Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses

Full text

SELF-ASSESSMENT EFFECTS ON SRL AND SE
1
Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-
analyses
Ernesto Panadero 1 & Anders Jonsson 2 & Juan Botella 3
Author Note
1 Departamento de Psicología Evolutiva y de la Educación. Facultad de Psicología.
Universidad Autónoma de Madrid, Spain.
2 Department of Science. Kristianstad University, Sweden.
3 Departamento de Psicología Social y Metodología. Facultad de Psicología.
Universidad Autónoma de Madrid, Spain.
Recommended citation:
Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-
regulated learning and self-efficacy: Four meta-analyses. Educational Research Review,
22, 74-98. doi:https://doi.org/10.1016/j.edurev.2017.08.004
Correspondence concerning this article should be addressed to: Ernesto Panadero.
Despacho 109. Departamento de Psicología Evolutiva y de la Educación. Facultad de
Psicología. Universidad Autónoma de Madrid, 28049, Cantoblanco. Spain. E-mail:
ernesto.panadero@uam.es. Phone (+34) 914973553.
Acknowledgements: First author funded by the Ministerio de Economía y
Competitividad via Spanish Ramón y Cajal programme (Referencia RYC-2013-13469).

SELF-ASSESSMENT EFFECTS ON SRL AND SE
2
Abstract
This meta-analytic review explores the effects of self-assessment on students’ self-
regulated learning (SRL) and self-efficacy. A total of 19 studies were included in the
four different meta-analyses conducted with a total sample of 2,305 students. The
effects sizes from the three meta-analyses addressing effects on different measures of
SRL were 0.23, 0.65, and 0.43. The effect size from the meta-analysis on self-efficacy
was 0.73. In addition, it was found that gender (with girls benefiting more) and certain
self-assessment components (such as self-monitoring) were significant moderators of
the effects on self-efficacy. These results point to the importance of self-assessment
interventions to promote students’ use of learning strategies and its effects on
motivational variables such as self-efficacy.
Keywords: self-assessment; self-regulated learning; self-efficacy; self-evaluation; self-
regulation; learning strategies; motivation; gender educational differences; self-
regulated learning measurement; emotional regulation.
Highlights
➢ Self-assessment effect on self-regulated learning (SRL) and self-efficacy was
explored
➢ 19 studies (2,305 students) were included in four different meta-analyses
➢ Effects sizes from the three meta-analyses on SRL were 0.23, 0.65 and 0.43
➢ The effect size from the meta-analysis on self-efficacy was 0.73

SELF-ASSESSMENT EFFECTS ON SRL AND SE
3
Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-
analyses
1. Introduction
Student self-assessment has been one of the main areas of research in
contemporary educational and educational psychology research since the seminal
reviewing work by Nancy Falchikov and David Boud in the late 80’s (Boud &
Falchikov, 1989; Falchikov & Boud, 1989). Research on self-assessment has also
transcended these fields of research, extending into fields such as social psychology
(e.g. Dunning, Heath, & Suls, 2004; Kruger & Dunning, 1999) and medical education
(Eva & Regehr, 2005; 2008). The educational research on self-assessment is currently
going through an important phase, as shown by recent publications reviewing the
accumulated empirical evidence and proposing a new agenda for self-assessment (e.g.
Brown & Harris, 2013; Panadero, Brown, & Strijbos, 2016).
As will be shown in this meta-analysis, the relationship between the constructs
self-assessment, SRL, and self-efficacy has been the object of empirical research for at
least twenty years. This relationship is both reciprocal and intricate: self-assessment is
conceptualized as a learning regulatory strategy (Nicol & McFarlane-Dick, 2006;
Panadero & Alonso-Tapia, 2013; Paris & Paris, 2001); SRL is dependent on self-
assessment – via self-monitoring and self-evaluation – to support student learning
(Butler & Winne, 1995; Zimmerman & Moylan, 2009); and self-efficacy is thought to
enhance students’ activation and use of regulatory strategies, such as monitoring and
evaluation (Pajares, 1996, 2008; Schunk & Ertmer, 2000). Furthermore, self-assessment
might increase the perceived capability among students, which could affect students’
self-efficacy (Andrade, Wang, Du, & Akawi, 2009). In the coming sections of the

SELF-ASSESSMENT EFFECTS ON SRL AND SE
4
theoretical framework we will present research on self-assessment, SRL, and self-
efficacy along with the moderating variables included in this meta-analytic review.
1.1 Self-assessment
In a recent state-of-the-art review, self-assessment was defined 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” (Panadero et al., 2016 p. 2). According to this definition, self-
assessment is about students assessing their own work; not about signaling their
perceived understanding to the teacher through “traffic lights” or evaluating their
satisfaction with the instruction. Also important to notice is the emphasis on a “wide
variety of mechanisms”, which acknowledges that there are different ways to implement
self-assessment in the classroom. As a matter of fact, Panadero et al. (2016) found 20
different categories of self-assessment implementations in their review of different self-
assessment typologies. For example, a very simple form of self-assessment is to award a
grade/mark to own work (sometimes called “self-evaluation
1
” or “self-grading”). A
more complex form of self-assessment may involve a rigorous analysis of strengths and
weaknesses, as well as the formulation of formative feedback, in relation to explicit
criteria (Andrade, 2010).
Actually, a large number of calls have been issued recently for moving away
from the simpler forms of self-assessment, where students are merely asked to score or
grade themselves, as opposed to making qualitative judgments about their own
performance (Andrade, 2010; Boud & Falchikov, 1989; Eva & Regehr, 2005). In
particular, the formative assessment agenda has contributed to changing the focus of
1
Not to be confounded with the concept of self-evaluation in SRL research. Here we are referring to self-
assessment studies in which students are asked to estimate their grade, also called self-scoring, with no
relationship to SRL research.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
5
self-assessment research. self-assessment is a fundamental component of formative
assessment since, as stated by Sadler (1989), it is ultimately the student herself that has
to “close that gap” between a current performance (as revealed by assessment) and the
desired standard. A student who only follows the teachers’ prescription without
understanding its purpose will not learn to monitor and self-adjust her work. As
emphasized by Black and Wiliam (1998), self-assessment is therefore not “an
interesting option or luxury” (p. 54-55), it is essential to productive learning, and
empirical research supports this idea.
1.1.1. Self-assessment effects on student learning and performance
Whether self-assessment has an impact on student learning has been explored in
a number of studies. For instance, in a narrative review of research about self-
assessment, Topping (2003) concluded that there is evidence that self-assessment can
result in improvements in the effectiveness and quality of learning. In a more recent
publication, Brown and Harris (2013) come to a similar conclusion by reviewing 23
studies, including a wide variety of operationalizations of self-assessment. The effects
range from -0.04 to 1.62 (Cohen’s d) with a median effect between 0.40 and 0.45. The
authors also note that self-assessment seems to improve student performance across a
range of grade levels and subject areas, but that it seems to be the implementation and
the complexity of the self-assessment intervention, rather than the type, which generates
the positive effects. As an example, rubric guided judgement as a type of self-
assessment, has been shown to result in both very high effect sizes, as well as very low
(and even negative) effect sizes. While students in the latter case (i.e. low effect sizes)
participated in two self-assessment lessons, during which they used a rubric for essay
writing to assess the quality of their drafts (Andrade & Boulay, 2003), the students in
the former (i.e. high effect size) also received rubrics articulating assessment criteria for

SELF-ASSESSMENT EFFECTS ON SRL AND SE
6
essay writing. However, these students also participated in generating a list of criteria
from a model paper (Andrade, Du, & Wang, 2008).
Besides rubrics, which have been suggested to support student learning if
combined with self-assessment or other meta-cognitive activities (Panadero & Jonsson,
2013), feedback has also been shown to influence the relationship between self-
assessment and learning. In a meta-analysis by Sitzmann, Ely, Brown, and Bauer
(2010), the correlation between self-assessment and learning was stronger for courses
that included feedback (r = 0.28) than for courses that did not include feedback (r =
0.14). Furthermore, when students self-assessed once, or on multiple occasions without
receiving feedback, the relationship with learning was weaker (0.29 and 0.30
respectively) as compared to situations where students received external feedback on
their accuracy (r = 0.51).
1.1.2. Self-assessment effects on self-regulated learning and self-efficacy
The abovementioned educational gains from self-assessment are suggested to be
related to the enhancement of ownership of learning and the use of SRL strategies. This
means that self-assessment is thought to contribute to student learning by, for instance,
enhancing the clarity of the learning goals, involving students in monitoring the
learning process, and facilitating reflection about the final product or learning outcome
(Brown & Harris, 2013; Nicol & McFarlane-Dick, 2006; Panadero & Alonso-Tapia,
2013).
Although the relationship between self-assessment and SRL strategies has been
claimed theoretically since the beginning of the formative assessment agenda (e.g.
Black & Wiliam, 1998), there is a need to review what is known from empirical
research. For example, Topping (2003) concluded that the evidence for self-assessment
affecting students’ SRL was “encouraging” and more research was needed. In a similar

