difficult to make due to a lack of information. For example, Memis and Seven (2015) frame their study in terms of formative assessment, and note that the purpose of the self-evaluation done by the sixth grade students is to “help students improve their [science] reports” (p. 39), but they do not indicate how the self-assessments were done, nor whether students were given time to revise their reports based on their judgments or supported in making revisions. A sentence or two of explanation about the process of self-assessment in the procedures sections of published studies would be most useful.

Figure 1 graphically represents the number of studies in the four most common topic categories found in the table—achievement, consistency, student perceptions, and SRL. The figure reveals that research on self-assessment is on the rise, with consistency the most popular topic. Of the 76 studies in the table in the appendix, 44 were inquiries into the consistency of students' self-assessments with other judgments (e.g., a test score or teacher's grade). Twenty-five studies investigated the relationship between self-assessment and achievement. Fifteen explored students' perceptions of self-assessment. Twelve studies focused on the association between self-assessment and self-regulated learning. One examined self-efficacy, and two qualitative studies documented the mental processes involved in self-assessment. The sum ( n = 99) of the list of research topics is more than 76 because several studies had multiple foci. In the remainder of this review I examine each topic in turn.

Figure 1 . Topics of self-assessment studies, 2013–2018.

Consistency

Table S1 (Supplementary Material) reveals that much of the recent research on self-assessment has investigated the accuracy or, more accurately, consistency, of students' self-assessments. The term consistency is more appropriate in the classroom context because the quality of students' self-assessments is often determined by comparing them with their teachers' assessments and then generating correlations. Given the evidence of the unreliability of teachers' grades ( Falchikov, 2005 ), the assumption that teachers' assessments are accurate might not be well-founded ( Leach, 2012 ; Brown et al., 2015 ). Ratings of student work done by researchers are also suspect, unless evidence of the validity and reliability of the inferences made about student work by researchers is available. Consequently, much of the research on classroom-based self-assessment should use the term consistency , which refers to the degree of alignment between students' and expert raters' evaluations, avoiding the purer, more rigorous term accuracy unless it is fitting.

In their review, Brown and Harris (2013) reported that correlations between student self-ratings and other measures tended to be weakly to strongly positive, ranging from r ≈ 0.20 to 0.80, with few studies reporting correlations >0.60. But their review included results from studies of any self-appraisal of school work, including summative self-rating/grading, predictions about the correctness of answers on test items, and formative, criteria-based self-assessments, a combination of methods that makes the correlations they reported difficult to interpret. Qualitatively different forms of self-assessment, especially summative and formative types, cannot be lumped together without obfuscating important aspects of self-assessment as feedback.

Given my concern about combining studies of summative and formative assessment, you might anticipate a call for research on consistency that distinguishes between the two. I will make no such call for three reasons. One is that we have enough research on the subject, including the 22 studies in Table S1 (Supplementary Material) that were published after Brown and Harris's review (2013 ). Drawing only on studies included in Table S1 (Supplementary Material), we can say with confidence that summative self-assessment tends to be inconsistent with external judgements ( Baxter and Norman, 2011 ; De Grez et al., 2012 ; Admiraal et al., 2015 ), with males tending to overrate and females to underrate ( Nowell and Alston, 2007 ; Marks et al., 2018 ). There are exceptions ( Alaoutinen, 2012 ; Lopez-Pastor et al., 2012 ) as well as mixed results, with students being consistent regarding some aspects of their learning but not others ( Blanch-Hartigan, 2011 ; Harding and Hbaci, 2015 ; Nguyen and Foster, 2018 ). We can also say that older, more academically competent learners tend to be more consistent ( Hacker et al., 2000 ; Lew et al., 2010 ; Alaoutinen, 2012 ; Guillory and Blankson, 2017 ; Butler, 2018 ; Nagel and Lindsey, 2018 ). There is evidence that consistency can be improved through experience ( Lopez and Kossack, 2007 ; Yilmaz, 2017 ; Nagel and Lindsey, 2018 ), the use of guidelines ( Bol et al., 2012 ), feedback ( Thawabieh, 2017 ), and standards ( Baars et al., 2014 ), perhaps in the form of rubrics ( Panadero and Romero, 2014 ). Modeling and feedback also help ( Labuhn et al., 2010 ; Miller and Geraci, 2011 ; Hawkins et al., 2012 ; Kostons et al., 2012 ).

An outcome typical of research on the consistency of summative self-assessment can be found in row 59, which summarizes the study by Tejeiro et al. (2012) discussed earlier: Students' self-assessments were higher than marks given by professors, especially for students with poorer results, and no relationship was found between the professors' and the students' assessments in the group in which self-assessment counted toward the final mark. Students are not stupid: if they know that they can influence their final grade, and that their judgment is summative rather than intended to inform revision and improvement, they will be motivated to inflate their self-evaluation. I do not believe we need more research to demonstrate that phenomenon.

The second reason I am not calling for additional research on consistency is a lot of it seems somewhat irrelevant. This might be because the interest in accuracy is rooted in clinical research on calibration, which has very different aims. Calibration accuracy is the “magnitude of consent between learners' true and self-evaluated task performance. Accurately calibrated learners' task performance equals their self-evaluated task performance” ( Wollenschläger et al., 2016 ). Calibration research often asks study participants to predict or postdict the correctness of their responses to test items. I caution about generalizing from clinical experiments to authentic classroom contexts because the dismal picture of our human potential to self-judge was painted by calibration researchers before study participants were effectively taught how to predict with accuracy, or provided with the tools they needed to be accurate, or motivated to do so. Calibration researchers know that, of course, and have conducted intervention studies that attempt to improve accuracy, with some success (e.g., Bol et al., 2012 ). Studies of formative self-assessment also suggest that consistency increases when it is taught and supported in many of the ways any other skill must be taught and supported ( Lopez and Kossack, 2007 ; Labuhn et al., 2010 ; Chang et al., 2012 , 2013 ; Hawkins et al., 2012 ; Panadero and Romero, 2014 ; Lin-Siegler et al., 2015 ; Fitzpatrick and Schulz, 2016 ).

Even clinical psychological studies that go beyond calibration to examine the associations between monitoring accuracy and subsequent study behaviors do not transfer well to classroom assessment research. After repeatedly encountering claims that, for example, low self-assessment accuracy leads to poor task-selection accuracy and “suboptimal learning outcomes” ( Raaijmakers et al., 2019 , p. 1), I dug into the cited studies and discovered two limitations. The first is that the tasks in which study participants engage are quite inauthentic. A typical task involves studying “word pairs (e.g., railroad—mother), followed by a delayed judgment of learning (JOL) in which the students predicted the chances of remembering the pair… After making a JOL, the entire pair was presented for restudy for 4 s [ sic ], and after all pairs had been restudied, a criterion test of paired-associate recall occurred” ( Dunlosky and Rawson, 2012 , p. 272). Although memory for word pairs might be important in some classroom contexts, it is not safe to assume that results from studies like that one can predict students' behaviors after criterion-referenced self-assessment of their comprehension of complex texts, lengthy compositions, or solutions to multi-step mathematical problems.

The second limitation of studies like the typical one described above is more serious: Participants in research like that are not permitted to regulate their own studying, which is experimentally manipulated by a computer program. This came as a surprise, since many of the claims were about students' poor study choices but they were rarely allowed to make actual choices. For example, Dunlosky and Rawson (2012) permitted participants to “use monitoring to effectively control learning” by programming the computer so that “a participant would need to have judged his or her recall of a definition entirely correct on three different trials, and once they judged it entirely correct on the third trial, that particular key term definition was dropped [by the computer program] from further practice” (p. 272). The authors note that this study design is an improvement on designs that did not require all participants to use the same regulation algorithm, but it does not reflect the kinds of decisions that learners make in class or while doing homework. In fact, a large body of research shows that students can make wise choices when they self-pace the study of to-be-learned materials and then allocate study time to each item ( Bjork et al., 2013 , p. 425):

In a typical experiment, the students first study all the items at an experimenter-paced rate (e.g., study 60 paired associates for 3 s each), which familiarizes the students with the items; after this familiarity phase, the students then either choose which items they want to restudy (e.g., all items are presented in an array, and the students select which ones to restudy) and/or pace their restudy of each item. Several dependent measures have been widely used, such as how long each item is studied, whether an item is selected for restudy, and in what order items are selected for restudy. The literature on these aspects of self-regulated study is massive (for a comprehensive overview, see both Dunlosky and Ariel, 2011 and Son and Metcalfe, 2000 ), but the evidence is largely consistent with a few basic conclusions. First, if students have a chance to practice retrieval prior to restudying items, they almost exclusively choose to restudy unrecalled items and drop the previously recalled items from restudy ( Metcalfe and Kornell, 2005 ). Second, when pacing their study of individual items that have been selected for restudy, students typically spend more time studying items that are more, rather than less, difficult to learn. Such a strategy is consistent with a discrepancy-reduction model of self-paced study (which states that people continue to study an item until they reach mastery), although some key revisions to this model are needed to account for all the data. For instance, students may not continue to study until they reach some static criterion of mastery, but instead, they may continue to study until they perceive that they are no longer making progress.

I propose that this research, which suggests that students' unscaffolded, unmeasured, informal self-assessments tend to lead to appropriate task selection, is better aligned with research on classroom-based self-assessment. Nonetheless, even this comparison is inadequate because the study participants were not taught to compare their performance to the criteria for mastery, as is often done in classroom-based self-assessment.

The third and final reason I do not believe we need additional research on consistency is that I think it is a distraction from the true purposes of self-assessment. Many if not most of the articles about the accuracy of self-assessment are grounded in the assumption that accuracy is necessary for self-assessment to be useful, particularly in terms of subsequent studying and revision behaviors. Although it seems obvious that accurate evaluations of their performance positively influence students' study strategy selection, which should produce improvements in achievement, I have not seen relevant research that tests those conjectures. Some claim that inaccurate estimates of learning lead to the selection of inappropriate learning tasks ( Kostons et al., 2012 ) but they cite research that does not support their claim. For example, Kostons et al. cite studies that focus on the effectiveness of SRL interventions but do not address the accuracy of participants' estimates of learning, nor the relationship of those estimates to the selection of next steps. Other studies produce findings that support my skepticism. Take, for instance, two relevant studies of calibration. One suggested that performance and judgments of performance had little influence on subsequent test preparation behavior ( Hacker et al., 2000 ), and the other showed that study participants followed their predictions of performance to the same degree, regardless of monitoring accuracy ( van Loon et al., 2014 ).

Eva and Regehr (2008) believe that:

Research questions that take the form of “How well do various practitioners self-assess?” “How can we improve self-assessment?” or “How can we measure self-assessment skill?” should be considered defunct and removed from the research agenda [because] there have been hundreds of studies into these questions and the answers are “Poorly,” “You can't,” and “Don't bother” (p. 18).

I almost agree. A study that could change my mind about the importance of accuracy of self-assessment would be an investigation that goes beyond attempting to improve accuracy just for the sake of accuracy by instead examining the relearning/revision behaviors of accurate and inaccurate self-assessors: Do students whose self-assessments match the valid and reliable judgments of expert raters (hence my use of the term accuracy ) make better decisions about what they need to do to deepen their learning and improve their work? Here, I admit, is a call for research related to consistency: I would love to see a high-quality investigation of the relationship between accuracy in formative self-assessment, and students' subsequent study and revision behaviors, and their learning. For example, a study that closely examines the revisions to writing made by accurate and inaccurate self-assessors, and the resulting outcomes in terms of the quality of their writing, would be most welcome.

Table S1 (Supplementary Material) indicates that by 2018 researchers began publishing studies that more directly address the hypothesized link between self-assessment and subsequent learning behaviors, as well as important questions about the processes learners engage in while self-assessing ( Yan and Brown, 2017 ). One, a study by Nugteren et al. (2018 row 19 in Table S1 (Supplementary Material)), asked “How do inaccurate [summative] self-assessments influence task selections?” (p. 368) and employed a clever exploratory research design. The results suggested that most of the 15 students in their sample over-estimated their performance and made inaccurate learning-task selections. Nugteren et al. recommended helping students make more accurate self-assessments, but I think the more interesting finding is related to why students made task selections that were too difficult or too easy, given their prior performance: They based most task selections on interest in the content of particular items (not the overarching content to be learned), and infrequently considered task difficulty and support level. For instance, while working on the genetics tasks, students reported selecting tasks because they were fun or interesting, not because they addressed self-identified weaknesses in their understanding of genetics. Nugteren et al. proposed that students would benefit from instruction on task selection. I second that proposal: Rather than directing our efforts on accuracy in the service of improving subsequent task selection, let us simply teach students to use the information at hand to select next best steps, among other things.

Butler (2018 , row 76 in Table S1 (Supplementary Material)) has conducted at least two studies of learners' processes of responding to self-assessment items and how they arrived at their judgments. Comparing generic, decontextualized items to task-specific, contextualized items (which she calls after-task items ), she drew two unsurprising conclusions: the task-specific items “generally showed higher correlations with task performance,” and older students “appeared to be more conservative in their judgment compared with their younger counterparts” (p. 249). The contribution of the study is the detailed information it provides about how students generated their judgments. For example, Butler's qualitative data analyses revealed that when asked to self-assess in terms of vague or non-specific items, the children often “contextualized the descriptions based on their own experiences, goals, and expectations,” (p. 257) focused on the task at hand, and situated items in the specific task context. Perhaps as a result, the correlation between after-task self-assessment and task performance was generally higher than for generic self-assessment.