SELF-ASSESSMENT EFFECTS ON SRL AND SE
7
vein, Brown and Harris (2013) conclude that the research evidence for the connection
between self-assessment and SRL is “not robust” and that it is still unclear which
students benefit from training in self-assessment.
Regarding self-efficacy, according to social cognitive theory (Bandura, 1986),
there are four factors increasing an individual’s self-efficacy, which are own
experiences of successful performance, watching others succeed, encouragement, and
physiological factors. The basic presumption as to why self-assessment has an effect on
students’ self-efficacy is that by gaining a deeper understanding of the requirements of
the task at hand, students are likely to perform better and therefore to experience
successful performance. This, in turn, is thought to trigger feelings of worth and a
perception of improved capability, which will finally impact on the level of self-efficacy
(e.g. Paris & Paris, 2001).
In the self-assessment literature, however, the findings regarding self-efficacy
are mixed. As an example, in a study by Andrade, Wang, Du, and Akawi (2009)
generating a list of criteria from a model essay and using a rubric to self-assess drafts,
was shown to increase students’ self-efficacy. However, on average, all students’ self-
efficacy increased, including students in the control group. The increase was larger in
the treatment group, but not significantly. There was also a difference between genders,
where girls’ average self-efficacy for writing tended to be higher as compared to the
boys, especially in the beginning. Since the boys’ self-efficacy increased more, as
compared to the girls, there was not a statistically significant difference at the end of the
intervention. Similarly, in a series of studies on the impact of rubric supported self-
assessment on students’ self-efficacy, Panadero (2011) found that self-efficacy was
impacted by the use of rubrics, but only in one of the three studies. The mixed findings
makes it interesting to explore this relationship further through meta-analytic

SELF-ASSESSMENT EFFECTS ON SRL AND SE
8
methodology. In the coming sections, the two dependent variables are presented in more
detail.
1.2. Self-regulated learning
According to one of the most widely used definitions, SRL is: “self-generated
thoughts, feelings, and actions that are planned and cyclically adapted to the attainment
of personal goals” (Zimmerman, 2000 p. 14). SRL has become one of the most
prevalent educational theories to explain students’ achievement as it includes a large
number of variables related to learning, such as goal orientation, task specific strategies,
metacognitive strategies, attribution theory, etc. (e.g. Panadero, 2017). As an example,
Richardson, Abraham, and Bond (2012) performed a meta-analysis based on 11
different SRL components (such as rehearsal, effort regulation, and help seeking),
showing that the use of SRL strategies was a significant predictor of academic
performance. Similar results have also been reported elsewhere (e.g. Broadbent & Poon,
2015).
There are several models of SRL, but most of them include a preparatory phase,
a performance phase, and an appraisal phase, each consisting of different sub-processes
(Panadero, 2017). In Zimmerman’s (2000) cyclical model, which is one of the most
cited in the SRL literature, these phases are called forethought, performance, and self-
reflection.
1.2.1 Self-regulated learning effects on student learning and performance
A number of previous meta-analyses have investigated the influence of SRL on
student learning and performance (for a meta-review, see Panadero, 2017). For instance,
Hattie, Biggs, and Purdie (1996) examined 51 intervention studies aiming to enhance
student learning through SRL. In their meta-analysis, the overall effect size for student
performance was 0.57. Dignath, Buettner, and Langfeldt (2008) analyzed the effects of

SELF-ASSESSMENT EFFECTS ON SRL AND SE
9
SRL on primary school students’ academic achievement, cognitive and metacognitive
strategy application, and motivation. Results from 48 comparisons show that SRL
interventions were effective, even at the primary school level (the overall mean effect
size for academic performance was d = 0.62). For motivational aspects (including self-
efficacy), the mean effect size was even higher (d = 0.76). In a later study (Dignath &
Büttner, 2008), encompassing both primary- and secondary school (357 effect sizes in
total), the mean effect size for academic performance was 0.61 for primary school and
0.54 for secondary school, suggesting that SRL can be fostered effectively at both
primary and secondary school level. However, the effect for motivational aspects was
considerably lower for secondary students.
In the abovementioned meta-analyses, SRL strategies are clustered into larger
categories, making it impossible to disentangle the effects from individual SRL
components. In a study by Sitzmann and Ely (2011), however, 16 different SRL
components were investigated (k = 430), reporting that goal level, persistence, effort,
and self-efficacy were the constructs with the strongest effects on learning for adults.
1.2.2. Self-regulated learning and self-assessment
As mentioned above, a strong theoretical connection has been suggested
between SRL and self-assessment (e.g. Andrade, 2010; Panadero & Alonso-Tapia,
2013). For example, two of the SRL subprocesses in Zimmerman’s (2000) model (self-
monitoring and self-evaluation) have a clear similarity to self-assessment, since they are
oriented towards assessing own performance. Furthermore, self-assessment has been
proposed to be key for the internalization of standards, so that students can regulate
their own learning more effectively (Paris & Paris, 2001, p. 95), which involves the first
phase of SRL (forethought).

SELF-ASSESSMENT EFFECTS ON SRL AND SE
10
Consequently, self-assessment does not only affect the self-reflection phase, but
also the forethought phase (for instance when providing the students with assessment
criteria, so that they are able to set realistic goals for the task) and the performance
phase (since monitoring can be done with more accuracy, as there is a clearer
understanding of the final product/learning outcome) (Andrade, 2010; Panadero &
Alonso-Tapia, 2013). The importance of integrating planning with self-assessment
related processes has been shown by Dignath et al. (2008), who used meta-analytic
methodology to explore SRL interventions in primary school settings. Findings show
that metacognitive interventions aiming at a combination of planning and monitoring
(P&M), or planning and evaluating, were the most successful to enhance students’
strategy use (d = 1.50 and 1.46 respectively) and had significant effects on motivational
outcomes (d = 0.58 and 1.59), as well as academic performance (P&M d = 0.78) (p.
115, Table 9). It has therefore been argued that interventions to promote self-assessment
should be initiated before students start performing the task, for instance by providing
the students with assessment criteria, so that the students can plan, monitor, and
evaluate with the help of these criteria (e.g. Andrade & Valtcheva, 2009; Jonsson,
2014). One of the most direct pieces of empirical evidence for self-assessment having
an effect on all phases of the SRL cycle comes from a study by Panadero and Romero
(2014). In this study, the use of explicit assessment criteria was shown to have a
significant impact on the forethought phase (η2 = 0.257), the performance phase (η2 =
0.084), and the self-reflection phase (η2 = 0.217). As can be seen, the
preparatory/forethought phase activation of learning strategies was affected the most.
1.3 Self-efficacy
SE is the belief about the personal capabilities to perform a task and reach the
established goals (Bandura, 1997). The concept was introduced by Bandura (1977), who

SELF-ASSESSMENT EFFECTS ON SRL AND SE
11
also made developments within the social cognitive theory (1986), making a major
impact in education and educational psychology (van Dinther, Dochy, & Segers, 2011).
self-efficacy has been found to be the strongest predictor of academic performance in
tertiary education in two meta-analyses (Richardson et al., 2012; Robbins et al., 2004),
and a more recent study also report effect sizes of similar magnitude (Honicke &
Broadbent, 2016).
The influence of self-efficacy on the conceptualization and development of SRL
has been crucial (Zimmerman, 2013). Over the years, self-efficacy has become one of
the most important variables not only in research on motivation, but also in research on
SRL (e.g. Schunk & Usher, 2011), and self-efficacy has therefore been incorporated
into SRL models (Panadero, 2017). For example, self-efficacy is an essential sub-
process in the models by both Zimmerman (2000) and Pintrich (2000). As will be
evident from this meta-analysis, self-assessment literature includes a significant number
of studies analyzing self-efficacy in isolation from SRL (e.g. Sitzmann et al., 2010,
reporting a moderate correlation between self-assessment and self-efficacy based on 32
effect sizes), and to a lesser extent, in combination.
1.4 Moderating variables
There are a number of variables that may influence the effects of self-assessment
on SRL and self-efficacy explored in the literature. The four moderating variables
included in this meta-analysis are presented below.
First, gender is a factor that is likely to influence the effects of self-assessment on
SRL and self-efficacy, since gender differences have been reported for all three
variables. However, the findings are not conclusive for all variables and the amount of
research evidence also differs greatly. Starting with the effects of gender on self-
assessment, this has not been extensively studied (e.g. Brown & Harris, 2013; Wright &

SELF-ASSESSMENT EFFECTS ON SRL AND SE
12
Houck, 1995) and mostly in relation to the accuracy of self-scoring. Gender effects on
self-assessment is therefore an under-researched area and several reviews have
recommended future research to explore this effect (e.g. Boud & Falchikov, 1989;
Brown & Harris, 2013; Panadero & Jonsson, 2013). There is also evidence supporting
the existence of gender differences in SRL, which, according to Bussey (2011), do not
relate to differences in SRL capabilities, but to self-efficacy and expectations (i.e. two
SRL sub-processes). As opposed to self-assessment, gender differences in relation to
self-efficacy have been explored in a number of reviews and meta-analyses. In a meta-
analysis by Whitley (1997), it was reported that males had a self-efficacy for computers,
which was 0.41 standard deviations above the average for females. Pajares conducted
two narrative reviews (2003, 2005) on self-efficacy for writing and mathematics,
finding that males tended to have higher self-efficacy for mathematics, while females
tended to have higher self-efficacy for writing during middle school, but that this
difference tend to decrease at older ages. However, these results need to be re-
interpreted in the light of the meta-analysis by Huang (2013), which subsumes the work
by Whitley (1997) and also uses a more rigorous meta-analytical methodology as
compared to Pajares (2003, 2005). The findings from Huang (2013) were a general
effect of 0.08 favoring males, therefore a “small difference” as interpreted by the
author, and differences across subjects. Gender is therefore a crucial variable to be
considered in the current meta-analysis.
Second, another factor potentially influencing the effects of self-assessment on
SRL and self-efficacy is age (or educational level) of the students. In relation to self-
assessment, such research is more or less non-existing and no previous studies
comparing self-assessment skills across different ages have been found, although there
have been calls to approach self-assessment training as a skill that need practice to

SELF-ASSESSMENT EFFECTS ON SRL AND SE
13
develop (Panadero et al., 2016). In relation to the development of SRL strategies,
however, the relationship between self-assessment and SRL is a growing area of
research. For instance, studies show that already young students can be taught and
develop SRL strategies (Perry & Rahim, 2011; Whitebread, Bingham, Grau, Pino
Pasternak, & Sangster, 2007). Furthermore, meta-analyses by Dignath and Büttner
(2008), Dignath et al. (2008), and Hattie et al. (1996) found differential effects of SRL
interventions aiming for primary and secondary school students. First, effect sizes for
academic achievement from interventions aiming to enhance SRL were larger for
younger students (i.e. primary- and lower secondary school) than for older students (i.e.
secondary- and higher education students) (Dignath & Büttner, 2008; Hattie et al.,
1996)
2
. Second, Dignath and Büttner (2008) found a larger effect on motivational
outcomes for primary students, as compared to secondary students, from SRL
interventions. And finally, when exploring strategy use, the effects were reversed (i.e.
secondary education students benefited more, as compared to primary students). Age
was also included as a variable in the meta-analysis by Huang (2013), who reported that
gender differences in self-efficacy tend to increase as age increases.
Third, as reported by Panadero et al. (2016), there is a large number of different
self-assessment practices, which might have differential effects. As an example, in the
list of effect sizes for the relationship between self-assessment and learning, as
presented by Brown and Harris (2013, p. 382), studies with a similar design can be
found both at the top and the bottom of the list (e.g. self-assessing writing with a
rubric), but which differs in the comprehensiveness of the self-assessment intervention.
As suggested by for instance Panadero and Jonsson (2013), as well as the work by
2
Hattie et al. (1996) found larger effects for primary and lower secondary education as compared to older
students (secondary- and higher education students). Dignath and Büttner (2008) found larger effects for
students in primary school as compared to students in secondary education.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
14
Sitzmann et al. (2010), different self-assessment components may therefore have
differential effects. Furthermore, from previous research, rubrics could be expected to
have a larger impact on performance/avoidance SRL as compared to learning SRL. It is
therefore of great need to compare the effects of self-assessment interventions with
different components and intensity in relation to effects on SRL and self-efficacy.
A fourth potential moderating variable is the agent who implements the
intervention (i.e. the teacher or the researcher). This moderator comes from SRL
research, where it has been found that when the SRL interventions were conducted by
the researcher, the effect size was higher as compared to when teachers were in charge
of the implementation (Dignath et al., 2008). Here it will be explored for both SRL and
self-efficacy.
1.5 Measurement of self-regulated lerning and self-efficacy
The choice of measurement instruments for SRL and self-efficacy is also a
potential moderating variable, since different types of instruments have been shown to
provide different results. For instance, in the study by Dignath et al. (2008), intervention
studies using questionnaires reported a higher impact on SRL, as compared to other
types of measurements (e.g. multiple choice test). However, which instruments to use is
a controversial issue in the field. There is a critique of self-reported data (Boekaerts &
Corno, 2005; Veenman, 2011), as well as a defense for other types of self-reported data
(Samuelstuen & Bråten, 2007). There is also a tension between off-line and online
measurements (Winne & Perry, 2000), as well as suggestions for new ways of
measuring SRL (Panadero, Klug, & Järvelä, 2016). In this meta-analysis, SRL
measurements have not been used as a moderating variable. Instead, due to its
importance, this variable has been decompounded into three different (dependent) SRL
variables.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
15
This tension between different measurement instruments is not visible in the self-
efficacy literature. As shown in the meta-analysis by Honicke and Broadbent (2016),
self-report has been the primary method to measure self-efficacy. Specifically, the
Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, &
McKeachie, 1991) has been the most frequently used instrument. An interesting
observation is therefore that even though the MSLQ was created as an instrument to
measure SRL, it is to a large extent used to measure self-efficacy. In sum, the
implication for this meta-analytic review is that there was no need to make a distinction
between different measurement instruments for self-efficacy, since all studies in the
sample used the same measurement method (i.e. questionnaires).
1.6 Research questions
According to the research reviewed above, the empirical support for the effects
of self-assessment on students’ SRL strategies and self-efficacy is seen as promising,
but not conclusive. There are, however, some concerns making it difficult to judge the
validity of this conclusion. First, there is a number of studies about the effects of self-
assessment on SRL that are not included in the abovementioned narrative reviews by
Topping (2003) and Brown and Harris (2013). Second, those reviews do not make any
distinction between: (a) different research designs (such as qualitative research, quasi-
experimental, and experimental design); (b) different measures of SRL; or (c) SRL and
self-efficacy. Thirdly, the review by Brown and Harris (2013) only covered K-12, and
provided meta-analysis only for the relationship of self-assessment to academic
achievement.
Given the inconclusive findings from the primary research, and the limitations
from recent reviews, this study aims to use meta-analytic methodology to explore the
evidence accumulated on the relationship between self-assessment on the one hand, and

SELF-ASSESSMENT EFFECTS ON SRL AND SE
16
SRL and self-efficacy on the other. Specifically, this study will explore the following
research questions:
RQ1: Do self-assessment interventions have an effect on students’ SRL?
RQ1a. Is there a differential effect based on different SRL instruments and
constructs?
RQ2: Do self-assessment interventions have an effect on students’ self-efficacy?
RQ3: Do the moderating variables gender, age/educational level, self-assessment
intervention, and implementation agent influence the effects of self-assessment on
students’ SRL and/or self-efficacy?
2. Method
2.1 Selection of studies
The search was conducted at two different occasions. The initial search was
performed in June 2015. The first author conducted an independent search using his
university’s access to PsycINFO, ERIC, and Google Scholar. The second author
performed an independent parallel search on a common interface called “Summon”,
which includes all available databases his university subscribes to. The search included,
but were not limited to, databases such as PsycINFO, PubMed, ScienceDirect, Web of
Science, and Google Scholar. Both authors used the following combinations of
keywords: Self-regulated learning + self-assessment; Self-regulated learning + self-
evaluation; Self-regulated learning + monitoring; Self-regulation + self-assessment;
Self-regulation + self-evaluation; Self-regulation + monitoring; Self-assessment + self-
efficacy; and Self-evaluation + self-efficacy. Reference lists of empirical and review
articles were also examined for additional references. A second search was performed in

SELF-ASSESSMENT EFFECTS ON SRL AND SE
17
April 2016 using the same databases and keywords to include any recently published
publications on the topic.
The inclusion criteria used were that: (a) the study included empirical results of
self-assessment interventions in relation to SRL and/or self-efficacy; (b) the study had
at least one control group; (c) the study had been peer-reviewed (i.e. either journal
articles or dissertations); and (d) the study was published in English.
Figure 1 presents a flow chart of the systematic review. In total, 142 records
were identified through database and manual searches. After removing duplicates, the
abstracts of the 65 remaining publications were screened in order to select only
empirical studies. The full texts of 36 empirical studies were read and assessed. After
excluding the studies not meeting all inclusion criteria, 19 studies were included in the
meta-analysis. This meta-analysis complies with PRISMA
3
guidelines (Moher, Liberati,
Tetzlaff, & Altman, 2009).
>>> Figure 1 about here <<<
The following information was collected from the selected articles: general
information (authors’ names, year of publication, country, aim, research questions, and
hypotheses), sample characteristics (sample size and gender proportions), study design
(including the variables used), procedure (including self-assessment intervention),
results, and conclusions. Table 1 present a summary with the more important
information from the included publications.
Insert table 1 around here
3
PRISMA stands for Preferred Reporting Items for Systematic reviews and Meta-Analyses. More
information in the above reference or at the PRISMA website: http://prisma-statement.org/