Butler (2018) notes that her study enriches our empirical understanding of the processes by which children respond to self-assessment. This is a very promising direction for the field. Similar studies of processing during formative self-assessment of a variety of task types in a classroom context would likely produce significant advances in our understanding of how and why self-assessment influences learning and performance.

Student Perceptions

Fifteen of the studies listed in Table S1 (Supplementary Material) focused on students' perceptions of self-assessment. The studies of children suggest that they tend to have unsophisticated understandings of its purposes ( Harris and Brown, 2013 ; Bourke, 2016 ) that might lead to shallow implementation of related processes. In contrast, results from the studies conducted in higher education settings suggested that college and university students understood the function of self-assessment ( Ratminingsih et al., 2018 ) and generally found it to be useful for guiding evaluation and revision ( Micán and Medina, 2017 ), understanding how to take responsibility for learning ( Lopez and Kossack, 2007 ; Bourke, 2014 ; Ndoye, 2017 ), prompting them to think more critically and deeply ( van Helvoort, 2012 ; Siow, 2015 ), applying newfound skills ( Murakami et al., 2012 ), and fostering self-regulated learning by guiding them to set goals, plan, self-monitor and reflect ( Wang, 2017 ).

Not surprisingly, positive perceptions of self-assessment were typically developed by students who actively engaged the formative type by, for example, developing their own criteria for an effective self-assessment response ( Bourke, 2014 ), or using a rubric or checklist to guide their assessments and then revising their work ( Huang and Gui, 2015 ; Wang, 2017 ). Earlier research suggested that children's attitudes toward self-assessment can become negative if it is summative ( Ross et al., 1998 ). However, even summative self-assessment was reported by adult learners to be useful in helping them become more critical of their own and others' writing throughout the course and in subsequent courses ( van Helvoort, 2012 ).

Achievement

Twenty-five of the studies in Table S1 (Supplementary Material) investigated the relation between self-assessment and achievement, including two meta-analyses. Twenty of the 25 clearly employed the formative type. Without exception, those 20 studies, plus the two meta-analyses ( Graham et al., 2015 ; Sanchez et al., 2017 ) demonstrated a positive association between self-assessment and learning. The meta-analysis conducted by Graham and his colleagues, which included 10 studies, yielded an average weighted effect size of 0.62 on writing quality. The Sanchez et al. meta-analysis revealed that, although 12 of the 44 effect sizes were negative, on average, “students who engaged in self-grading performed better ( g = 0.34) on subsequent tests than did students who did not” (p. 1,049).

All but two of the non-meta-analytic studies of achievement in Table S1 (Supplementary Material) were quasi-experimental or experimental, providing relatively rigorous evidence that their treatment groups outperformed their comparison or control groups in terms of everything from writing to dart-throwing, map-making, speaking English, and exams in a wide variety of disciplines. One experiment on summative self-assessment ( Miller and Geraci, 2011 ), in contrast, resulted in no improvements in exam scores, while the other one did ( Raaijmakers et al., 2017 ).

It would be easy to overgeneralize and claim that the question about the effect of self-assessment on learning has been answered, but there are unanswered questions about the key components of effective self-assessment, especially social-emotional components related to power and trust ( Andrade and Brown, 2016 ). The trends are pretty clear, however: it appears that formative forms of self-assessment can promote knowledge and skill development. This is not surprising, given that it involves many of the processes known to support learning, including practice, feedback, revision, and especially the intellectually demanding work of making complex, criteria-referenced judgments ( Panadero et al., 2014 ). Boud (1995a , b) predicted this trend when he noted that many self-assessment processes undermine learning by rushing to judgment, thereby failing to engage students with the standards or criteria for their work.

Self-Regulated Learning

The association between self-assessment and learning has also been explained in terms of self-regulation ( Andrade, 2010 ; Panadero and Alonso-Tapia, 2013 ; Andrade and Brookhart, 2016 , 2019 ; Panadero et al., 2016b ). Self-regulated learning (SRL) occurs when learners set goals and then monitor and manage their thoughts, feelings, and actions to reach those goals. SRL is moderately to highly correlated with achievement ( Zimmerman and Schunk, 2011 ). Research suggests that formative assessment is a potential influence on SRL ( Nicol and Macfarlane-Dick, 2006 ). The 12 studies in Table S1 (Supplementary Material) that focus on SRL demonstrate the recent increase in interest in the relationship between self-assessment and SRL.

Conceptual and practical overlaps between the two fields are abundant. In fact, Brown and Harris (2014) recommend that student self-assessment no longer be treated as an assessment, but as an essential competence for self-regulation. Butler and Winne (1995) introduced the role of self-generated feedback in self-regulation years ago:

[For] all self-regulated activities, feedback is an inherent catalyst. As learners monitor their engagement with tasks, internal feedback is generated by the monitoring process. That feedback describes the nature of outcomes and the qualities of the cognitive processes that led to those states (p. 245).

The outcomes and processes referred to by Butler and Winne are many of the same products and processes I referred to earlier in the definition of self-assessment and in Table 1 .

In general, research and practice related to self-assessment has tended to focus on judging the products of student learning, while scholarship on self-regulated learning encompasses both processes and products. The very practical focus of much of the research on self-assessment means it might be playing catch-up, in terms of theory development, with the SRL literature, which is grounded in experimental paradigms from cognitive psychology ( de Bruin and van Gog, 2012 ), while self-assessment research is ahead in terms of implementation (E. Panadero, personal communication, October 21, 2016). One major exception is the work done on Self-regulated Strategy Development ( Glaser and Brunstein, 2007 ; Harris et al., 2008 ), which has successfully integrated SRL research with classroom practices, including self-assessment, to teach writing to students with special needs.

Nicol and Macfarlane-Dick (2006) have been explicit about the potential for self-assessment practices to support self-regulated learning:

To develop systematically the learner's capacity for self-regulation, teachers need to create more structured opportunities for self-monitoring and the judging of progression to goals. Self-assessment tasks are an effective way of achieving this, as are activities that encourage reflection on learning progress (p. 207).

The studies of SRL in Table S1 (Supplementary Material) provide encouraging findings regarding the potential role of self-assessment in promoting achievement, self-regulated learning in general, and metacognition and study strategies related to task selection in particular. The studies also represent a solution to the “methodological and theoretical challenges involved in bringing metacognitive research to the real world, using meaningful learning materials” ( Koriat, 2012 , p. 296).

Future Directions for Research

I agree with ( Yan and Brown, 2017 ) statement that “from a pedagogical perspective, the benefits of self-assessment may come from active engagement in the learning process, rather than by being “veridical” or coinciding with reality, because students' reflection and metacognitive monitoring lead to improved learning” (p. 1,248). Future research should focus less on accuracy/consistency/veridicality, and more on the precise mechanisms of self-assessment ( Butler, 2018 ).

An important aspect of research on self-assessment that is not explicitly represented in Table S1 (Supplementary Material) is practice, or pedagogy: Under what conditions does self-assessment work best, and how are those conditions influenced by context? Fortunately, the studies listed in the table, as well as others (see especially Andrade and Valtcheva, 2009 ; Nielsen, 2014 ; Panadero et al., 2016a ), point toward an answer. But we still have questions about how best to scaffold effective formative self-assessment. One area of inquiry is about the characteristics of the task being assessed, and the standards or criteria used by learners during self-assessment.

Influence of Types of Tasks and Standards or Criteria

Type of task or competency assessed seems to matter (e.g., Dolosic, 2018 , Nguyen and Foster, 2018 ), as do the criteria ( Yilmaz, 2017 ), but we do not yet have a comprehensive understanding of how or why. There is some evidence that it is important that the criteria used to self-assess are concrete, task-specific ( Butler, 2018 ), and graduated. For example, Fastre et al. (2010) revealed an association between self-assessment according to task-specific criteria and task performance: In a quasi-experimental study of 39 novice vocational education students studying stoma care, they compared concrete, task-specific criteria (“performance-based criteria”) such as “Introduces herself to the patient” and “Consults the care file for details concerning the stoma” to vaguer, “competence-based criteria” such as “Shows interest, listens actively, shows empathy to the patient” and “Is discrete with sensitive topics.” The performance-based criteria group outperformed the competence-based group on tests of task performance, presumably because “performance-based criteria make it easier to distinguish levels of performance, enabling a step-by-step process of performance improvement” (p. 530).

This finding echoes the results of a study of self-regulated learning by Kitsantas and Zimmerman (2006) , who argued that “fine-grained standards can have two key benefits: They can enable learners to be more sensitive to small changes in skill and make more appropriate adaptations in learning strategies” (p. 203). In their study, 70 college students were taught how to throw darts at a target. The purpose of the study was to examine the role of graphing of self-recorded outcomes and self-evaluative standards in learning a motor skill. Students who were provided with graduated self-evaluative standards surpassed “those who were provided with absolute standards or no standards (control) in both motor skill and in motivational beliefs (i.e., self-efficacy, attributions, and self-satisfaction)” (p. 201). Kitsantas and Zimmerman hypothesized that setting high absolute standards would limit a learner's sensitivity to small improvements in functioning. This hypothesis was supported by the finding that students who set absolute standards reported significantly less awareness of learning progress (and hit the bull's-eye less often) than students who set graduated standards. “The correlation between the self-evaluation and dart-throwing outcomes measures was extraordinarily high ( r = 0.94)” (p. 210). Classroom-based research on specific, graduated self-assessment criteria would be informative.

Cognitive and Affective Mechanisms of Self-Assessment

There are many additional questions about pedagogy, such as the hoped-for investigation mentioned above of the relationship between accuracy in formative self-assessment, students' subsequent study behaviors, and their learning. There is also a need for research on how to help teachers give students a central role in their learning by creating space for self-assessment (e.g., see Hawe and Parr, 2014 ), and the complex power dynamics involved in doing so ( Tan, 2004 , 2009 ; Taras, 2008 ; Leach, 2012 ). However, there is an even more pressing need for investigations into the internal mechanisms experienced by students engaged in assessing their own learning. Angela Lui and I call this the next black box ( Lui, 2017 ).

Black and Wiliam (1998) used the term black box to emphasize the fact that what happened in most classrooms was largely unknown: all we knew was that some inputs (e.g., teachers, resources, standards, and requirements) were fed into the box, and that certain outputs (e.g., more knowledgeable and competent students, acceptable levels of achievement) would follow. But what, they asked, is happening inside, and what new inputs will produce better outputs? Black and Wiliam's review spawned a great deal of research on formative assessment, some but not all of which suggests a positive relationship with academic achievement ( Bennett, 2011 ; Kingston and Nash, 2011 ). To better understand why and how the use of formative assessment in general and self-assessment in particular is associated with improvements in academic achievement in some instances but not others, we need research that looks into the next black box: the cognitive and affective mechanisms of students who are engaged in assessment processes ( Lui, 2017 ).

The role of internal mechanisms has been discussed in theory but not yet fully tested. Crooks (1988) argued that the impact of assessment is influenced by students' interpretation of the tasks and results, and Butler and Winne (1995) theorized that both cognitive and affective processes play a role in determining how feedback is internalized and used to self-regulate learning. Other theoretical frameworks about the internal processes of receiving and responding to feedback have been developed (e.g., Nicol and Macfarlane-Dick, 2006 ; Draper, 2009 ; Andrade, 2013 ; Lipnevich et al., 2016 ). Yet, Shute (2008) noted in her review of the literature on formative feedback that “despite the plethora of research on the topic, the specific mechanisms relating feedback to learning are still mostly murky, with very few (if any) general conclusions” (p. 156). This area is ripe for research.

Self-assessment is the act of monitoring one's processes and products in order to make adjustments that deepen learning and enhance performance. Although it can be summative, the evidence presented in this review strongly suggests that self-assessment is most beneficial, in terms of both achievement and self-regulated learning, when it is used formatively and supported by training.

What is not yet clear is why and how self-assessment works. Those of you who like to investigate phenomena that are maddeningly difficult to measure will rejoice to hear that the cognitive and affective mechanisms of self-assessment are the next black box. Studies of the ways in which learners think and feel, the interactions between their thoughts and feelings and their context, and the implications for pedagogy will make major contributions to our field.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2019.00087/full#supplementary-material

1. ^ I am grateful to my graduate assistants, Joanna Weaver and Taja Young, for conducting the searches.

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Keywords: self-assessment, self-evaluation, self-grading, formative assessment, classroom assessment, self-regulated learning (SRL)

Citation: Andrade HL (2019) A Critical Review of Research on Student Self-Assessment. Front. Educ. 4:87. doi: 10.3389/feduc.2019.00087

Received: 27 April 2019; Accepted: 02 August 2019; Published: 27 August 2019.

Reviewed by:

Copyright © 2019 Andrade. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Heidi L. Andrade, handrade@albany.edu

This article is part of the Research Topic

Advances in Classroom Assessment Theory and Practice

• Student Self-Assessment

Student self-assessment occurs when learners assess their own performance. With practice, they learn to:

• objectively reflect on and critically evaluate their own progress and skill development
• identify gaps in their understanding and capabilities
• discern how to improve their performance
• learn independently and think critically.

Use self-assessment to develop the learning skills students will need for professional competence, and to make them aware of and more responsible for their own learning processes.

Sometimes teachers use self-assessment and peer assessment together. For example, they might require students to use a rubric to critique the work of their peers, and then to apply the same criteria to their own work. Nulty (n.d.) argues that students must first learn to peer assess if they are to self-assess effectively.

Skilled self-assessment can be as reliable as other forms of assessment, but teachers must provide students with training and practice if they want results to closely align with other assessors' results.

You can introduce students to the idea of self-assessment using:

• ongoing structured formative learning (for example, by using online quizzes that give students immediate feedback on their performance) or
• summative assessment (for example, requiring students to grade their own performance).

The literature suggests that self-assessment may be more useful as a formative learning tool than as a summative assessment.

Self-assessment benefits students by:

• helping them develop important meta-cognitive skills that contribute to a range of important graduate capabilities. All professionals must be able to evaluate their own performance; thus, this practice should be embedded in higher-education learning as early as possible.
• increasing their self-awareness through reflective practice, making the criteria for self-evaluation explicit, and making performance-improvement practices intrinsic to ongoing learning.
• contributing to the development of critical reviewing skills, enabling students to more objectively evaluate their own performance and, when used in conjunction with peer assessment, others' performance as well. With peer assessment, students become more practised in giving constructive feedback and receiving and acting on the feedback they receive.
• helping them take control of their own learning and assessment, and giving them the chance to manage their own learning and development more independently.
• giving them greater agency regarding assessment, thus enriching their learning.
• possibly, in the long run, reducing the teacher's assessment workload – although this benefit is not sufficient on its own to introduce student self-assessment.

Although studies have shown that most students are fairly capable self-assessors, introducing self-assessment can raise dilemmas and challenges. For example:

• Lower-performing and less experienced students tend to overestimate their achievements. As with peer assessment, students' ability to self-assess accurately must be developed over time, and with substantial guidance. It is definitely not initially a time-saving exercise for the teacher.
• Students may resist self-assessment, perceiving assessment and grading to be the teacher's job, or having no confidence in their ability to assess themselves.
• Issues can arise if students' self-assessments are not consistent with peer or staff assessments.

Designing self-assessment

Students often readily accept the use of self-assessment as part of a formative learning process. It satisfies their need for formal self-reflection on their progress, and gives them agency when they are planning their learning. It may also give them valuable experience in self-assessment that they can apply throughout the course.

Design self-assessment carefully, and ensure that you integrate its use into the assessment plan. This way you optimise the benefits to learning, appropriately engage students in the process by giving them clear directions and explanations and ensure that contingency plans are in place if issues arise.

Here are some factors to consider when including student self-assessment in your learning design:

• It is unreasonable to expect students to become experts in self-assessment after a single course.
• It is reasonable to expect that they will be capable self-assessors by the end of their undergraduate program.
• Consider students' different experience levels when designing tasks, and support the development of their self-assessment capabilities accordingly.
• Provide more guidance and facilitation for less experienced students.
• Make clear to students the rationale for self-assessment and its intended benefits to their learning, so that they do not misconstrue the strategy as indicating that the teacher is lazy.
• At first, you can provide predetermined assessment criteria for students to use in self-assessing their work. In some areas and at higher levels of study, these may be best determined by the teacher.
• Students may find it significantly more interesting and motivating if you involve them in developing the assessment criteria. This also encourages their autonomy and self-management as learners.
• Helping develop assessment criteria develops students' assessment literacy and promotes a shared understanding of tasks and assessment standards.
• Students can be capable assessors of their own and their peers' performance. Help them build their meta-awareness about this capability so that they can articulate and defend their critiques of their own work, and clarify what they can do to improve their performance.
• Providing an expert assessment of students' work allows them to cross-check their self-assessment, as does combining self-assessment with peer assessment.
• Use assessed examples of students' work to illustrate different levels of achievement. This will clarify the standards and show how criteria are applied.
• This is a complex decision. Self-assessment for grading may be more appropriate in high-level undergraduate or postgraduate courses, especially where class sizes are smaller.
• If you decide that self-assessment will contribute to the grade, precisely state to both students and assessors, at the outset, how much it will contribute.
• Introduce self-assessment for practice and familiarisation before you use it to contribute to grading. For example, have students attach a self-assessment report to their submitted work.
• Assessment of learning is intrinsically inexact and subjective. Use assessment rubrics, whether predetermined by the teacher or negotiated with students, to specify expected standards of performance against stated criteria.
• Shared use of a rubric by staff and students can prompt valuable conversations about assessment principles and quality standards.
• The more a student's self-assessment contributes to the grade, the greater will be the need for the teacher to moderate the grade with their own assessment. Remember, though, that "if tutors moderate student self-assessments with anything other than a light touch, students do not put their hearts into being objective in their self-assessment" ( Race, 2001, p. 14 ). But if self-assessment results are not moderated, the fairness of the process will be questionable, no matter how capable the students may be as self-assessors. A moderation process can simply consist of comparing the tutor's and/or peer's grade and the student's self-assessed grade. Where they are very different, you can discuss the discrepancy with the student, with an eye to possibly reviewing the grade. Such processes are more difficult to manage in very large classes.

Practical methods

Reflective journal.

Having students produce a reflective journal about their own learning and achievements is a logical way to engage them in self-assessment, as it gives both them and their assessors insights into their learning.

Extend the reflective-journal task to include their thoughts on how they can improve their performance.

You can assess the reflective journal, or the students can reflect on their reflections, or assess their peers' journals and give feedback.

One version of this type of assessment task is the "self-assessment schedule" (Boud, 1992), a formal document prepared by the student that presents their achievements alongside their learning goals and comments on what they feel they have achieved.

Self-assessment prompts for students

You can incorporate self-assessment into almost any assessment task, either when or after students submit their assignments.

Race (2001) suggests structuring the self-assessment by prompting students;  for example, by asking them:

• What do you think is a fair grade for the work you have handed in, and why do you think so?
• What did you do best in this assessment task?
• What did you do least well in this assessment task?
• What did you find was the hardest part?
• What was the most important thing you learned in doing this assessment task?
• If you had more time to complete the task, what (if anything) would you change, and why?

Self-assessment in group work

Self-assessment can focus on aspects of a task that only the student can comment on, such as their contribution to teamwork and the collaborative production of a group's outputs. When students are allowed to do this, they see it as reducing the risk of being judged unfairly (Nulty, n.d.).

Self-assessment of class participation

Assessing students' participation in class discussions and activities is often seen as an overly subjective process. If students can see that you value their perceptions of the quality of their own and their peers' contributions, they are likely to become more active in the classroom. Combining student self- and peer assessment with tutor assessment makes for a more reliable grade (Dancer & Kamvounias, 2005).

Using technology

Use online tools such as journals or blogs to manage self-assessment based on reflective activities. You might, for example, require students to publish regular reflections in response to question prompts. Both you and they can then assess their learning process. You can set up a private journal for this purpose, or a blog that can be shared with other students (or made public) and comments invited.

For more objective tasks, such as scientific or mathematical calculation, you can provide automatically marked online tests. Invite students to create questions to contribute to the test database; this adds a meta-cognitive layer to the exercise. Online tools such as Peerwise are being developed for this purpose, but the Moodle Learning Management System allows the compilation of question banks , and self-assessment can be incorporated into a workshop activity.

Boud, D. (1992). The use of self-assessment schedules in negotiated learning. Studies in Higher Education ,17(2), 185-201.

Brew, C., Riley, P. & Walta, C. (2009). Education students and their teachers: Comparing views on participative assessment practices. Assessment and Evaluation in Higher Education, 36(4), 641-657.

Dancer, D. & Kamvounias, P. (2005). Student involvement in assessment: a project to assess class participation fairly and reliably. Assessment and Evaluation in Higher Education , 30(4), 445-454.

Kearney, S. (2019). Transforming the first-year experience through self and peer assessment . Journal of University Teaching & Learning Practice , 16(5). https://doi.org/10.53761/1.16.5.3

McDonald, B. & Boud, D. (2003). The impact of self-assessment on achievement: The effects of self-assessment training on performance in external examinations. Assessment in Education , 10(2), 209-220.

Nulty, D.D. (n.d.). A guide to peer and self assessment: approaches and practice strategies for academics. Griffith Institute for Higher Education, Griffith University.

Nulty, D.D. (2010). Peer and self assessment in the first year of university. Assessment and Evaluation in Higher Education, 1469–297X.

Panadero, E., Pérez, D.G., Ruiz, J.F., Fraile J., Sánchez-Iglesias, I. & Brown, G. T. L. (2022). University students’ strategies and criteria during self-assessment: instructor’s feedback, rubrics, and year level effects . European Journal of Psychology of Education (October). https://doi.org/10.1007/s10212-022-00639-4

Race, P. (2001). A Briefing on Self, Peer and Group Assessment . Assessment Series No. 9., LTSN Generic Centre.

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Self-Report is Indispensable to Assess Students’ Learning

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Self-report is required to assess mental states in nuanced ways. By implication, self-report is indispensable to capture the psychological processes driving human learning, such as learners’ emotions, motivation, strategy use, and metacognition. As shown in the contributions to this special issue, self-report related to learning shows convergent and predictive validity, and there are ways to further strengthen its power. However, self-report is limited to assess conscious contents, lacks temporal resolution, and is subject to response sets and memory biases. As such, it needs to be complemented by alternative measures. Future research on self-report should consider not only closed-response quantitative measures but also alternative self-report methodologies, make use of within-person analysis, and investigate the impact of respondents’ emotions on processes and outcomes of self-report assessments.    

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What Is a Teacher Self-Assessment? Tools, Types and Benefits

The number of public school teachers has grown in the past decade, the National Center for Education Statistics reports: A comparison between the 2011-12 and 2020-21 academic years reveals an 11 percent increase. However, according to the Government Accountability Office (GAO), there are teacher shortages in approximately three-quarters of U.S. states. During the pandemic, between 2019 and 2021, 7 percent of public school teachers decided to leave the profession, further exacerbating the challenge, the GAO reports.

Despite the many difficulties they face, teachers remain dedicated to providing the best education possible to their students. As teachers seek to nurture their students’ curiosity and creativity, teacher self-assessments emerge as a valuable practice that can benefit everyone. These reflective exercises, combined with pursuing an advanced education in inclusive instruction, can empower educators to gauge their effectiveness in the classroom.

What Is a Teacher Self-Assessment?

A teacher self-assessment is a tool that a teacher can use to measure their teaching performance against their goals. The process often involves answering questions about their teaching methods and what impact they have on student learning.

Questions may focus on key elements of teaching practice, such as:

• Am I creating a conducive learning environment?
• How is my teaching practice facilitating student-centered learning?
• What enrichment activities am I offering? Are they encouraging open discussions?

A teacher self-assessment may involve collecting and analyzing relevant evidence, for example, student work samples or questionnaire responses.

Since objectivity is key in self-assessments, teachers use specific criteria to assess the effectiveness of their teaching practice. Another key is to be straightforward and honest when answering questions. Self-assessments are best conducted in a setting conducive to reflection, whether that’s at home, in a classroom after school hours, or in a quiet space of choice.

Different Types of Teacher Self-Assessments

A teacher self-assessment can be approached in any of a number of different ways. A common practice is for teachers to observe themselves through video recordings. This approach can help them rate their own effectiveness in delivering instruction, engaging students and managing the classroom.

In another example, teachers compile a portfolio containing lesson plans and sample materials. Teachers could include a collection of work that showcases their growth, reflections, achievements and evidence of development. Teachers can then refer to these materials when they set goals for the future as well.

Teacher self-assessments can also include activities such as journaling. Teachers can record their experiences throughout a school year — both good and bad — and then review their notes at the end of the year. This approach can provide insights into what was effective and what could be improved upon.

A teacher’s self-assessment can include observation from peers as well. In this approach, teachers ask their colleagues, typically more experienced teachers, to observe them in their classroom. This can lead to constructive feedback and new ideas that can help teachers improve their teaching practice and their students’ educational outcomes.

These self-assessment approaches can empower teachers to gain insights, identify growth areas and improve their overall effectiveness.

The Benefits of Self-Assessment

Teacher self-assessments provide a systematic approach to building new skills, which can help teachers become more competitive and position themselves for better pay. Of course, many criteria influence teacher salaries, and there are variances in teacher salaries by state as well.

By engaging in a self-assessment, teachers can gain insights into their teaching methods and strategies, which can enable them to identify areas for improvement and make necessary adjustments to improve student outcomes.

Teacher self-assessments offer numerous other benefits for educators as well, such as the following:

• They can uncover challenges and skills teachers may have overlooked or not fully recognized. A deep exploration of their teaching practice can illuminate areas for further development.
• They can help teachers identify pressing problems. For example, teachers might recognize the need to prioritize certain important issues for students, leading them to work with an instructional coordinator to address specific concerns through targeted solutions and improvements.
• They can allow teachers to delve into specific aspects of their teaching practice. Teachers can move beyond the jargon, such as “good performance,” to come up with concrete evaluations of their instructional performance.

Another benefit of teacher self-assessments is that they allow teachers to make informed decisions, implement effective strategies and be honest with themselves about what’s working in the classroom. Making an effort to conduct self-assessments also demonstrates a teacher’s willingness to improve, their commitment to pursuing teaching excellence and their desire to achieve professional growth.

Teacher Self-Assessment vs. Traditional Evaluations: What’s Different?

Teacher self-assessments and traditional evaluations may be different, but they both can play an important role in teachers’ professional development. Self-assessment is a personalized, reflective approach. The source of the evaluation is the teacher themselves. In other words, teachers consider their own teaching practice to judge their strengths and weaknesses and identify areas for improvement.

Self-assessments are also voluntary and tend to be informal. Teachers may choose to ask their peers to observe them in class for the purpose of self-improvement.

On the other hand, traditional evaluations are based on the perspectives of school administrators or teachers’ supervisors. Unlike a self-assessment, a traditional evaluation is a formal process that relies on standardized criteria, accountability measures and observation protocols defined by a school district.

Teacher self-assessments empower teachers to practice autonomy in setting goals and creating action plans for improvement. In contrast, traditional evaluations are typically a requirement imposed by a supervisor or external authority such as a school district administrator. This approach may limit a teacher’s ability to shape the evaluation process.