SELF-ASSESSMENT EFFECTS ON SRL AND SE
18
The size of the studies in the sample range from 30 to over 300 participants
(mean 115.25). In total, the studies encompass 2,305
4
students from primary school to
higher education. Studies involving students from primary school are few (2), while
studies from secondary school and higher education are more evenly represented in the
sample (10 and 8 studies respectively). The overall mean age in the sample is 17.5 years
(range 10.5-26 years) and approximately 70 percent of the sample is female.
In studies not reporting student characteristics, or not reporting specifically
enough, first authors were contacted and asked to provide the required information.
However, not all authors responded to this query.
Although subjects such as computers and language occur slightly more often in
the sample, there is large variation in the content domains focused, such as geography,
mathematics, and psychology. There are also a couple of studies focusing on
psychomotor skills, such as throwing dart. Eight studies in the sample used a quasi-
experimental design, while the remaining studies used experimental conditions.
2.2 Dependent and moderating variables
2.2.1 Self-regulated learning variables
Three different types of SRL measurements have been used in the studies
included in this meta-analytic review. These variables are defined by a combination of
SRL constructs and measurement instruments.
The first variable is “Learning SRL”, which encompasses measurements of SRL
via questionnaires, representing actions associated with learning strategies serving
positive self-regulatory functions for students’ learning, such as metacognitive
strategies, motivation, and emotions. A typical example of an instrument is the MSLQ
4
By adding the number of participants in the four different meta-analyses, there would be a total of 2,987
participants. However, since some of the participants are included in more than one meta-analysis, the
more conservative number is reported in the text.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
19
(Pintrich, Smith, Garcia, & Mckeachie, 1991), which includes scales such as rehearsal,
elaboration, or managing time and study environment. This is also the instrument that is
most frequently (Roth, Ogrin, & Schmitz, 2016). The higher the value on these scales,
the more the students are thought to use regulatory actions that will enhance their
learning.
The second SRL variable is “Negative SRL”, which has its theoretical
foundations in the work by Boekaerts (2011) and Kuhl (2000). Like Learning SRL, in
self-assessment research Negative SRL has also been measured by questionnaires, but
associated with negative emotions and stress, encompassing regulatory actions directed
by anxiety, external pressure to perform, and task avoidance. An increase in Negative
SRL is therefore thought to be detrimental for students’ learning. The main instrument
for measuring Negative SRL is the Emotion and Motivation Self-Regulation
Questionnaire (EMSR-Q), initially used in Panadero (2011), where one of the scales
was termed “Performance/Avoidance self-regulation”. The EMSR-Q was later validated
in a study (Alonso-Tapia, Panadero, & Ruiz, 2014) with a significantly higher sample
(664 participants). In this validation study, it was decided to confirm the distinctness of
this SRL variable in relation to the Learning SRL-Q, because the effects were
completely opposite: Higher values on the Avoidance SRL-Q meant more negative self-
regulatory actions that are detrimental for learning with a negative correlation to
Learning SRL scores.
For this reason, it was more informative to run separate meta-analyses on
Learning SRL and Negative SRL in this study. Three of the studies in the sample
explored Negative SRL.
Finally, the third SRL variable is called “SRL measured qualitatively” and
includes qualitative data of SRL, such as think-aloud protocols and open questions.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
20
Only three studies in the sample have used this type of measurement, but due to the
current debate on validity in SRL measurements (Boekaerts & Corno, 2005; Panadero,
Klug, & Järvelä, 2015; Winne & Hadwin, 2000) it was decided do explore “SRL
measured qualitatively” as a separate variable.
2.2.2 Self-efficacy
All the studies in the sample used self-reported questionnaires or scales to
measure self-efficacy, which is also the general tendency in self-efficacy research
(Honicke & Broadbent, 2016). As the construct and measurement is similar across
studies, only one category of self-efficacy was employed in this review.
2.2.3 Gender
Student gender was used as a moderating variable in the meta-analyses. The
percentage of females was computed to run the analyses. As can be seen from Table 1,
females were in majority in most of the studies and in some studies the entire sample
was female.
2.2.4 Age and educational level
Age/educational level were used as moderating variables due to their potential
influence on the development of self-assessment, SRL, and self-efficacy. As can be seen
in Table 2, a range of educational level courses was reported in some studies. In those
cases, an average of students’ age was used. Regarding educational level, there were
three categories: primary, secondary, and higher education.
Insert table 2 around here
2.2.5 Self-assessment intervention categories
In order to be categorized as a self-assessment intervention, students have to
assess the quality of their own performance. This means that several concepts and

SELF-ASSESSMENT EFFECTS ON SRL AND SE
21
measures, which may be referred to as self-assessment elsewhere, were not included.
Examples of concepts and measures that were not included are:
Evaluations of the quality of, or the satisfaction with, instruction. The focus of
the (self-) assessment has to be student performance – not instruction.
Estimations of own general competence, self-concept, or understanding. The
(self-) assessment has to address the quality of performance on specific
tasks/assignments – not general (latent) constructs.
In a most rudimentary form, students can be asked to self-assess without any aids.
self-assessment interventions can also be more wide-ranging and supporting, for
instance by including instruments (such as criteria or rubrics), training, and/or feedback
on self-assessment performance. The classification of self-assessment interventions in
this study is based on the following different categories that were identified from the
included publications (Table 3 contains information on categories for each study):
1. Monitoring (M). In studies like Zimmerman and Kitsantas (1996), students are
provided a protocol or log to record their performance.
2. Self-evaluation (SEV). In studies like Schunk and Ertmer (1999), Alishah and
Dolmaci (2013), and Baleghizadeh and Masoun (2013), students fill in self-
assessment or self-efficacy questionnaires.
3. Self-assessment instruments (SAI). In studies like Panadero et al. (2012),
Panadero et al. (2013), and Kahrizi et al. (2014), students are provided with
instruments (such as rubrics, scripts, or checklists) that can be used to plan,
monitor, and/or self-assess own task performance. In all but two studies
(Goodrich, 1996; Mahlberg, 2015), students are also provided with feedback.
4. Feedback and revision (FR). In studies like Panadero et al. (2013), DiGiacomo
(2014), and Kahrizi et al. (2014), students are provided with teacher feedback

SELF-ASSESSMENT EFFECTS ON SRL AND SE
22
and are then allowed to revise their work. In studies like Panadero et al. (2012)
and DiGiacomo (2014), students are provided feedback and are then allowed to
do other (similar) tasks. This condition is in most cases associated with some
self-assessment instrument, but there are exceptions.
5. Self-assessment training (SAT). In Kahrizi et al. (2014) and Nbina and Viko
(2010) students were exposed to training in self-assessment during an extended
period of time. Because there are only two studies using this type of self-
assessment, this category will not be included in the meta-analyses.
All studies included in this review fit within this categorization, but most of
them use different combinations, such as SAI + FR (which is the most common). Other
combinations are: (M+SEV+FR), (M+SEV), and (M+FR). For that reason, a general
self-assessment intervention score variable was calculated to explore whether more
comprehensive interventions have larger effects.
The initial coding of the selected articles was done by the second author. Then
the first author independently coded eight articles. Perfect agreement was reached at all
occasions (Cohen K = 1) as the specificity of the categories is high.
2.2.6 Implementation agent
Current SRL research suggests that who is in charge of implementing the self-
assessment intervention may influence the outcome. Due to the low number of studies
in which the teacher was the implementation agent, this variable could only be used in
relation to self-efficacy.
2.3 Statistical methods
To estimate the effects of the self-assessment interventions Cohen’s d was used,
or standardized mean difference (Cohen, 1988), dividing the difference between the
means by the pooled standard deviation. Effect sizes were coded such that positive

SELF-ASSESSMENT EFFECTS ON SRL AND SE
23
values indicate better performance for the groups with self-assessment, as compared to
the groups without self-assessment. As the standard deviation was not reported in two
of the studies (Olina & Sullivan, 2002, 2004), it was estimated from other statistics in
the papers. In addition, in one of these studies (Olina & Sullivan, 2004) the sample size
of one of the four conditions was not reported. Equal size for the four groups was
therefore assumed.
Meta-analyses were conducted assuming a random-effects model. A random
(rather than a fixed) effects model was chosen for two main reasons (Borenstein,
Hedges, Higgins, & Rothstein, 2010). First, a random effects model allows for
generalizing the results of the effect of self-assessment beyond the specific set of studies
included here. Second, random effects models are more conservative concerning
statistical inferences as compared to fixed effects models. Additionally, the inter-study,
or specific, variance was estimated by the maximum likelihood method.
Statistical analyses of the heterogeneity were performed with Review Manager
(2012), the SPSS macros by Lipsey and Wilson (2001), and the R package METAFOR
(Wichtbauer, 2010). The forest plots were obtained with Review Manager (2012).
Combined estimates of the effect size indices weighting were obtained by the inverse of
variance method. The procedures provided by Hedges and Olkin (1985) were employed
for analyzing the sample of effect size values: Q test for homogeneity, Qb for
categorical moderators, and QR for continuous moderators.
2.4 Publication bias
Initially, the threat of publication bias was intended to be explored for all four
meta-analyses of the main dependent variables. However, the small number of studies
in two of them (Negative SRL k = 6, and SRL measured qualitatively k = 3) hindered
any reasonable use of the tests for asymmetry. Therefore, only the two other meta-

SELF-ASSESSMENT EFFECTS ON SRL AND SE
24
analyses were included in the publication bias analysis. Publication bias was explored
analyzing deviations from symmetry in the funnel plot (Light & Pillemer, 1984). We
also calculated the Orwin’s (1983) fail-safe number.
Regarding the meta-analysis of Learning SRL (k=12) the Orwin fail-safe
number is Nfs = 31. Therefore, with a number of 31 estimates of effect sizes not
published, and with an average zero effect size added to our meta-analysis, the
combined effect would statistically be zero. According to the criterion proposed by
Rosenthal (1979), the value for tolerance is 12·5+10 = 70. As this value is higher than
Nfs it cannot be excluded that the observed effect is due to publication bias. However,
both the visual inspection of the funnel plot (Figure 2) and the tests applied suggest that
there is no publication bias in this set of studies (Kendall tau test, p =. 534; Egger test, p
= .44), as symmetry could be assumed based on the funnel plot and the Kendall and
Egger tests.
>>> Figure 2 about here <<<
Regarding the self-efficacy meta-analysis (k = 27) the Orwin fail-safe number is
Nfs = 488. The Rosenthal’s value for tolerance is 27·5+10 = 145. As this value is lower
than Nfs we can exclude that the whole observed effect is due to publication bias.
However, both the visual inspection of the funnel plot (figure 3) and the tests applied,
may indicate some publication bias (Kendall tau test, p = .0001; Egger test, p = .0001).
Visual inspection of the funnel plot suggests that two of the d values could be
considered as outliers (Kitsantas & Zimmerman, 1998; b & d effect sizes). A sensibility
test, excluding those two estimates, was performed, showing that Kendall’s test is still
significant (p = .0021), but not the Egger’s test (p = .3298). In short, although there is

SELF-ASSESSMENT EFFECTS ON SRL AND SE
25
no threat of publication bias for the existence of a true effect for self-efficacy, there
might exist some over-estimation of the magnitude of the effect.
>>> Figure 3 about here <<<
3. Results
This section is organized around the four dependent variables as outlined above:
Learning SRL, Negative SRL, SRL measured qualitatively, and self-efficacy. For each
of the variables the influence of self-assessment is presented, then the effects from the
moderating variables (i.e. gender, age/educational level, self-assessment intervention,
and implementation agent). Since a minimum of k=3 was considered necessary for
sufficiently consistent results from the meta-analyses, groups with k=1 were not run.
Furthermore, groups with k=2 are reported (highlighted in grey in the tables), but not
discussed in the text.
3.1 Effects of self-assessment on Learning SRL
Figure 4 presents the forest plot for effect sizes from self-assessment
interventions in relation to Learning SRL. Data comes from 12 effect sizes and 369
participants, as well as from studies with different designs (i.e., experimental and quasi-
experimental). The combined effect size is d = 0.23, 95% CI [0.08 - 0.39]. These results
support the existence of a positive effect of self-assessment on SRL. It is important,
however, to notice that the effect size is relatively small.
>>> Figure 4 about here <<<
3.1.1 Moderating variables

SELF-ASSESSMENT EFFECTS ON SRL AND SE
26
As can be seen in Table 4, none of the effects of the moderating variables were
statistically significant. Therefore, according to the current empirical evidence Learning
SRL seems to be unaffected by these moderating variables.
>>> Table 4 about here <<<
3.2 Effects of self-assessment on Negative SRL
Figure 5 presents the forest plot for effect sizes from self-assessment
interventions in relation to Negative SRL. Data comes from 6 effect sizes and 347
participants, as well as from studies with mostly experimental design (one study with
quasi-experimental design). The combined effect size is d = -0.65, 95% CI [-1.52 -
0.22]. It should be kept in mind that Negative SRL represents self-regulatory actions
thought to be detrimental for learning. In other words, lower values in relation to this
variable should be interpreted as positive for students’ learning. Consequently, these
results support the positive effect of self-assessment on SRL. As compared to the effects
of self-assessment on Learning SRL, this effect size is somewhat larger (i.e. medium
effect size).
>>> Figure 5 about here <<<
3.2.1 Moderating variables
As can be seen in Table 5, none of the effects of the moderating variables were
statistically significant. However, it is noteworthy that two of them approached
statistical significance, despite the low number of studies reporting on this SRL
variable.
>>> Table 5 about here <<<
3.3 Effects of self-assessment on SRL measured qualitatively
Figure 6 presents the forest plot for effect sizes from self-assessment
interventions in relation to the SRL measured qualitatively variable. Data comes from 3

SELF-ASSESSMENT EFFECTS ON SRL AND SE
27
effect sizes and 338 participants, as well as from studies with either experimental or
quasi-experimental design. The combined effect size is d = 0.43, 95% CI [-0.17 - 1.03].
Similar to the effects of self-assessment on Negative SRL, this is a medium effect size.
These results are promising in further supporting the positive effect of self-assessment
on SRL. However, this result needs more statistical power as only three studies were
included in the current meta-analysis. Further research using this type of SRL
measurement will be key for the field, as the results are encouraging and even a few
additional studies would make a significant contribution.
>>> Figure 6 about here <<<
3.3.1 Moderating variables
As can be seen in Table 6, none of the effects of the moderating variables were
statistically significant. It is vital to remember that the number of studies exploring this
SRL variable was particularly low (k = 3), which affects the power of the tests.
>>> Table 6 about here <<<
3.4 Effects of self-assessment on self-efficacy
Figure 7 presents the forest plot for the 27 effect sizes of self-assessment
interventions for self-efficacy. The combined effect size is d = 0.73, 95% CI [0.39 -
1.08]. These results support the positive effect of self-assessment on self-efficacy. As
compared to the effects of self-assessment on the SRL variables, this effect size is
larger.
>>> Figure 7 about here <<<
3.4.1 Moderating variables
As can be seen in Table 7, the moderating effect of gender [Q(1) = 5.271; B = ;
p = 0.026] and two of the self-assessment intervention categories (monitoring and self-

SELF-ASSESSMENT EFFECTS ON SRL AND SE
28
assessment instrument) were statistically significant. The first finding suggests that self-
assessment interventions may have a larger effect on girls’ self-efficacy, as compared to
boys’. The second finding suggests that monitoring is a powerful component of self-
assessment in terms of promoting students’ self-efficacy (d = 1.456) and that not using a
self-assessment instrument might increase self-efficacy more.
>>> Table 7 about here <<<
4. Discussion
This meta-analytic review has explored the effects of self-assessment interventions
on students’ SRL strategies and self-efficacy, along with moderating variables assumed
to have an impact on such effects. The interventions to promote self-assessment were
shown to have a positive effect on students’ SRL and, to a higher extent, on students’
self-efficacy. Furthermore, two of the moderating variables, gender and self-assessment
components, were shown to have differential effects on students’ self-efficacy.
4.1 The effect of self-assessment interventions on students’ self-regulated learning
The results suggest that self-assessment interventions have a positive effect on
students’ SRL strategies. These effects range from small to medium in the three meta-
analyses (0.23, 0.65, and 0.43).
The findings differ from previous narrative reviews on self-assessment, such as
Topping (2003) and Brown and Harris (2013), which have concluded that the evidence
supporting the relationship between self-assessment and SRL were not robust at that
time. However, the findings from the present study are based on a more solid meta-
analytic methodology, as well as a larger sample of studies specifically targeting effects
on SRL strategies.
Besides the general effect, there are two other findings that warrant further
attention. First, the different SRL measurements provide differential effects. Second,

SELF-ASSESSMENT EFFECTS ON SRL AND SE
29
there are no statistically significant effects of the moderating variables investigated.
These findings are discussed further below.
4.1.1. Differences between self-regulated measurements
A key contribution of this study is the distinction made between different SRL
measurements. A similar approach has been reported by Dignath et al. (2008), who used
different measurements as moderators (p. 188 Table 15). It is, however, difficult to
compare the results from these two meta-analyses since: (1) The studies included in this
review mainly used questionnaires, while Dignath et al. (2008) included studies that
used, for example, multiple-choice tests or simulation tasks; (2) Dignath et al. (2008)
did not include studies investigating Negative SRL; and (3) this review only includes
three studies measuring SRL qualitatively, which is not sufficient to make any
comparisons with the “other” category in Dignath et al. (2008).
As shown in the results section, although the different SRL measurements point
in the same direction, there are differences in the magnitude of the effect sizes. In the
case of SRL measured qualitatively, with only three effect sizes included, more research
is needed in order to draw any firm conclusions. However, in relation to Learning SRL
the findings suggest that self-assessment interventions may increase positive self-
regulatory actions oriented towards learning goals. This effect seems to be a
consequence of self-assessment helping students to deepen their knowledge about the
learning goals, how to monitor their progress, and to evaluate and correct themselves
when needed (Panadero & Alonso-Tapia, 2013). Furthermore, self-assessment is seen to
decrease negative self-regulatory actions, as found in relation to Negative SRL. This
indicates that, by being aware of the learning goals, as well as how to monitor and
evaluate own performance in relation to them, students might for instance experience
less fear of failure.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
30
It is important to explore the reasons for the larger effect of self-assessment
when SRL is measured via qualitative data (0.43), as compared to questionnaires (0.24),
in more detail. The use of self-reported questionnaires for measuring SRL has been
contested (Boekaerts & Corno, 2005; Veenman, 2011), based on the limitations of
asking the students to accurately report their actions when: (a) they have been
concentrating on a task and were more or less unable to record their actions; (b) they do
not have any standard to compare their SRL strategies against, especially when it
comes to Likert-scales (i.e. how are students supposed to know how much “very much”
or “very often” is without comparing to a standard or to other students?) (Sitzmann et
al., 2010); and (c) there is a general tendency of inaccuracy in such measurement
situations (Eva & Regehr, 2005). In addition, some of the studies included in this meta-
analytic review used general context SRL questionnaires, which do not fully capture the
effect of specific interventions (Alonso-Tapia et al, 2014; Boekaerts & Corno, 2005).
Given the abovementioned limitations of self-reported measurements, together
with the tendency for students to overestimate their SRL (Boekaerts & Corno, 2005),
the findings from the Learning SRL variable might actually be lower than expected,
providing an explanation for the much larger combined effect size for SRL measured
qualitatively. The data coming from the three SRL measured qualitative studies may in
some respects be considered more valid, as compared to questionnaire data, since it is
more direct (i.e., provided by the students’ actions without being “filtered” through the
questionnaire) and also more sensitive to the specific context. If comparing the two
studies that included a combination of qualitative and self-reported questionnaire data
(Panadero et al., 2012; Panadero & Romero, 2014), a similar pattern is found. Panadero
and Romero (2014) used an open question providing self-reported qualitative data and
Panadero et al. (2012) used thinking aloud protocols. Results from questionnaire data