Combining these two approaches can provide a comprehensive and well-rounded evaluation of a teacher’s performance and professional growth.

Self-Assessment Tools

Teachers can use any of various tools for their self-assessments, including self-reflection, surveys and questionnaires. For instance, they can use rubrics containing checklists that outline teaching criteria. Before the start of the school year, the teacher develops a checklist to measure areas such as planning, lesson content, classroom organization, instruction delivery, student engagement and classroom management. Throughout the year, they rate themselves and identify areas for improvement.

Teachers can also use readily available self-assessment tools that offer structured frameworks. These tools pose questions that cover aspects of teaching such as subject matter knowledge, planning skills and effectiveness in delivering instruction. Teachers assess themselves and receive personalized feedback. For example, the American Institutes for Research offers teachers a self-assessment tool to help them reflect on their teaching practices that support social and emotional learning for students.

Another teacher self-assessment method is collaboration with others. As such, a teacher can ask their students about the students’ perception of what’s being taught, the classroom environment, their level of engagement and their level of satisfaction. Students can share their feedback through a survey or questionnaire the teacher provides.

Advance Your Teaching Career

Teacher self-assessments can unlock teachers’ potential to advance in their careers. They can help teachers overcome challenges and harness their strengths. They are a powerful tool that can empower lifelong learning, enabling teachers to adapt to challenges, stay ahead of the curve and embrace the latest teaching practices.

Teachers with a growth mindset and a commitment to continuous improvement can further enhance their careers by advancing their education. Augusta University Online’s Master of Education in Instruction program offers a curriculum focused on classroom management, pedagogical theory, assessment analysis and curriculum design. The program focuses on preparing teachers with the skills and knowledge they need to foster inclusive and student-focused learning environments.

Learn more about how the program can help you reach your professional goals as a teacher.

Recommended Readings What Can You Do With a Master of Education?

Sources: Abeka, Teacher Self-Evaluation American Institutes for Research, Self-Assessing Social and Emotional Instruction and Competencies: A Tool for Teachers Center on Great Teachers and Leaders, Teacher Leadership: Self-Assessment and Readiness Tools Education Week , “The Status of the Teaching Profession Is at a 50-Year Low. What Can We Do About It?” EF, “Why This One Habit Can Transform Your Teaching” National Center for Education Statistics, Characteristics of Public School Teachers National Education Association, NEA Teacher Evaluation and Accountability Toolkit SafetyCulture, “Teacher Evaluation Methods for Effective Quality Teaching” U.S. Government Accountability Office, “Pandemic Learning: Less Academic Progress Overall, Student and Teacher Strain, and Implications for the Future”

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• Professional learning

Teach. Learn. Grow.

Teach. learn. grow. the education blog.

The importance of student self-assessment

Want to know a secret? I didn’t mean to become a secondary ELA teacher. One of the reasons why an ELA endorsement wasn’t originally at the top of my list was this persistent worry: How does one keep up with marking and grading all those ELA assignments? I do love a good challenge, though, so despite my concerns, I jumped into the adventure, and I am so glad that I did.

Over time, I faced that ELA assignment volume fear. Through support from mentors, observations of experts, and practice in professional development settings, I learned how to embed student self-assessment into learning processes so that my workload concerns were alleviated. More importantly, my students were able to build metacognition and self-efficacy skills .

I don’t want to give the impression that learning how to embed self-assessment processes was smooth or linear, or that the process is complete for me. There was trial and error, zigs and zags, and I am still learning how to improve my practice. I hope that sharing the things I’ve learned along the way can bring relief and support you in your work.

The topic of student self-assessment is huge, so for the purposes of this blog post, I will focus on three things that I’ve learned along the way. If you want to know more about the components and benefits of self-assessment, check out this short Dylan Wiliam video . I also encourage you to read Heidi Andrade’s “A critical review of research on student self-assessment.” She asks and answers several key questions about self-assessment including, “What is self-assessment?” and “Why self-assess?” She also digs into how it relates to feedback.

1. Reflect on your role

When I first started teaching, I was prepared to operate as a learner-manager instead of a learner-empowerer. As a learner-manager ELA teacher, I would give directions for an essay, set a due date, collect essays, take hours to mark and grade the essays, and then hand back the papers. Inevitably I would be frustrated when students ignored my marks or tossed out the paper, only to ask how to raise their grade at the end of the quarter. Ugh! Wasn’t this precisely why I hadn’t planned on being an ELA teacher?

Authentic, meaningful, and effective student self-assessment requires participants to be honest and vulnerable.

Slowly I came to understand how important it was for me to make the shift from thinking of myself as a learner-manager (an “I say it. You do it” approach) to thinking of myself as a learner-empowerer (a “How do I partner with my students to build knowledge, skills, and self-efficacy throughout the learning journey?” approach). (For more about learner-manager versus learner-empowerer as well as information about the connections to equity and a trauma-informed practice, see my post “6 ways to help heal toxic stress, trauma, and inequity in your virtual or in-person classroom” .)

Once I committed to being a learner-empowerer, my actions followed suit. I engineered learning goal paths that made students active agents in the learning processes. For example, I built in small, quick opportunities to practice self-assessing along the way so that by the time we arrived at an end point, students’ work was solid and they could reflect on the goal or explain a grade. More on that in the next tip.

2. Use reliable strategies, processes, and tools

Here are four “moves”—examples of specific actions I learned to embed in learning processes—that I think can help you as you consider the role of self-assessment in your classroom.

Nurture a community of learning

Authentic, meaningful, and effective student self-assessment requires participants to be honest and vulnerable. I had to deliberately foster a safe, respectful, and inclusive learning environment. In my blog post “5 little things that are really big” I explain specific ways to partner with students to make this happen. My colleague Cara Holt outlines 10 useful community-building strategies in her blog post, and another colleague, Vicki McCoy, explores self-assessment and metacognition in “How formative assessment boosts metacognition—and learning.”

Reallocate time and energy

Early in my teaching career, I was hesitant to fully dive into processes for successful student self-assessment. It felt like the learning experiences in a lesson or unit would take longer if I did because of the need to make room for the exercises that make self-assessment fruitful, such as clarifying goals, using examples, and engaging in peer feedback. I had to trust that reworking how I used teaching and learning time would pay off in the end, and it did.

By making time in the lesson or unit for the short, frequent exercises that make self-assessment successful, I spent less time tracking down unfinished work and nagging students about revisions. I also ultimately saved time grading. Once my students and I got the hang of self-assessment processes, students could reflect on the learning goals and articulate their grade rationale—and they were usually right on! No more frustration of unread markups and ignored grades.

Use examples of work

Self-assessment is even more fruitful when students can process examples of work that illustrate the learning goals and success criteria. In other words, for meaningful self-assessment, we had to work to make sure there was a solid foundation of understanding about examples and how to use the examples as a guide. After creating that solid understanding of success through examples, my students and I could take next steps with effective self, peer, and teacher feedback, which ultimately led to successful self-assessment.

I learned to start with strong examples so that students were sure to have a sound reference for what the end result should look like. Sometimes I could access these examples from the provided curriculum materials or from previous students; sometimes I made the examples myself, especially if the learning goals or path to the learning goals were specific to my students’ motivations and interests.

If my students were working on a learning goal (such as building argumentation claims and counterclaims) expressed in a multi-step product (such as a multiple paragraph argument), we would process a whole example. We would also examine specific pieces (e.g., paragraphs or even sentences). The students and I would look for the success criteria together using processes that aligned to the learning goal(s). For instance, if we were working on argumentation learning goals, we would use an argument rubric to guide our processing of the example, usually a few parts of the rubric at a time. This practice set forth the words and procedures for effective teacher, self, and peer feedback grounded in concrete illustrations of the learning goals and success criteria. With the provided examples, we could practice a feedback strategy such as Stars (strengths) and Stairs (next steps): Using the language of the rubric, what is a strength of this argumentation example? Using the language of the rubric, what is a next step for this argumentation example? Students had plenty of practice with the strategy first for processing examples, then for practicing feedback, and finally as a frame for self-assessment: Using the language of the rubric, what is a strength of your argumentation? Using the language of the rubric, what is a next step for your argumentation?

Figuring out the self-assessment strategies, processes, and tools that work best in partnership with your students is an ongoing expedition that requires time, patience, and a sense of humor.

Once a solid understanding of the end result and its pieces are established through sound examples, it can be fun to process silly non-examples with students. For example, one of my favorite silly non-examples to use when practicing reading and writing for information is a YouTube video of a dad following his children’s written instructions for making a peanut butter and jelly sandwich . You can even ask students to help you make those silly non-examples, which is another way for students to both become active partners in learning and internalize what does and does not illustrate the learning goal(s) and success criteria. Making time for processing examples and non-examples equips students with a clear picture of the end result and the frames for self-assessment success.

Include self-assessment prompts during the journey and at the end

For far too long, I would tell students to self-assess and hope that they followed through. Eventually I made it a habit to embed self-assessment prompts, space, and time directly on formative (practice) and summative materials.

When relevant, I would simply embed a one-sentence self-assessment frame (e.g., On a scale of 1–5, my claim sentence is currently a ___ because ___.) Other times it was better to prompt more than a one-sentence self-assessment (e.g., To self-assess your use of textual evidence to support your claim, please follow these steps: 1. In your draft, highlight where you used textual evidence to support your claim. 2. Based on the textual evidence that you used, circle your current level of skill on the provided rubric. 3. Use the information on the provided rubric to list one action you take to make your textual evidence even stronger.) The students and I would use their self-assessment answers to plan next steps, which sometimes looked like adjusting the lesson plan for the next day for more practice or making mixed groups for the next formative exercise.

For the summative task(s), I got into the habit of making sure to include student self-assessment as the last part of the experience. For instance, if there was a written or spoken product, after the conclusion sentence students would also self-assess using a provided frame. If the summative was a set of prompts or questions, the last prompt or question would be self-assessment.

3. Embrace the process

Figuring out the self-assessment strategies, processes, and tools that work best in partnership with your students is an ongoing expedition that requires time, patience, and a sense of humor. You’ll take steps forward, steps sideways, and steps back. It can get messy, but that’s normal. Authentic, human-centered learning is messy!

I encourage you to try one new thing at a time, celebrate quick wins, think of “failures” as learning opportunities, and lean on your students for their help. Be reassured that applying self-assessment practices is one of the most valuable parts of the learning process. For more on the value of student self-assessment, see the discussion section in “Examining the impact of self-assessment with the use of rubrics on primary school students’ performance.”

Suggested next steps

I encourage you to continue the journey of including students as active agents in the learning process. Growing in or expanding upon the practices listed here can help you continue that journey. In case you find them helpful, here are a few discussion questions that can guide your thinking about student self-assessment. Tackle them on your own or with a colleague.

Questions for teachers

• What’s one student self-assessment strategy that you already use?
• What’s one student self-assessment strategy that you would like to try?
• What support do you need to try a new self-assessment strategy?
• What will inspire you to keep up the hard work of embedding student self-assessment in the learning journey?

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The Function of Self-Assessment and Self-Evaluation in the Quality Cycle

Table of Contents

The Self-Assessment Review (SAR) serves as a critical tool for academic institutions, providing an annual reflection on teaching standards against internal and Ofsted benchmarks. This document delves into the SAR process, emphasising its role in streamlining feedback and enhancing course management and accountability. At its core, SAR ensures that individual course assessments flow upward, from strand to department and finally to the broader teaching establishment, prior to being ratified by governing bodies. This hierarchical feedback system empowers course leads to take ownership, driving improvement while ensuring their accountability.

Introduction

A Self-Assessment Review (SAR) is a review of what the teaching establishment, department or strand has carried out over the last year, including an evaluation against the teaching establishments own internal criteria and Ofsted requirements. When completing a SAR, it is best to “imagine that you were sitting down explaining how and what you did to a stranger not familiar with your work, including judgements on how well you do it” (Hatton, 2016).

The SAR Process

Quality arrangements of own organisation learning programme.

I have selected the BSc in Applied Computing for evaluation as this is the learning programme on which I mostly teach. The predicted achievement is 90% which is significantly higher than another local teaching organisation, the only other provider of this learning programme. At the end of the course, the predicted achievement was accurate as one student suspended study for a year and did not complete the course this year, resulting in the actual achievement being 90%. Overall good outcomes with the majority of students going to work in industry or further study in the form of a master’s degree, this could still be improved further. Linked closely with the predicted/actual achievement is the level of retention being 100% as all students either completed the course or suspended until next year. The learner voice is positive with 100% of students rating 4 or above out of 5 for “overall, I am satisfied with the quality of the course…”, another prominent rating from students is 75% of students gave a score of 5 out of 5 for “staff are good at explaining things”. The vast majority of the feedback is very positive, some of the less positive feedback is 50% of students rated “feedback on my work has been timely” as average. The student attendance on the course is poor, with an average percentage of 72%, this is lower than the teaching establishments minimum requirement for attendance, however, can be expected as the learners are adults.

Areas for Improvement in my Learning Programme

Feedback delivery speed.

The second area that requires addressing is attendance; this can be improved with greater levels of communication during induction relating to the attendance expectations. This should be followed up throughout the year with warnings for those students who dip below the required 90% percent attendance. If the attendance doesn’t continue it should be explained to students that disciplinary action will follow, this will have an impact on the majority of students and result in the attendance figure being higher. Another aspect to consider is to investigate why the attendance is so low, the BSc requires a large amount of independent research that many students complete at a local university library, currently the teaching establishment has no system in place to record when students have conducted research outside the class, or even in its own library. A simple email from students followed by a check of the library will likely have an impact on attendance figures.