SELF-ASSESSMENT EFFECTS ON SRL AND SE
31
did not reach statistical significance in any of the studies, while results from the
qualitative data did.
As mentioned before, this means that there might be problems with the validity
and accuracy when using questionnaires to measure specific SRL interventions and
students’ inability to accurately report their SRL strategies via questionnaires (Alonso-
Tapia et al., 2014; Boekaerts & Corno, 2005; Roth et al, 2016). Qualitative data may
therefore have provided a more valid picture of students’ actual use of SRL strategies in
those two studies. Consequently, forthcoming studies using data other than Learning
SRL questionnaires, could be expected to report higher effect sizes.
4.1.2. Moderating effects on self-regulated learning
The influence of the following moderating variables was explored: Gender,
age/educational level, self-assessment interventions, and implementation agent. As seen
by the results, none of these variables had a statistically significant impact on students’
SRL strategies. However, the limited number of studies included in these meta-analyses
makes it difficult to definitely rule out the possible influence of these variables.
Notably, for Negative SRL the effects of gender and age were close to statistical
significance. Here, however, we will make a conservative interpretation of the results
and therefore only mention that there seems to be a tendency for boys and younger
students to experience less Negative SRL as a consequence of self-assessment
interventions.
Previous meta-analyses on SRL (Dignath & Büttner, 2008; Dignath et al., 2008;
Hattie et al., 1996; Sitzmann & Ely, 2011) have not addressed gender effects, which
means that the findings cannot be compared to any similar research. However, gender
differences in SRL could have been expected since such differences have been reported
in individual studies on SRL, as well as in reviews on self-assessment.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
32
Regarding age and educational level, previous meta-analyses have found
differential effects of educational level, where for instance secondary education students
benefited more from SRL interventions in terms of an increased use of SRL strategies,
as compared to students in primary school (Dignath & Büttner, 2008). Since age and
educational level did not produce statistically significant effects in this meta-analysis,
the results do not align with the findings reported by Dignath and Büttner (2008).
Furthermore, since Dignath and Büttner (2008) did not investigate Negative SRL, it is
not possible to say whether the tendency for younger students to experience less
Negative SRL as a consequence of self-assessment interventions is in conflict with their
findings or not.
The third moderator was type of self-assessment intervention, which is also an
aspect not previously explored in SRL meta-analyses. As suggested by Brown and
Harris (2013), the effects of self-assessment interventions on student learning might
depend more on the comprehensiveness of the interventions than on the type of self-
assessment. However, the results show no effects of the type of self-assessment
intervention on students’ SRL. This is not to say that all self-assessment interventions
are equally effective, just that the ones explored here had similar effects for SRL
strategies. It should also be noted that these results only refer to Learning SRL, since
there were too few studies in the other SRL categories.
This finding is of particular importance for research on the use of rubrics as an
instrument for facilitating self-assessment. It has been suggested that the use of rubrics
might decrease negative SRL strategies, while supporting student self-regulation
(Brookhart, 2014; Panadero & Jonsson, 2013). According to Panadero and Jonsson
(2013), as well as Jonsson (2014), the transparency provided by explicit criteria can
support students’ self-regulation by helping them to set realistic goals, as well as to

SELF-ASSESSMENT EFFECTS ON SRL AND SE
33
monitor and self-evaluate their performance. However, the present meta-analytic review
does not support the assumption that rubrics would have a larger impact on Learning
SRL as compared to other self-assessment interventions. It should be noted, however,
that the effects of rubrics on Negative SRL could not be explored due to the limited
number of empirical studies on this topic.
There are two possible explanation for the unexpected finding that rubrics do not
have a larger impact on Learning SRL as compared to other self-assessment
interventions. Firstly, the most specific explanation is provided by Panadero and
Romero (2014), who discuss the negative effect of the rubric used in their study.
Students had a limited time to complete the task (one hour) and the rubric might have
increased students’ feelings of working under pressure by making the expectations
explicit (including the highest standard). Additionally, the task counted for the final
grade, which was also made explicit and more evident by the rubric. According to the
authors, rubrics might therefore need a longer interventions in order to avoid inducing
feelings of stress among the students. Secondly, research on the effects of using rubrics
for formative purposes differs greatly regarding the quality of the interventions, as well
as the quality of the research (Panadero & Jonsson, 2013), both of which may affect the
outcome of this meta-analytic review.
The last moderator was implementation agent. However, no conclusions can be
drawn, due to the low number studies in which the teachers implemented the self-
assessment interventions. Therefore, the findings from previous meta-analyses, where
the SRL interventions benefited from being implemented by the researcher (Dignath &
Büttner, 2008, Hattie et al., 1996), should be taken into consideration for future self-
assessment research.
4.2 The effect of self-assessment interventions on students’ self-efficacy

SELF-ASSESSMENT EFFECTS ON SRL AND SE
34
As suggested by Schunk (1996), having students assess their progress makes it
clear to them that they have become more competent, which in turn strengthens their
self-efficacy. This assertion is supported by the results from our self-efficacy meta-
analysis, where self-assessment interventions are seen to have a substantial impact on
students’ self-efficacy (d = 0.73). The magnitude of this effect size is comparable to the
effect of feedback on students’ achievement, as reported by Hattie (2009). The results
also come from a larger set of effect sizes (k = 27), as compared to our SRL meta-
analyses. The findings from the self-efficacy analyses therefore imply that self-
assessment interventions can be a powerful strategy to increase students’ confidence in
their own capabilities. This, in turn, is important since self-efficacy is one of the major
predictors of student performance (Honicke & Broadbent, 2016; Richardson et al.,
2012; Robbins et al., 2004; Sitzmann & Ely, 2011).
The influence from self-assessment interventions on students’ self-efficacy is
two-folded. As self-assessment provides the student with information about the learning
goals and how to progress towards them (Andrade, 2010; Panadero & Alonso-Tapia,
2013), training in self-assessment may make students feel more confident (Schunk,
1996). Furthermore, as task performance is likely to improve as a result of self-
assessment training (Brown & Harris, 2013), confidence may increase even further.
4.2.1 Comparing self-efficacy and self-regulated learning effect sizes
An interesting observation is that there is a considerable difference between the
size of the observed effects from the three SRL type of measurement and self-efficacy.
One possible explanation for this difference is offered by Sitzmann et al. (2010). From
their meta-analysis on different outcomes of self-assessment, they concluded that self-
assessment may be best characterized as an affective evaluation outcome. If the results
from the current study are organized from the largest combined effect to the lowest,

SELF-ASSESSMENT EFFECTS ON SRL AND SE
35
they come in the following order: self-efficacy (0.73), Negative SRL (0.65), SRL
measured qualitatively (0.43), and Learning SRL (0.24). By going one step further and
analyzing the underlying constructs of, and data for, the different variables, the
following pattern emerges: (a) self-efficacy is a personal belief mediated by cognitive,
but also motivational and affective, processes (Bandura, 1997, chapter 4); (b) Negative
SRL is largely a measure of how emotions (e.g. anxiety) influence SRL strategies; (c) of
the three studies providing data for the SRL measured qualitatively variable, only one
used cognitive measures (Goodrich, 1996, p. 49), while emotion regulation was
included in one of the others (Panadero et al., 2012); and (d) data for Learning SRL is a
diverse combination of multiple instruments (such as the MSLQ and the EMSR-Q)
including cognitive, motivational, and emotional aspects. A tentative explanation for the
observed pattern of combined effect sizes could therefore be that affective measures are
influenced more by self-assessment as compared to cognitively oriented measures.
Another possible explanation for the observed pattern among effect sizes, is that
it might be an artefact produced by instrument design. In relation to self-efficacy,
students are asked whether they feel capable of performing a particular task. self-
efficacy instruments typically make use of projective scales or items asking for personal
opinions or perceptions that are future-oriented. The MSLQ, which is the most
frequently used tool for measuring both SRL (Ogrin et al., 2016) and self-efficacy
(Honicke & Broadbent, 2016), includes self-efficacy items such as: “I believe I will
receive an excellent grade in this class” or “I’m certain I can master the skills being
taught in this class”
5
. Items that measure (meta-)cognitive aspects of SRL, on the other
hand, are usually more precise and oriented towards frequency of behavior, often in
present or past tense. Going back to the MSLQ, examples of items are: “I make simple
5
There are two other MSLQ scales using this type of items that are also related to affective outcomes:
Task value and Expectancy components.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
36
charts, diagrams, or tables to help me organize course material” or “I work hard to do
well in this class even if I don’t like what we are doing”. These differences between
instruments for affective and cognitive measures could therefore create a differential
effect, based on instrument design, where the level of uncertainty in future-oriented
items could be higher (“How capable do you think you are to perform this in the
future?”), as compared to more precise past-oriented questions (“What was your level of
concentration in the task you just performed?”). In sum, this calls for future research to
analyze the design of SRL and self-efficacy instruments, in order to explore whether
this construct irrelevant variance exists and might contribute to the observed
differences.
4.2.2 Moderating effects on self-efficacy
Two of the four moderators analyzed have a statistically significant impact on
students’ self-efficacy: gender and self-assessment intervention type. First, the findings
suggest that girls’ self-efficacy increases more as a result of self-assessment
interventions, as compared to boys’. Although most studies included in this meta-
analysis had a sample of both boys and girls, only a few explored gender differences.
Consequently, the selected studies offer very little in terms of hypotheses about why
boys and girls are affected differently. An exception is Andrade et al. (2009), who
suggest that by using rubrics for self-assessment, girls may feel more successful since
there are no risk of debilitating external feedback or social comparisons.
In Huang’s meta-analysis (2013) a small difference favoring males’ self-efficacy
(d = 0.08) was found. This difference was explained by moderator analysis,
demonstrating that content domain contributed significantly to the variation in effect
sizes. While females displayed higher self-efficacy in relation to language arts, males’
self-efficacy was higher in mathematics, computer, and social sciences. The influence