Bibliography

Further reading, understanding and using educational theories by karl aubrey and alison riley., the use of data in school counseling: hatching results for students, programs, and the profession by trish hatch., assessment clear and simple: a practical guide for institutions, departments, and general education by barbara e. walvoord..

This book provides a concise and step-by-step guide to the process of assessment. It delves into the process of determining if students are learning what educators think they should learn and offers a clear way to integrate assessment into program review, accreditation, and setting strategic goals.

How to Create and Use Rubrics for Formative Assessment and Grading by Susan M. Brookhart.

Educause review, chronicle of higher education.

This is one of the most prominent publications in the field of higher education. It covers a broad range of topics including academic reviews, program assessments, and educational innovations.

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Wilson College Online Blog

Benefits and examples of student self-assessments, written by: wilson college   •  jan 18, 2024.

Benefits and Examples of Student Self-Assessments ¶

Studies suggest that student self-assessments can help students perform better. A study published in the online journal Assessment & Evaluation in Higher Education found that when students evaluate themselves and receive feedback, their grades improve. Feedback literacy means being able to understand and use feedback to improve learning.

Proper student self-assessment goes beyond simple self-rating or guesswork. It involves students evaluating their own work against specific criteria. Then, they can reflect on and judge their performance based on objective and select benchmarks.

The application of self-assessment can vary. Examples of student self-assessment range from using learning logs and reflective journals to rubrics self-assessments. Teachers need to understand how student self-assessment helps students learn so they can properly guide them and advance their educational development. 

What Is Student Self-Assessment? ¶

Student self-assessment is an approach that students can apply to gauge how they’re doing in school. This can be accomplished by checking their own work, performance, behavior, as well as understanding what they’ve learned and what they still need to learn. This encourages students to take ownership of their learning and fosters accountability. Examples of student self-assessment activities can include establishing goals, identifying strengths and weaknesses, and setting plans for improvement.

Effective self-assessment means consistently showing students how to think about their own learning through the entire learning process. Insights gained from the self-assessment process can help improve their achievement. Teachers play a critical role in regularly making the process clear to students and removing obstacles for productive self-assessments.

Why Is Student Self-Assessment Important? ¶

Student self-assessment is important for several reasons. It encourages independent learning, enhances learning outcomes, and can potentially reduce teachers’ workloads. It also helps students understand what they are learning and the reasons why they are learning about a particular subject. This understanding is a key to educational growth and helps provide a clear path to follow in their ongoing growth journey. 

Student-self assessment also promotes metacognition, which means understanding one’s own thought processes. This helps improve cognitive functions, including problem solving, learning, and decision making.

What Are the Benefits of Student Self-Assessment? ¶

Student self-assessment boosts motivation and engagement by empowering students to take control of their learning. Active involvement in assessing their progress increases their investment in education. 

Numerous studies highlight the positive impact of student self-assessment. For instance, a study in the International Journal of Educational Research revealed marked enhancement in writing skills among students who use rubrics compared to those who did not.

Additional examples of student self-assessment benefits include the following.

• Students can evaluate knowledge and learning processes.
• Critical thinking and problem-solving skills can be improved.
• It champions a growth mindset for viewing challenges as opportunities.

Student self-assessment not only benefits students themselves, it provides valuable feedback to educators. This helps teachers gain insights into students’ understanding, strengths, and weaknesses. It also promotes a supportive and inclusive learning environment, as students feel that their perspectives and voices are valued.

7 Examples of Student Self-Assessments ¶

For teachers, discovering effective self-assessment strategies, processes, and tools in collaboration with students is an ongoing journey that demands time and patience. Here are seven examples of student self-assessments.

1. Learning Log ¶

A learning log serves as a personal journal for students to record their thoughts, questions, and experiences on their educational journey. It encourages deep self-awareness, helping students track their growth, identify strengths and weaknesses, and gain insights into their learning patterns. By updating their learning log regularly, students take ownership of their learning, fostering a deeper understanding of their academic development and promoting accountability.

2. Reflective Journals ¶

Reflective journals prompt students to write about their learning experiences, challenges, and achievements, including personal reflections, emotions, and opinions. Such journals encourage deep reflection and enhance self-awareness. Reflective logs, however, differ from learning logs by being more open-ended and less focused on objective data. Journaling in an online format can also be beneficial. It can open versatile reflective tools for students in the form of documents, videos, or audio, fostering richer learning experiences and improved outcomes.

3. Goal Setting ¶

Students can set specific goals that are measurable, achievable, relevant, and timebound (SMART) within their learning logs or reflective journals. Examples of student self-assessment SMART goals can include improving academic performance—more specifically, a student can say: “I will increase my grade from a B+ to an A next marking period.” 

Another example of goal setting can include working to increase class participation—for example, establishing a promise to participate in school at least 3 times every day. This approach provides students with clear roadmaps for their learning journey and fosters a sense of accountability.

4. Rubric Self-Assessment ¶

Rubrics can aid in the self-assessment process. This helps students identify areas for improvement and take ownership of their learning process, providing a clear framework to evaluate their own work and gain an understanding of high-quality performance. 

Commonly used rubric types in schools include holistic rubrics (an overall performance assessment) and analytic rubrics (a breakdown of performance into separate elements). Another type, developmental rubrics, measure growth along a proficiency scale from novice to expert. Assessment rubrics help students proactively self-assess their work against predefined criteria.

5. Questionnaires or Surveys ¶

Students can regularly fill out self-assessment questionnaires or surveys to reflect on their learning experiences, as well as identify strengths, weaknesses, and improvement opportunities. Examples of student self-assessment questions can include: What did you learn today? Did you try your best on your assignment? Answers to these types of questions can help students set action plans for continuous improvement.

6. Self-Reflection Worksheets ¶

Self-reflection worksheets enable students to reflect on the breadth of their learning experiences, identify areas for improvement, and develop action plans to address those areas. Including action plans in these worksheets empowers students to take tangible steps toward enhancing their abilities, turning self-awareness into a catalyst for growth and development. 

7. Exit Tickets ¶

Exit tickets are brief assessments or reflections completed by students at the end of a lesson or class session. They help educators gauge student comprehension, identify areas that need further attention, and tailor future lessons accordingly. Exit tickets also encourage students to reflect on their learning and provide valuable feedback to the teacher.

Become A Transformative Educator ¶

Despite the noted benefits of student self-assessment, putting forward new strategies into practice can come with challenges. If you are looking to incorporate teaching strategies to better connect with students and help improve learning outcomes, an advanced degree in education can prepare you with essential skills to become a transformative educator.

Wilson College Online offers a Master of Education (MEd) degree , covering subjects such as differentiated instruction, best practices, and technology integration. This program hones teaching skills through both research and classroom practices. It also offers educators the flexibility to continue teaching in their current district, thanks to the program’s asynchronous self-paced model.

Discover how the Wilson College Online Master of Education degree can empower you to further impact the lives of students.

Recommended Readings

Pros and Cons of Education Apps for Toddlers

Special Education Teacher Salary and Career Overview

Tips for Building an Engaging Special Education Classroom

ASCD, “How to Provide Better Feedback Through Rubrics”

Assessment & Evaluation in Higher Education, “Self-assessment is About More Than Self: The Enabling Role of Feedback Literacy”

EducationWeek, “Rubric Do’s & Don’ts”

Edutopia, “Teaching Students to Assess Their Learning”

E-Learning Heroes, “Using Learning Journals in E-Learning #344”

International Journal of Educational Research Open, “Examining the Impact of Self-assessment with the Use of Rubrics on Primary School Students’ Performance”

Kami, “How to Use SMART Goals for Your Students”

NWEA, “2 Types of Student Goal Setting that Empower Early Learners”

NWEA, “Proof That Student Self-Assessment Moves Learning Forward”

ProProfs, “Self-Assessment for Students: The Ultimate Guide”

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2017 ASEE Annual Conference & Exposition

Self-assessment to improve learning and evaluation, presented at tips and tricks for assessing student performance.

Self-assessment is a powerful mechanism for enhancing learning. It encourages students to reflect on how their own work meets the goals set for learning concepts and skills. It promotes metacognition about what is being learned, and effective practices for learning. It encourages students to think about how a particular assignment or course fits into the context of their education. It imparts reflective skills that will be useful on the job or in academic research.

Most other kinds of assessment place the student in a passive role. The student simply receives feedback from the instructor or TA. Self-assessment, by contrast, forces students to become autonomous learners, to think about how what they should be learning. Having learned self-assessment skills, students can continue to apply them in their career and in other contexts throughout life.

While self-assessment cannot reliably be used as a standalone grading mechanism, it can be combined with other kinds of assessment to provide richer feedback and promote more student “buy-in” for the grading process. For example, an instructor might have students self-assess their work based on a rubric, and assign a score. The instructor might agree to use these self-assigned grades when they are “close enough” to the grade the instructor would have assigned, but to use instructor-assigned grades when the self-grades are not within tolerance.

Self-assessment can also be combined with peer assessment to reward students whose judgment of their own work agrees with their peers’. In Calibrated Peer Review, students are asked to rate the work of three of their peers, and then to rate their own work on the same scale. Only after they complete all of these ratings are they allowed to see others’ assessments of their own work. CPR assignments are often configured to award points to students whose self-ratings agree with peers’ ratings of their work. The Coursera MOOC platform employs a similar strategy. Recently a “calibrated self-assessment” strategy has been proposed, that uses self-assigned scores as the sole grading mechanism for most work, subject to spot-checks by the instructor. Self-assigned grades are trusted for those students whose spot-checked grades are shown to be valid; students whose self-assigned grades are incorrect are assigned a penalty based on the degree of misgrading of their work.

In self-assessment, as in other kinds of assessment, a good rubric is essential to a good review process. It will include detailed criteria, to draw students’ attention to important aspects of the work. The criteria should mention the goals and keywords of the assignment, so that students will focus on their goals in assessment as well as their writing.

This paper will cover the benefits of self-assessment, and then provide several examples of how it can be combined with other assessments.

Dr. Gehringer is a professor in the Departments of Computer Science, and Electrical & Computer Engineering. His research interests include data mining to improve software-engineering practice, and improving assessment through machine learning and natural language processing.

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

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University students’ strategies and criteria during self-assessment: instructor’s feedback, rubrics, and year level effects

• Open access
• Published: 24 October 2022
• Volume 38 , pages 1031–1051, ( 2023 )

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• Ernesto Panadero 1 , 2 ,
• Daniel García Pérez 3 ,
• Javier Fernández Ruiz   ORCID: orcid.org/0000-0001-5419-7687 4 ,
• Juan Fraile 5 ,
• Iván Sánchez-Iglesias 6 &
• Gavin T. L. Brown 7  

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This study explores the effects of feedback type, feedback occasion, and year level on student self-assessments in higher education. In total, 126 university students participated in this randomized experiment under three experimental conditions (i.e., rubric feedback, instructor’s written feedback, and rubric feedback plus instructor’s written feedback). Participants, after random assignment to feedback condition, were video-recorded performing a self-assessment on a writing task both before and after receiving feedback. The quality of self-assessment strategies decreased after feedback of all kinds, but the number of strategies increased for the combined feedback condition. The number of self-assessment criteria increased for rubric and combined conditions, while feedback helped shift criteria use from basic to advanced criteria. Student year level was not systematically related to changes in self-assessment after feedback. In general, the combination of rubric and instructor’s feedback produced the best effects.

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Self-assessment of learning is linked to greater self-regulation (Andrade, 2018 ; Yan, 2019 ) and achievement (Brown & Harris, 2013 ). Furthermore, the ability to evaluate one’s own work and processes is an important objective of higher education (Tai et al., 2017 ). However, our understanding of how students integrate feedback within their self-assessment processes is limited (Panadero et al., 2016 ), though we have a considerable knowledge on how feedback concerning task, process, and self-regulatory processes has been shown to improve educational outcomes (Butler & Winne, 1995 ; Hattie & Timperley, 2007 ). In one of the few studies exploring self-assessment and external feedback, (Yan & Brown, 2017 ) showed in an interview study with teacher education students that students claim to seek external feedback to form a self-assessment. Hence, it is important to understand how to support the development of realistic and sophisticated self-assessment. A successful formative assessment practice has been the introduction of rubrics or scoring guides into classroom practice (Brookhart & Chen, 2015 ). Hence, it was expected that students would describe more complex self-assessment processes when provided feedback based on a rubric.

In a randomized experiment with university students, this study systematically extends our understanding of the role feedback plays on self-assessment by manipulating the type of feedback, its timing, and the expertise level of tertiary students. The study extends our understanding of the self-assessment “black box” by examining the strategies and criteria students used. Hence, this study provides new insights into how we can support robust self-assessment.

• Self-assessment

Self-assessment “involves a wide variety of mechanisms and techniques through which students describe (i.e., assess) and possibly assign merit or worth to (i.e., evaluate) the qualities of their own learning processes and products” (Panadero et al., 2016  p. 804). This definition indicates that self-assessment can take different shapes, from self-grading (e.g., Falchikov & Boud, 1989 ) to formative approaches (e.g., Andrade, 2018 ). However, what exactly happens when students self-assess is still largely mysterious.

Yan and Brown ( 2017 ) interviewed 17 undergraduate students from a teacher education institute using six general learning scenarios (e.g., How good are you at learning a new physical skill ?) and five questions specific to self-assessment (e.g., What criteria did you use to conduct self-assessment ?). From that data, the authors built a schematic cyclical self-assessment process consisting of three subprocesses: (1) determining performance criteria, (2) self-directed feedback seeking, and (3) self-reflection. Despite being an early effort to unpack the black box, the results are limited by a small sample and highly descriptive and interpretive analysis of interview data.