SELF-ASSESSMENT EFFECTS ON SRL AND SE
37
of content domain has not been investigated in this meta-analysis, but as can be seen in
our Table 1, there is an even distribution of subjects that might favor either gender.
Second, no significant effects were found in this meta-analytic review regarding
age/educational level, and no previous meta-analyses have been found exploring this
aspect in relation to self-efficacy. An important implication of these findings is that self-
assessment interventions might have an equally powerful effect on students’ self-
efficacy across educational levels.
Third, regarding self-assessment interventions, our findings do not support the idea
that more comprehensive self-assessment interventions, or intervention using self-
evaluation or feedback and revision components, result in larger effects on students’
self-efficacy. However, it was found that both monitoring and self-assessment
instrument components have differential effects. Regarding monitoring, it was found
that studies including monitoring reported an increase in self-efficacy. A viable
hypothesis for this unexpected finding might lie in the research design of such
monitoring studies as they tend to focus on performances that provide immediate
feedback that is easy to interpret. For example, in Zimmerman and Kitsantas (1996) the
performance investigated is dart throwing, which means that the participants can easily
see their scores on the board immediately after releasing the dart. Similarly, in Ramdass
and Zimmerman (2008) the students were taught how to check their answers in division
tasks in mathematics. In line with this hypothesis, Sitzmann et al. (2010) make a
distinction between interpersonal and psychomotor tasks on the one hand and cognitive
tasks on the other, because in the former case students can observe whether their actions
were successful, but not in the latter. The students could therefore easily see whether
their answers were correct or not, without making any interpretations or assessments
according to quality criteria. This situation can be compared to other self-assessment

SELF-ASSESSMENT EFFECTS ON SRL AND SE
38
interventions, involving more complex performance, such as classifying arthropods
(Goodrich, 1996) or performing a landscape analysis (Panadero et al., 2012). In these
latter cases, it is not immediately obvious for the students to what extent they have
succeeded on the task, since any feedback (internal or external) must be grounded in an
assessment of the performance according to criteria, which is more complex than
monitoring performances that can be categorized as either right or wrong. The second
self-assessment intervention that shown significant effects was the use of self-
assessment instruments (e.g. rubrics, scripts). Participants using such instruments
reported lower self-efficacy after the intervention than participants not using them. A
viable hypothesis is that, as these instruments make the assessment criteria and
standards explicit, students become aware of the complexity of high quality
performance and, therefore, report lower self-efficacy. Future research should clarify
whether students using a self-assessment instrument have a more realistic perception of
their capacity, as compared to students who do not know the criteria or standards in
such detail. Importantly, this finding contradicts the proposition that the use of rubrics
improves students’ self-efficacy (Panadero & Jonsson, 2013). The main explanations
for these conflicting findings are that: a) in 2013, a lower number of studies exploring
this effect was found, and b) in the present review, using meta-analytic methodology,
the effect, as well as the direction of the effect, can be determined with greater
accuracy.
Fourth, regarding the implementation agent, no significant effects were found and
no previous meta-analyses have been found exploring this aspect.
4.3 Future lines of research
While the results of this meta-analytic review may be considered important for
the field, the selected studies do not clarify the exact mechanisms for how self-

SELF-ASSESSMENT EFFECTS ON SRL AND SE
39
assessment interventions influence students’ SRL strategies and self-efficacy.
Consequently, research is needed, which investigate the actual processes of self-
assessment and SRL more closely. As was shown in the introduction, self-assessment
may influence all phases of the SRL cycle, including the planning phase. Still, since
there are different forms of self-assessment, as well self-assessment interventions with
different components, future research also needs to be more specific with regards to
self-assessment typologies, in order to advance our understanding of how self-
assessment interventions impact on students’ SRL (Panadero et al., 2016).
A second line of future research relates to the discussion emerging from the
study by Sitzmann et al. (2010). It needs to be clarified whether self-assessment
interventions benefit affective outcomes more than cognitive ones, while at the same
time ruling out the hypothesis that this finding is an artifact created by the measurement
instrument design. Also, even if self-assessment would primarily influence affective
outcomes, such as self-efficacy, it is still likely to have an effect on cognitive aspects,
for instance since self-efficacy is one of the strongest predictors of student GPA
(Richardson et al., 2012).
Another suggestion for future research, which relates to the discussion above,
comes from the fact that there has not yet been any sufficiently fine-grained
measurements separating the different SRL components. In the light of the findings
from this review, that the effect of self-assessment is different for SRL and self-efficacy
variables, it might be hypothesized that self-assessment does not have the same level of
impact for all SRL processes.
An obvious recommendation, based on the findings, is that upcoming self-
assessment research exploring the effects on students’ SRL strategies should move
away from using only self-reported data from questionnaires (see also Samuelstuen &

SELF-ASSESSMENT EFFECTS ON SRL AND SE
40
Bråten, 2007). Instead, it would be recommended to include more direct SRL
measurements, such as thinking-aloud protocols, observation, etc. A broader base of
different data, including a combination of intervention and measurement (Panadero et
al., 2015), is likely to enhance our understanding of the SRL processes.
Finally, more research is needed to explain the possible causes for the larger impact
of self-assessment on girls’ self-efficacy.
4.5 Educational implications
The major educational implication from this meta-analytic review is that, as
pointed out by Sadler (1989), Boud (1995), and Black and Wiliam (1998), self-
assessment is a necessity for productive learning. self-assessment has been shown to
have a positive influence on academic performance (Brown & Harris, 2013) and
according to the results in our review, self-assessment also has a positive influence on
students’ SRL strategies and self-efficacy. We therefore re-iterate that self-assessment
needs to be a major instructional aim in every classroom, but not only for the sake of
improving performance. Since self-assessment interventions has an impact on students’
SRL and self-efficacy, self-assessment needs to be implemented for the sake of
students’ empowerment and self-sustained learning.
Unfortunately, it is not possible from the results of this meta-analytic review to
identify which self-assessment components, or combinations of components, are the
most effective in terms of fostering SRL strategies or self-efficacy. Even though
findings suggest that monitoring is an effective self-assessment component for
increasing students’ self-efficacy, most studies in the sample using monitoring rely on
performances that provide immediate feedback that is easy to interpret. It is therefore
not necessarily the process of monitoring that is effective in fostering increasing

SELF-ASSESSMENT EFFECTS ON SRL AND SE
41
students’ self-efficacy, but the immediate and easily interpretable feedback that affects
students’ perceptions of their capability.
5. Conclusion
The findings from this meta-analytic review suggest that self-assessment
interventions have a positive influence on students’ SRL strategies and self-efficacy.
Importantly, the magnitude of these positive effects differ between SRL measurement
types, implying that the role of SRL measurement needs to be carefully considered in
upcoming research. The present review also shows that some moderating variables,
such as gender and certain self-assessment components, influence the effects on
students’ self-efficacy. Specifically, self-assessment interventions have a larger impact
on girls’ self-efficacy, as compared to boys’, and self-monitoring has a larger impact on
students’ self-efficacy as compared to the other self-assessment components
investigated in this study. All in all, this review supports the idea that self-assessment
interventions may have a positive influence on students’ SRL and self-efficacy,
although we still need to increase our corpus of knowledge about the actual
mechanisms for these effects.
6. References
(Marked with an * the studies included in the meta-analyses).
*Alishah, A. R., & Dolmaci, M. (2013). The interface between self-efficacy concerning
the self-assessment on students studying English as a foreign language. Paper
presented at the Akdeniz Language Studies Conference 2012.
http://www.sciencedirect.com/science/article/pii/S1877042813001341