More recently, Panadero et al. ( 2020 ) analyzed the behavior of 64 secondary education students when self-assessing Spanish and mathematics tasks. Multi-method data sources (i.e., think aloud protocols, direct observation and self-report via questionnaires) described self-assessment actions as either strategies or criteria. The study showed that (1) the use of self-assessment strategies and criteria was more frequent and advanced without feedback and among girls, (2) there were different self-assessment patterns by school subject, (3) patterns of strategy and criteria use differed by school year, and (4) none of the self-assessment strategies or criteria had a statistically significant effect on self-efficacy.

Factors influencing self-assessment

Feedback in general has been shown to improve academic performance, especially when focused on specific tasks, processes, and self-regulation (Hattie & Timperley, 2007 ; Wisniewski et al., 2020 ). Butler and Winne’s ( 1995 ) feedback review showed that self-regulated learners adjust their internal feedback mechanisms in response to external feedback (e.g., scores, comments from teachers). Scholars have claimed that students need instructor’s feedback about their self-assessments as well as about content knowledge (Andrade, 2018 ; Brown & Harris, 2014 ; Panadero et al., 2016 ; Boud, 1995 ). Previous studies have shown little effect of external feedback on student self-assessment (Panadero et al., 2012 , 2020 ; Raaijmakers et al., 2019 ). Thus, understanding how external feedback such as instructor’s or via instruments (e.g., rubrics) can influence students’ self-assessment is important.

Among feedback factors that influence student outcomes (Lipnevich et al., 2016 ), the timing of feedback is important. In general, delayed feedback is more likely to contribute to learning transfer, whereas prompt feedback is useful for difficult tasks (Shute, 2008 ). However, linking feedback to self-assessment is relatively rare. Panadero et al. ( 2020 ) found that secondary education students self-assessed using fewer strategies and criteria after receiving feedback. This has crucial implications for instructors as to when they should deliver their feedback, if they want students to develop calibrated self-assessments.

One potentially powerful mechanism for providing feedback is a marking, scoring, or curricular rubric, which has been shown to have stronger effects on performance than other assessment tools, such as exemplars (Lipnevich et al., 2014 ). The use of rubrics in education and research has grown steadily in the last years (Dawson, 2017 ), due to its instructional value with positive effects for students, teachers and even programs (Halonen et al., 2003 ). Rubric use has been associated with positive effects on self-assessment interventions and academic performance (Brookhart & Chen, 2015 ). Previous research has demonstrated that a rubric alone produced better results than combining rubrics with exemplars (Lipnevich et al., 2014 ). Although there is previous research exploring the effects of rubrics when compared or combined with feedback (Panadero et al., 2012 , 2020 ; Wollenschläger et al., 2016 ), we still need insights around the impact of rubrics with or without feedback on student self-assessment.

It was established in the self-assessment literature that more sophisticated and accurate self-assessments are conducted by older and more academically advanced students (Brown & Harris, 2013 ; Barnett & Hixon, 1997 ; Boud & Falchikov, 1989 ; Kostons et al., 2009 , 2010 ). As Boud and Falchikov ( 1989 ) demonstrated, it was subject specific competence that reduced discrepancy between self-assessments and teacher evaluations. However, recent research shows that the relationship might not be so straight forward (Panadero et al., 2020 ; Yan, 2018 ). Additionally, it is unclear at what level of higher education students need to be to have sufficient expertise to self-assess appropriately. Thus, an investigation with students in consecutive years of study in the same domain might clarify the role of year level on self-assessment capacity.

Research aim and questions

The current study adds to this body of research by examining the number and type of self-assessment strategies and criteria among higher education students in a randomized experiment which manipulated three feedback conditions (rubric vs. instructor’s vs. combined) without a control group because the university Ethics Committee did not grant permission. Importantly, we also examined feedback occasion (before vs. after) and year level (1st, 2nd, and 3rd university undergraduates). This is a single group, multi-method study (i.e., think aloud, observation, and self-report; though only the two first ones are analyzed here).

We explored three research questions (RQ):

What are the self-assessment strategies and criteria that higher education students implement before and after feedback?

Hypothesis 1 (H1): Self-assessment strategies and criteria will decrease when feedback is provided, in line with Panadero et al. ( 2020 ).

What are the effects of feedback type and feedback occasion on self-assessment behaviors (i.e., number and type of strategy and criteria)?

H2: Rubric feedback will provide better self-assessment practices than other feedback types, in line with Lipnevich et al. ( 2014 ).

What is the effect of student year level on the results?

H3: Students in higher years within a discipline will use more sophisticated strategies and criteria in their self-assessments. There are results in different directions from no differences in primary education but less self-assessment in more advanced secondary education students (Yan, 2018 ), to more similarities than expected yet some differences identified in secondary education students (Panadero et al., 2020 ). Nevertheless, as our participants are higher education students, it is expected they will behave differently with more advanced students showing higher self-assessment skills.

A convenience sampling method at one university site where the first author worked created a sample of 126 undergraduate psychology students (88.1% females) across first, second, and third years of study (34.9%, 31.7%, and 33.3%, respectively). Participants were randomly assigned to one of three feedback conditions: rubric only ( n  = 43), instructor’s written feedback ( n  = 43), and rubric and instructor’s written feedback combined ( n  = 40). Participants received credit in accordance with the faculty volunteering programme. In a 3 × 3 ANOVA, given a risk level of α  = 0.005, and a statistical power of 1 −  β  = 0.800, the current sample size would detect a medium effect size, f  = 0.280 (G*Power 3.1.9.2; Faul et al., 2007 ).

Data collection and instruments

Data from the video-recorded think aloud protocols was inductively coded using the categories defined in a previous study (Panadero et al., 2020 ). In addition, two structured feedback intervention tools were used (i.e., rubric and instructor’s feedback).

Coded video-recorded data

Think-aloud protocols.

Participants were asked to think aloud while conducting two self-assessments of their written essay. The first was an unguided self-assessment in which students were asked to evaluate the quality of their essay and the reasons for their evaluation. Participants were asked to express their thoughts and feelings and reminded that if they were silent, they would be prompted to think out loud. After the feedback was provided, students were asked to talk about their thoughts and feelings concerning the feedback and to repeat the think aloud process of self-assessing their essay. If the participant remained silent for more than 30 s, they were verbally reminded to think out loud. There were no time restrictions to perform the self-assessment.

A closed coding process was followed, as the codes were already defined as part of a previous study (see Panadero et al., 2020 ) with secondary education students. In such study, a deductive approach was employed to create the two general coding categories of self-assessment elements: strategies and criteria. Additionally, we created codes for those general categories. The categories were contrasted with the data using an inductive approach, to ensure that they were applicable to the new sample and procedure.

The video-recorded think-aloud content was coded to identify the strategies and criteria each student used. As in our previous study, we further organized each set of 13 categories into four levels for clarity in interpretation (0–3). Such levels classify the categories depending on their type and complexity. Details of the levels, categories, definitions, and exemplar comments are provided in Table 1 .

Intervention prompts

Rubric (appendix 1).

It was created for this study using experts’ models of writing composition. It contains three types of criteria: (1) writing process, (2) structure and coherence, and (3) sentences, vocabulary, and punctuation. There are three levels of quality: low, average, and high. The rubric is analytic as three criteria should be scored independently. The rubric was provided to some of the students during the experimental procedure, depending on the experimental condition, but it was not explicitly used by de instructor to provide feedback on the essays.

Instructor’s feedback (Appendix 2)

The instructor provided feedback to each essay using the same categories as the rubric. For the “writing process” criterion, as that was not directly observable by the instructor, he provided feedback by suggesting whether some of those strategies had been put into places (e.g., planning). Additionally, it included a grade ranging from 0 to 10 points. All essays were evaluated by the second author. The first author evaluated a third of the essays reaching total agreement in the rubric categories.

This randomized experiment is part of a larger study; this report focuses on the specific self-assessment strategies and criteria students elicited (see Fig.  1 ), as measured via thinking aloud protocols and observations. After attending a 3 h’ group seminar on academic writing, participants wrote a short essay answering the question: “Why is the psychologist profession necessary?”. This topic was directly directed to the participants’ psychology programme. There was no length limitation for the essays that were written in the participants’ computers, which then submitted it to the research team. This essay did not have implications outside of the research experiment but we emphasized its utility for the students’ academic perspective of the programme. Some days later (approx. 1 week), participants went individually to the laboratory setting. There, they participated in the experiment face-to-face with one of the authors.

Experimental procedure

First, they received the instructions for self-assessing their essay that was handed out to them in its original form, in other words with no feedback. Students were instructed to while self-assessing think aloud their thoughts, emotions, and motivational reactions. Then, they performed the first think aloud self-assessment of the essay they had written. Right after, participants were given feedback on their essay according to the condition they had been assigned to (rubric vs. instructor vs. combined) and asked to self-assess again. The rubric group was handed out the rubric with the instruction of using it for their self-assessment. In the instructor’s feedback group, the participants were said that they should use the instructor’s feedback for their self-assessment. Finally, the combined group received both instructions. After reading the feedback, each participant repeated the self-assessment thinking aloud.

Data analysis

The coding of the think aloud utterances for strategies and criteria was evaluated in three rounds of inter-judge agreement. In round one, agreement between two judges on 15 videos reached an average Krippendorff’s α  = 0.78, with three categories below 0.70. After discussion and consensus building around the low agreement categories, a second set of 15 videos was coded with an average Krippendorff’s α  = 0.83. A third round, using 15 new videos, produced Krippendorff’s α  = 0.87. This indicates the final coding values are dependable. The direct observation was performed in situ during data collection but more intensively during the coding of the video data. The observation data was used to inform and confirm the thinking aloud categories via defining the participants’ behavior, so as supplementary data to further establish the categories.

The categorical variables were described using multiple dichotomous frequency tables, as each participant could display more than one behavior. To study the effect of the factors (feedback occasion, condition, and year level) on self-assessment strategies and criteria frequencies, we conducted ANOVAs and square test to compare differences among the levels.

RQ1: What are the self-assessment strategies and criteria that higher education students implement before and after feedback?

Type of strategies.

Table 2 shows the multiple self-assessment strategies enacted by the participants. The most used before feedback were Read the essay , Think of different responses , and Read the instructions . After the feedback, the most used were Read the feedback or rubric received and Compare essay to feedback or rubric . These strategies are low level according to our code except for Think of different responses which show a deeper level of self-assessment elaboration. Three main results can be extracted. First, the strategies used before and after feedback are similar in nature, with five categories occurring at both moments. However, second, once the students received the feedback, there was a general decrease in the number of frequency of strategies with three out of the five strategies showing significant decreases. This is logical as most of the strategies were basic, and participants did not need to enact them again (e.g., read the essay, which they had done just minutes before). Also, there was the appearance of two new strategies that were not present before the feedback as they are specific to the reception of feedback (i.e., Read the feedback or rubric received and Compare essay to feedback or rubric ). Third, after the feedback, there was also a new category that the participants did not activate it before: Compares question and response .

Type of criteria

As the students could choose more than one criterion, we described multiple dichotomous variables. In general, the most used criteria before the feedback were: Sentences and punctuation marks , Negative intuition , Positive intuition , and Paragraph structure (Table 3 ). The most used after the feedback were as follows: Feedback received , Sentences and punctuation marks , Paragraph structure , and Writing process . When it comes to the trajectories, most of the criteria frequencies decreased significantly after receiving the feedback. However, there were three criteria that increased after feedback (significantly Writing process and Paragraph structure , non-significantly Sentences and punctuation marks ) all being advanced strategies and all increasing in the rubric and combined condition but decreasing in the instructor’s condition. Additionally, a new criterion was used Feedback received , which, for obvious reasons, only occurred after feedback.

RQ2: What are the effects of feedback type and feedback occasion on number and type of strategy and criteria in self-assessment behaviors?

At time 1, before receiving feedback, the number of strategies by condition (Table 4 ) differed statistically and substantially ( F (2, 121)  = 4.22, p  = 0.017, η 2  = 0.65) with a significant post hoc difference between the instructor condition ( M  = 2.78, SD  = 0.183) and the combined condition ( M  = 2.06, SD  = 0.185); the rubric condition did not differ from any of the two ( M  = 2.37, SD  = 0.179). When it comes to number of criteria used, the conditions were equivalent ( F (2, 121)  = 0.48, p  = 0.62, η 2  = 0.008, 1 – β  = 0.127) with no differences among the three groups: instructor ( M  = 3.32, SD  = 0.224), rubric ( M  = 3.51, SD  = 0.219), or combined ( M  = 3.63, SD  = 0.227). We also analyzed if there were differences within the different levels of strategies ( χ 2 (6)  = 8.38, p  = 0.21), and levels of criteria ( χ 2 (6)  = 6.32, p  = 0.39), but both were equivalently distributed across conditions.

At time 2, after feedback, the number of strategies by condition (Table 4 ) did not differ ( F (2,121)  = 0.42, p  = 0.66, η 2  = 0.007, 1 −  β  = 0.118): instructor ( M  = 2.56, SD  = 0.976), rubric ( M  = 2.44, SD  = 0.765), or combined ( M  = 2.40, SD  = 0.671), showing that the effects of rubric had no meaningful impact on the number of strategies. However, the number of criteria differed substantially ( F (2,121)  = 25.30, p  < 0.001, η 2  = 0.295) with significant post hoc differences for Rubric ( M  = 4.48, SD  = 0.165) and combined conditions ( M  = 4.50, SD  = 0.171) that outperformed the instructor condition ( M  = 3.02, SD  = 0.169), both at p  < 0.001. Similar to the number of strategies, the level of strategies was equivalently distributed across conditions ( χ 2 (6) = 2.29, p  = 0.89). However, and to be expected, the level of criteria differed significantly ( χ 2 (4) = 12.00, p  = 0.02), which is likely to be a function of the large sum of criteria differences across conditions at Time 2 (i.e., 193, 134, 180, respectively). When viewed as differences based on percentage of responses at each level, this is statistically not significant (χ 2 (4)  = 7.74, p  = 0.10).

When we explored the interaction condition by feedback occasion, we found no significant effect in self-assessment strategies ( F (2,121)  = 1.74, p  = 0.180, η 2  = 0.028). However, we found a significant main effect of condition in self-assessment criteria ( F (2,115)  = 7.97, p  = 0.001, η 2  = 0.116). The pre-post increase in number of strategies deployed was greater (post hoc p  = 0.002) in the rubric ( M  = 0.938, SE  = 0.247) than in the instructor's feedback ( M  =  − 0.291, SE  = 0.253) condition. The combined condition ( M  = 0.881, SE  = 0.256) also yielded a greater increase (post hoc p  = 0.004) compared to the instructor’s feedback.

RQ3: What is the effect of student year level on the results?

We calculated the differences in strategies and criteria by year level between pre- and post-feedback conditions in two-way ANOVAs with condition and year level as factors. When it comes to the use of strategies, neither main effects (i.e., year level, F (2, 115)  = 1.04, p  = 0.359, η 2  = 0.018, 1 −  β  = 0.227; feedback type, F (2, 115)  = 1.72, p  = 0.183, η 2  = 0.029, 1 −  β  = 0.355) nor interaction ( F (2, 115)  = 0.973, p  = 0.425, η 2  = 0.033, 1 −  β  = 0.300) was significant, largely due to lack of power. Likewise, in the use of criteria, the same result was seen (i.e., year level, F (2, 115)  = 1.68, p  = 0.192, η 2  = 0.028, 1 −  β  = 0.347; feedback type, F (2, 115)  = 7,57, p  < 0.001, η 2  = 0.116, 1 −  β  = 0.940; and interaction, F (2, 115)  = 0.25, p  = 0.911, η 2  = 0.009, 1 −  β  = 0.102). Therefore, our hypothesis that older students would show more advanced self-assessment action is not supported.

This study explored the effects of three factors (i.e., feedback type, feedback occasion, and year level) on self-assessment strategies and criteria. This study contributes to our understanding of what happens in the “black box” of self-assessment by disentangling the frequency and type of self-assessment actions in response to different types of feedback.

Effects on self-assessment: strategy and criteria

In RQ1, we categorized self-assessment actions in a writing task in terms of strategies and criteria. Strategies were categorized on their depth or sophistication ranging from very basic activities (e.g.,  Read   the essay ) to advanced ones (e.g.,  t hink of different responses ). Understandably, the most common strategies were relatively low level, as they are foundational to understanding the task. However, once feedback was received most of the strategies focused on the content of the feedback received (e.g.,  Compare essay to feedback or rubric ), making the feedback as the anchor point of comparison (Nicol, 2021 ). In consequence, the strategies used prior to feedback were greatly reduced in number, indicating that, with feedback, self-assessment strategies were led by that information. Self-assessment criteria demonstrated similar effects. Prior to feedback, students used a wide range of criteria ranging from very basic (e.g.,  Negative intuition ) to advanced (e.g.,  Writing process ). Upon receipt of feedback, most of the criteria responded to the feedback in a less sophisticated manner, especially in the presence of rubrics.

In terms of the three different feedback conditions (RQ2), the two conditions containing rubrics outperformed the instructor’s feedback group in terms of criteria and close the initial gap in strategies. Despite of the instructor’s feedback condition having a higher number of self-assessment strategies before the intervention than the combined group, that difference vanished after feedback. Both the rubric and combined conditions had a higher number and more advanced types of criteria after feedback than the instructor’s feedback condition by large margins. No statistically significant differences in self-assessment strategies and criteria were found across the year levels (RQ3) regardless of feedback presence or type.

Regarding the alignment of our results to previous research, first, the feedback occasion effects on self-assessment strategies are very similar to a study with secondary education students (Panadero et al., 2020 ), as these strategies decreased significantly after feedback except for the ones related to the use of the feedback. In contrast, while the secondary education students decreased their number of criteria used and the type of criteria, here university students increased the number of criteria and used more advanced criteria when using rubrics, an instrument that was not implemented in Panadero et al. ( 2020 ). (Wollenschläger et al., 2016 ) compared three conditions (rubric, rubric and individual performance feedback, rubric and individual performance-improvement), finding that the latest was more powerful in increasing performance than the two first conditions. An important difference of this study is that it examined the impact of rubric and feedback on self-assessment, while the Wollenschläger et al. ( 2016 ) study examined the effects on academic performance. Hence, the impact of feedback appears to be contingent upon the kind of assessment being implemented.

Also, the secondary education students in Panadero et al. ( 2020 ) study showed differences across year levels, which was not found here with university students. This year level lack of effects aligns with Yan ( 2018 ) primary education students where he did not find differences, but is it not aligned with the same study when comparing secondary education students where he found significant differences (i.e., older students self-reporting lower levels of self-assessment). Unlike studies that have reported clearly delineated phases of self-assessment (Yan & Brown, 2017 ), the think aloud protocols in this study did not identify clear-cut phases, finding instead a naturally evolving process. While (Panadero et al., 2012 ) reported that scripts were better than rubrics, this study found that the presence of rubrics led to more sophisticated criteria use; future research would need to determine if script-based feedback would have any greater impact.

Three main conclusions from this study can be reached. First, there are different effects due to the form of feedback, with rubric-assisted feedback being especially promising for self-assessment. The effect of rubrics corrected the initial difference between the instructor’s feedback and the combined group so that, after receiving the feedback or/and rubric, all conditions were equal in terms of the number of self-assessment strategies. Also, and more interestingly, the rubrics conditions showed bigger effects on the use of criteria even in a situation in which the participants had already self-assessed freely before. This might indicate that rubrics as a tool are indeed very useful in stimulating student reflection on their work (Brookhart, 2018 ), more so than instructor’s feedback which may have been perceived as external criticism rather than supportive of improvement. This effect could be caused by instructor’s feedback putting students in passive position (e.g., they are being evaluated, they are recipients of feedback), while rubrics provided them with guides to explore and reflect by themselves. This also might speak to the importance of tools, such as rubrics, to support active self-assessment, rather than of the importance of providing corrective or evaluative feedback. This result might seem logical, as rubrics contain clear criteria and performance levels to which performance can be anchored. This may be especially pertinent to higher education students who are used to being assessed and graded against standards (e.g., Brookhart and Chen, 2015 ). Therefore, one viable conclusion is that the best type of feedback among the explored ones here is using rubrics, followed by a combination of rubric and instructor’s feedback.

Second, the introduction of feedback does impact self-assessment practices. Feedback decreased the number of strategies and increased the level of criteria used. A feature of this study is that students had to self-assess before they received feedback and then again upon receiving it. This process shows the impact of feedback in that it changes the strategies and criteria that students used. Therefore, for educational benefit, feedback may best be presented after students are required to implement their own self-assessment based on their own strategies and criteria. It may be that performance feedback prior to self-assessment will discourage students from the constructive strategies and criteria they exhibited in the pre-feedback stage.

And third, although self-assessment strategies did not become more advanced over years of study among our participants (i.e., our year level variable), this is not likely to be because there was a ceiling effect in the task itself. It is possible for students to exhibit in such a task more sophisticated strategies and criteria. It may be that, once entry to higher education is achieved, self-assessment is relatively homogeneous for this type of task. Perhaps much more demanding tasks (e.g., research thesis) would require more sophisticated self-assessment behaviors.

Limitations and future research

First, our participants conducted a first self-assessment without any structure or teaching on how to effectively evaluate one’s own work. Future research could introduce an intervention on self-assessment prior to the introduction of feedback to better eliminate confounds between self-assessment and feedback. Second, feedback focused on the essay writing task, not on the self-assessment process; such feedback may have had an effect on the quality of subsequent self-assessments (e.g. Andrade, 2018 ; Panadero et al., 2016 ). Third, the absence of a control group with no feedback is a limitation, although our conditions can be more realistic controls than no feedback as it is unusual to find activities without some kind of feedback in real educational settings. Additionally, internal feedback seems to be ubiquitous and automatic in any event (Butler & Winne, 1995 ), so even in the absence of experimenter-controlled feedback, there will be feedback. Fourth, the rubric contained an assessment criteria (i.e., writing process ) that only the students could assess as the instructor did not have access to the process. Fifth, it could be an interesting line of work to explore peer feedback and how it affects self-assessment strategies and criteria. While there has been some research in that direction (To & Panadero, 2019 ), it would be interesting to explore these effects using our methodology to fulfill the aim of “opening the black box of self-assessment.” Sixth, it is likely that greater insights into self-assessment could be achieved by combining this self-reported approach to self-assessment with technology, such as eye-tracking (Jarodzka et al., 2017 ) or physiological reaction equipment (Azevedo et al., 2018 ). These additional tools may allow for a more precise understanding of the underlying cognitive, emotional, and motivational processes in self-assessment and in response to feedback. And seventh, future research should also seek to determine if there are gender or content-specific effects on self-assessment and feedback (Panadero et al., 2016 ).

Conclusions

In general, this study shows that rubrics have the greatest potential to increase positively the quality of student self-assessment behaviors. The study also indicates that feedback has a mixed effect on self-assessment strategies and criteria use. This may explain in part why reliance on feedback from peers or markers has been shown to have a negative impact on overall academic performance (Brown et al., 2016 ). Students who rely more on their own evaluative and self-regulatory learning strategies are more likely to discount external feedback. The provision of rubrics is likely to enable more effective and thoughtful self-assessed judgements about learning priorities. All in all, this study helps to better understand the specific strategies and criteria higher education students enact while self-assessing, something that is key to really understanding how self-assessment works.

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Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Research funded by Fundación BBVA call Investigadores y Creadores Culturales 2015 (project name Transición a la educación superior id. 122500) and by Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad) National I + D Call (Convocatoria Excelencia) project reference EDU2016-79714-P.

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Facultad de Psicología Y Educación, Universidad de Deusto, Bilbao, Spain

Ernesto Panadero

IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

Departamento de Investigación y Psicología en Educación, Universidad Complutense de Madrid, Madrid, Spain

Daniel García Pérez

Departamento de Psicología Evolutiva y de la Educación, Universidad Autónoma de Madrid, 28049, Madrid, Spain

Javier Fernández Ruiz

Universidad Francisco de Vitoria, Madrid, Spain

Juan Fraile

Departamento de Psicobiología y Metodología de las Ciencias del Comportamiento, Universidad Complutense de Madrid, Madrid, Spain

Iván Sánchez-Iglesias

Faculty of Education & Social Work, The University of Auckland, Auckland, New Zealand

Gavin T. L. Brown

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Facultad de Psicología y Educación, Universidad de Deusto, Bilbao, Spain. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.

Current themes of research :

Self-regulated learning. Formative assessment (self-assessment, peer assessment, and teacher’s feedback). Rubrics. Socially shared regulated learning.

Most relevant publications in the field of Psychology of Education :

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research.  Frontiers in psychology ,  8 , 422.

Panadero, E., & Jonsson, A. (2020). A critical review of the arguments against the use of rubrics.  Educational Research Review ,  30 , 100,329.

Panadero, E., Brown, G. T., & Strijbos, J. W. (2016). The future of student self-assessment: A review of known unknowns and potential directions.  Educational Psychology Review ,  28 (4), 803–830.

Daniel García-Pérez

Departamento de Investigación y Psicología en Educación, Universidad Complutense de Madrid, Madrid, Spain.

Educational research. Democratic education. Assessment. Learning strategies.

García-Pérez, D., Fraile, J., & Panadero, E. (2021). Learning strategies and self-regulation in context: How higher education students approach different courses, assessments, and challenges.  European Journal of Psychology of Education ,  36 (2), 533–550.

Panadero, E., Garcia-Pérez, D., & Fraile, J. (2018). Self-assessment for learning in vocational education and training.  Handbook of vocational education and training: Developments in the changing world of work , 1–12.

Departamento de Psicología Evolutiva y de la Educación, Universidad Autónoma de Madrid, Madrid 28,049, Spain. E-mail: [email protected].

Higher education. Formative assessment. Teacher education. Assessment design.

Fernández-Ruiz, J., Panadero, E., & García-Pérez, D. (2021). Assessment from a disciplinary approach: Design and implementation in three undergraduate programmes.  Assessment in Education: Principles, Policy & Practice ,  28 (5–6), 703–723.

Fernández Ruiz, J., Panadero, E., García-Pérez, D., & Pinedo, L. (2021). Assessment design decisions in practice: Profile identification in approaches to assessment design.  Assessment & Evaluation in Higher Education , 1–16.

Universidad Francisco de Vitoria, Madrid, Spain.

Rubrics. Self-regulated learning. Assessment.

Fraile, J., Panadero, E., & Pardo, R. (2017). Co-creating rubrics: The effects on self-regulated learning, self-efficacy and performance of establishing assessment criteria with students.  Studies in Educational Evaluation ,  53 , 69–76.

Panadero, E., Fraile, J., Fernández Ruiz, J., Castilla-Estévez, D., & Ruiz, M. A. (2019). Spanish university assessment practices: Examination tradition with diversity by faculty.  Assessment & Evaluation in Higher Education ,  44 (3), 379–397.

Departamento de Psicobiología y Metodología de las Ciencias del Comportamiento, Universidad Complutense de Madrid, Madrid, Spain.

Statistics. Data analysis. Psychology. Psychometrics.

Panadero, E., Fernández-Ruiz, J., & Sánchez-Iglesias, I. (2020). Secondary education students’ self-assessment: The effects of feedback, subject matter, year level, and gender.  Assessment in Education: Principles, Policy & Practice ,  27 (6), 607–634.

Faculty of Education & Social Work, The University of Auckland, Auckland, New Zealand.

Educational assessment. Psychology of assessment. Psychometrics.

Brown, G. T. (2004). Teachers’ conceptions of assessment: Implications for policy and professional development.  Assessment in Education: Principles, Policy & Practice ,  11 (3), 301–318.

Brown, G. T. L., & Harris, L. R. (2013). Student self-assessment. In J. McMillan (Ed.), The SAGE handbook of research on classroom assessment (pp. 367–393). Thousand Oaks, CA: SAGE.

Appendix 1. Rubric

Appendix 2. Instructor feedback (three samples)

The text structure has important flaws. It does not follow a coherent argument; on the contrary, ideas change abruptly in each paragraph. For instance, any of the first three paragraphs could actually be the introduction paragraph because each of them present different ideas as it was the introduction. Later, in the argument crux, there are several ideas without connection. Finally, the previous to the last paragraph seems to be closing the text but, nonetheless, there is an additional paragraph after it. Furthermore, that previous to the last paragraph includes a new idea (about the methodology), which has not been mentioned before and it could be used as an argument in favor of Psychology.

To sum, even though a central message can be perceived (the multiple areas of application of Psychology), it is not developed nor transmitted effectively. Regarding grammar, highlighted in the text, there are mistakes and comments in the footnotes.

The text has a quite clear structure, with a paragraph of introduction, three for crux, and a closing paragraph. However, there are two arguments in the introduction, and one of them is not developed in order to refute it (the skepticism of certain people). In addition, the last paragraph includes a new idea that has not been discussed before and it does not recap and finish with the main message to be transmitted. In general, there is a correct use of connectors and discourse markers.

Regarding the style and grammar, in general, the construction of the sentences is correct, and the vocabulary is appropriate. Nevertheless, there are some mistakes in the sentence construction and some limitations in the vocabulary selection, which are highlighted in the text and commented in footnotes.

The text has an adequate argumentative structure, with an introductory paragraph, four for the argument crux, and a closing paragraph. Connectors and discourse marks are properly used.

Regarding the text style, it is correct considering the vocabulary, the use of punctuation marks, and the sentence construction. There are some minor mistakes highlighted in the text and commented in footnotes.

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Panadero, E., Pérez, D.G., Ruiz, J.F. et al. University students’ strategies and criteria during self-assessment: instructor’s feedback, rubrics, and year level effects. Eur J Psychol Educ 38 , 1031–1051 (2023). https://doi.org/10.1007/s10212-022-00639-4

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DOI : https://doi.org/10.1007/s10212-022-00639-4

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Self-Assessment Report (SAR) Process​​​

Introduction.

The Self-Assessment Report (SAR) is the first step of a programme review. It is the critical self-analysis of a programme or entity based on documented evidence and completed by the programme or entity itself prior to the external peer review. Prior to this, a curriculum review exercise is usually conducted, a report of which will be included in the SAR.

What is it?​

The SAR is an approximately 40-page (plus appendices) holistic report that covers all aspects of the programme and allows the deliverers and organizers of the programme to self-appraise its accomplishments and progress.

There are guidelines that provide for ‘What constitutes a ‘complete self-assessment report’ as required by the university’s Academic Quality Framework. Before the SAR report is forwarded to the Peer Reviewers, the Quality Assurance Review Committee (QARC) members independently review for its completeness, which requires the following elements: 

The SAR addresses all 18 cells (see IUCEA Road Map, Volume 1: p. 36), and provides a substantial array of evidence to support the findings of the Report. 

The SAR clearly defines the strengths and weaknesses of the programme, as the Self-Assessment Committee sees them, and also areas of good practice. 

The contents of the proposed Improvement Plan (or Action Plan) are closely aligned with the identified weaknesses of the programme, and the proposed actions are SMART (i.e. specific, measurable, achievable, realistic and time-bound). 

How does it work?

QAI conducts self-assessment training for SAR participants appointed by the Dean. The individuals who undergo the SAR training then carry out the SAR exercise for programme(s) in their entity. The completed SAR is independently reviewed by QARC for its ‘completeness’ and signed-off for the next level, which is the peer/external review process.​

Benefits: 

Why is self-evaluation important?

The workers within an educational organization should always aim to produce, improve and enhance its quality, rather than passively accepting that everything is fine.

The self-assessment will provide information that was not previously known to all parties and enhance the transparency and accountability of a programme/entity.

The self-assessment involves all members of an organization, including students, in the discussion on the quality of education, and takes their input into account.

A self-assessment serves as a preparation for a site visit by external experts, providing them with basic information.​

Participation:

The SAR team is composed of 4-6 faculty members, at least 1 academic staff member and at least 1 student. These individuals are nominated by the Dean and selected by the Provost. The Dean will nominate a faculty member from the group to serve as Chair and to be responsible for the production of the SAR. Normally, the Programme Director/Head of progr​ammes chairs the SAR group. A secretary from within the entity will be assigned to work with the group. 

An effective self-assessment is time-consuming. It requires effort b​y staff and students. The approximate duration of a programme SAR is 4-6 months, and a cluster-programme or full entity SAR is 6-8 months. 

The following table shows all AKU entities/programmes that have gone through self-assessment training and have completed or are undergoing the SAR exercise:​

QAI is available to aid the programmes throughout the SAR process through a three-tier support system.

1st tier: Training Sessions

QAI will conduct SAR training for members of the SAR committee 

If requested, QAI can provide an additional cell-by-cell training session(s)

QAI will guide the members through the process, address any challenges and answer all questions, and provide examples of other entities that have faced similar challenges

QAI will refer the SAR team to available self-assessment forums/resources both internal and external to the university

2nd tier: Email and Phone Correspondence

Any questions or concerns from the SAR team can be addressed to QAI via email or telephone and will be responded to as soon as possible

QAI will send periodic reminders to the SAR Chair requesting updates on the process and/or inquiring if they require additional aid

Contact [email protected] for any questions

3rd tier: Additional One-on-one Meetings

Individual or group consultation with SAR members and QAI members

Zoom, Skype or face-to-face

Testimonials:

“I personally started to analyze m​y own teaching/learning practices.” 

(SONAM Pakistan, 2015)

“Initially we felt threatened and defensive towards having to participate in the cyclical review process. However, as we went through the self-assessment process, the act of identifying our own issues means we are more likely to make the needed changes for improvement. It was a very usefu​l process!” 

(Department Chair Medical College Pakistan Graduate Programmes, 2016)

“[I] have more clarity on ​​how to identify evidences and to develop an improvement plan.” 

(IED PK SAR Training, 2018)

QAI Associate Director Faisal Notta conducting SAR training for SONAM Pakistan (2015)

  QTL Director Tashmin Khamis conducting SAR training for IED PK (2018)

​resources:.

For the full guidance sheet, the SAR checklist of “completeness” and other helpful links, check out our Tools for Programme Review​ at AKU in the Resources section.​

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The SHI (and related materials) is available as an interactive, customizable online tool or downloadable, printable version. The SHI aligns with the Whole School, Whole Community, Whole Child (WSCC) model.

CDC developed the SHI in partnership with school administrators and staff, school health experts, parents, and national nongovernmental health and education agencies to:

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• Enable schools to develop an action plan for improving student health that can be incorporated into the School Improvement Plan.
• Engage teachers, parents, students, and the community in promoting health-enhancing behaviors and better health.
• SHI for Elementary Schools [PDF – 2.3 MB]
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Your Guide to Using the School Health Index (SHI) [PDF – 13 MB] includes information and resources for district and school staff who are familiar with the SHI and who are charged with completing the assessment. Included in this Guide are information, materials, and resources on how to implement the SHI in schools, as well as a Facilitating Groups section for conducting trainings, workshops, or presentations on the SHI.

This course  introduces you to CDC’s School Health Index: Self-Assessment and Planning Guide . After this training, you will be ready to conduct or participate in a self-assessment and create a plan to improve the health of students in your school or district.

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PARIS — During Monday night’s Maine School Administrative District 17 business meeting, Curriculum Director Jill Bartash shared data from state assessment tests for reading and math conducted over the last three years. Scores for most schools are showing improved performance since the end of the pandemic, but it is clear that challenges from two years of school closings, quarantines and remote learning remain.

An additional challenge to analyzing assessment scores year-over-year are that three different testing formats have been utilized in Maine in the past six years, Bartash explained to directors. During the 2018-19 academic year, eMPowerME was implemented, and the following year there were no assessments done while schools were closed during the early months of COVID-related mandates.

SAD 17 Curriculum Director Jill Bartash walked school board directors through state assessment results during Monday night’s school board meeting. Nicole Carter / Advertiser Democrat

During the 2020-21 and 2021-22 school years tests were provided by Northwest Evaluation Association (NWEA), a division of Houghton Mifflin Harcourt publishing. Since 2022 the Maine Department of Education has utilized Maine Through Year, another test program developed by NWEA.

The assessments are done in public school districts statewide. SAD 17 has also used Star assessments for reading and math for several years, which has the benefit of more consistent data history.

According to Bartash, the major takeaways from assessment data are attendance, loss of learning between 2019 and 2022, and for continuing educator training to meet the demands of post-pandemic education.

“Attendance matters,” she explained in her presentation.”Students need to be in school to learn new concepts, to fill learning gaps from previous years, and to gain the benefits of social connections to the school community. Advertisement

“Rebounding [from COVID] will take time. Students missed from two-thirds to an entire year of schooling. This will take years to recapture. And professional development, coaching and intervention matter.”

She said educators are teaching children who have missed out on fundamentals to prepare them for later grades and it is important they are prepared to fill in gaps that have not been part of their curriculum in the past.

To counter those challenges, SAD 17 will continue targeting school attendance with a team approach to reduce chronic absenteeism, prioritize professional development and training to support students as they progress from lost classroom experiences and build multi-tiered supports and interventions to meet struggling students.

Most recent results were not certified, but year-to-year comparisons by school show that Oxford Hills scores continue to lag below state averages. But as statewide scores are improving in post-pandemic school years, local results are on similar upward trajectories.

The state averages in 2020-21 were 85.0 in reading and 81.3 in math. The next two years each declined to 64.6 in reading and 48.7 in math, but recovered slightly this year to 65.3 although math went to 47.2.

In SAD 17, 2020-21 averaged 80.1 in reading and 74.1, dipping down to 56.2 in reading and 32.2 in math last year, but this year reading improved to 57.0 and math held at 32.2.

In other business, the board voted unanimously to approve $2 million in tax and revenue anticipation notes. It establishes a line of credit for the district to pay bills during the school year when there are timing gaps in federal grants and reimbursements and local share payments. Renewed annually, the fund is a tool for managing cash flow of budgeted expenditures.

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The  Health Insurance Portability and Accountability Act (HIPAA) Security Rule  requires that  covered entities  and its business associates conduct a risk assessment of their healthcare organization. A risk assessment helps your organization ensure it is compliant with HIPAA’s  administrative, physical, and technical safeguards . A risk assessment also helps reveal areas where your organization’s protected health information (PHI) could be at risk. To learn more about the assessment process and how it benefits your organization, visit the  Office for Civil Rights' official guidance .

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The Office of the National Coordinator for Health Information Technology (ONC), in collaboration with the HHS Office for Civil Rights (OCR), developed a downloadable Security Risk Assessment (SRA) Tool to help guide you through the process. The tool is designed to help healthcare providers conduct a security risk assessment as required by the HIPAA Security Rule. The target audience of this tool is medium and small providers; thus, use of this tool may not be appropriate for larger organizations.

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The SRA Tool is a desktop application that walks users through the security risk assessment process using a simple, wizard-based approach. Users are guided through multiple-choice questions, threat and vulnerability assessments, and asset and vendor management. References and additional guidance are given along the way. Reports are available to save and print after the assessment is completed.

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This version of the SRA Tool takes the same content from the Windows desktop application and presents it in a familiar spreadsheet format. The Excel Workbook contains conditional formatting and formulas to calculate and help identify risk in a similar fashion to the SRA Tool application. This version of the SRA Tool is intended to replace the legacy "Paper Version" and may be a good option for users who do not have access to Microsoft Windows or otherwise need more flexibility than is provided by the SRA Tool for Windows.

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What's new in Version 3.4: 

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The Security Risk Assessment Tool at HealthIT.gov is provided for informational purposes only. Use of this tool is neither required by nor guarantees compliance with federal, state or local laws. Please note that the information presented may not be applicable or appropriate for all health care providers and organizations. The Security Risk Assessment Tool is not intended to be an exhaustive or definitive source on safeguarding health information from privacy and security risks. For more information about the HIPAA Privacy and Security Rules, please visit the  HHS Office for Civil Rights Health Information Privacy website .

NOTE: The NIST Standards provided in this tool are for informational purposes only as they may reflect current best practices in information technology and are not required for compliance with the HIPAA Security Rule’s requirements for risk assessment and risk management. This tool is not intended to serve as legal advice or as recommendations based on a provider or professional’s specific circumstances. We encourage providers, and professionals to seek expert advice when evaluating the use of this tool.

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