SELF-ASSESSMENT EFFECTS ON SRL AND SE
42
Alonso-Tapia, J., Panadero, E., & Ruiz, M. A. (2014). Development and validity of the
Emotion and Motivation Self-regulation Questionnaire (EMSR-Q). Spanish
Journal of Psychology, 17(e55), 1-15. doi:10.1017/sjp.2014.41
Andrade, H. (2010). Students as the definitive source of formative assessment:
Academic self-assessment and the self-regulation of learning. In H. J. Andrade
& G. J. Cizek (Eds.), Handbook of formative assessment (pp. 90-105). New
York: Routledge.
Andrade, H., Du, Y., & Wang, X. (2008). Putting rubrics to the test: The effect of a
model, criteria generation, and rubric-referenced self-assessment on elementary
school students’ writing. Educational Measurement: Issues and Practices,
27(2), 3-13.
Andrade, H., & Valtcheva, A. (2009). Promoting learning and achievement through
self-assessment. Theory Into Practice, 48(1), 12-19.
doi:10.1080/00405840802577544
*Andrade, H., Wang, X. L., Du, Y., & Akawi, R. L. (2009). Rubric-referenced self-
assessment and self-efficacy for writing. Journal of Educational Research,
102(4), 287-301.
*Baleghizadeh, S., & Masoun, A. (2014). The effect of self-assessment on EFL
learners’ self-efficacy. TESL Canada Journal, 31(1), 42.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.
Psychological Review, 84(2), 191-215. doi:10.1037/0033-295X.84.2.191
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory.
Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman
and Company.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
43
Black, P., Harrison, C., Lee, C., Marshall, B., & William, D. (2003). Assessment for
learning: Putting it into practice. Berkshire, England: Open University Press.
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in
Education: Principles, Policy and Practice, 5(1), 7-73.
doi:10.1080/0969595980050102
Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on
assessment and intervention. Applied Psychology, 54(2), 199-231.
doi:10.1111/j.1464-0597.2005.00205.x
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2010). A basic
introduction to fixed-effects and random-effects models for meta-analysis.
Research Synthesis Methods, 1, 97-111. doi:10.1002/jrsm.12
Boud, D. (1995). Enhancing learning through self-assessment. New York:
RoutledgeFalmer.
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.
Brown, G. T. L., & Harris, L. R. (2014). The future of self-assessment in classroom
practice: Reframing self-assessment as a core competency. Frontline Learning
Research, 3(2014), 22-30. doi:10.14786/flr.v2i1.24
Bussey, K. (2011). The influence of gender on students' self-regulated learning and
performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-
regulation of learning and performance (pp. 426-441). New York: Routledge.
Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). New
York: Academic Press.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
44
*Coronado-Aliegro, J. (2007). The effect of self-assessment in the self-efficacy of
students studying Spanish as a foreign language. (PhD), University of
Pittsburgh.
*DiGiacomo, G. (2014). Enhancing self-monitoring and self-reflection through a self-
regulatory skills intervention embedded in a middle school mathematics
curriculum. (PhD), City University of New York.
Dignath, C., Büttner, G., & Langfeldt, H. (2008). How can primary school students
learn self-regulated learning strategies most effectively? A meta-analysis on
self-regulation training programmes. Educational Research Review, 3(2), 101-
129. doi:10.1016/j.edurev.2008.02.003
Eva, K. W., & Regehr, G. (2005). Self-assessment in the health professions: A
reformulation and research agenda. Academic Medicine, 80(10), S46-S54.
Goodrich Andrade, H., & Boulay, B. A. (2003). Role of rubric-referenced self-
assessment in learning to write. The Journal of Educational Research, 97(1), 21-
34. doi:10.1080/00220670309596625
*Goodrich, H. W. (1996). Student self-assessment: At the intersection of metacognition
and authentic assessment. (PhD), Harvard University, US.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to
achievement. London: Routledge.
Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL:
Academic Press.
Jonsson, A. (2014). Rubrics as a way of providing transparency in assessment.
Assessment & Evaluation In Higher Education, 1-13.
doi:10.1080/02602938.2013.875117

SELF-ASSESSMENT EFFECTS ON SRL AND SE
45
*Kahrizi, P., Farahian, M., & Rajabi, S. (2014). The impact of self-assessment on self-
regulation and critical thinking of EFL learners. Modern Journal of Language
Teaching Methods, 4(1), 353.
*Kitsantas, A., Reiser, R. A., & Doster, J. (2004). Developing self-regulated learners:
Goal setting, self-evaluation, and organizational signals during acquisition of
procedural skills. The Journal of Experimental Education, 72(4), 269-287.
*Kitsantas, A., & Zimmerman, B. J. (1998). Self-regulation of motoric learning: A
strategic cycle view. Journal of Applied Sport Psychology, 10(2), 220-239.
doi:10.1080/10413209808406390
Lan, W. Y. (1996). The effects of self-monitoring on students’ course performance, use
of learning strategies, attitude, self-judgement ability, and knowledge
representation. The Journal of Experimental Education, 64(2), 101-115.
Light, R. J., & Pillemer, D. B. (1984). Summing up. The science of reviewing research.
Cambridge, MA: Harvard University Press.
Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA:
Sage.
*Mahlberg, J. (2015). Formative self-assessment college classes improves self-
regulation and retention in first/second year community college students.
Community College Journal of Research and Practice, 1-12.
doi:10.1080/10668926.2014.922134
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2009). Preferred
reporting items for systematic reviews and meta-analyses: the PRISMA
statement. Annals of internal medicine, 151(4), 264-269.
doi:10.1371/journal.pmed1000097

SELF-ASSESSMENT EFFECTS ON SRL AND SE
46
*Nbina, J. B., & Viko, B. (2010). Effect of instruction in metacognitive self-assessment
strategy on chemistry students’ self-efficacy and achievement. Academia Arena,
2(1), 1-10.
Nicol, D., & McFarlane-Dick, D. (2006). Formative assessment and self-regulated
learning, a model and seven principles of good feedback practice. Studies in
Higher Education, 31(2), 199-218. doi:10.1080/03075070600572090
*Olina, Z., & Sullivan, H. J. (2002). Effects of classroom evaluation strategies on
student achievement and attitudes. Educational Technology, Research and
Development, 50(3), 61-75. doi:10.1007/BF02505025
*Olina, Z., & Sullivan, H. J. (2004). Student self-evaluation, teacher evaluation, and
learner performance. Educational Technology Research and Development,
52(3), 5-22. doi:10.1007/BF02504672
Orwin, R. G. (1983). A fail-safe N for effect size in meta-analysis. Journal of
educational statistics, 157-159.
Panadero, E. (2011). Instructional help for self-assessment and self-regulation:
Evaluation of the efficacy of self-assessment scripts vs. rubrics. (Ph.D.),
Universidad Autónoma de Madrid, Spain.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions
for research. Frontiers in Psychology, 8(422). doi:10.3389/fpsyg.2017.00422
Panadero, E., & Alonso-Tapia, J. (2013). Self-assessment: Theoretical and practical
connotations. When it happens, how is it acquired and what to do to develop it
in our students. Electronic Journal of Research in Educational Psychology,
11(2), 551-576. doi:http://dx.doi.org/10.14204/ejrep.30.12200
*Panadero, E., Alonso-Tapia, J., & Huertas, J. A. (2012). Rubrics and self-assessment
scripts effects on self-regulation, learning and self-efficacy in secondary

SELF-ASSESSMENT EFFECTS ON SRL AND SE
47
education. Learning and Individual Differences, 22(6), 806-813.
doi:10.1016/j.lindif.2012.04.007
*Panadero, E., Alonso-Tapia, J., & Reche, E. (2013). Rubrics vs. self-assessment scripts
effect on self-regulation, performance and self-efficacy in pre-service teachers.
Studies In Educational Evaluation, 39(3), 125-132.
doi:10.1016/j.stueduc.2013.04.001
Panadero, E., Brown, G. T. L., & Strijbos, J. W. (2016). The future of student self-
assessment: A review of known unknowns and potential directions. Educational
Psychology Review. doi:10.1007/s10648-015-9350-2
Panadero, E., Jonsson, A., & Strijbos, J. W. (2016). Scaffolding self-regulated learning
through self-assessment and peer assessment: Guidelines for classroom
implementation. In D. Laveault & L. Allal (Eds.), Assessment for Learning:
Meeting the challenge of implementation (pp. 311-326). New York: Springer.
Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics for formative assessment
purposes revisited: A review. Educational Research Review, 9(0), 129-144.
doi:http://dx.doi.org/10.1016/j.edurev.2013.01.002
Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-
regulated learning field: When measurement and intervention come hand in
hand. Scandinavian Journal of Educational Research, 60(6), 723-735.
doi:10.1080/00313831.2015.1066436
*Panadero, E., & Romero, M. (2014). To rubric or not to rubric? The effects of self-
assessment on self-regulation, performance and self-efficacy. Assessment in
Education: Principles, Policy & Practice, 21(2), 133-148.
doi:10.1080/0969594X.2013.877872

SELF-ASSESSMENT EFFECTS ON SRL AND SE
48
Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated
learning. Educational Psychologist, 36(2), 89-101.
doi:10.1207/S15326985EP3602_4
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M.
Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp.
452-502). San Diego, CA: Academic Press.
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for
the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
*Ramdass, D., & Zimmerman, B. J. (2008). Effects of self-correction strategy training
on middle school students' self-efficacy, self-evaluation, and mathematics
division learning. Journal of Advanced Academics, 20(1), 18-41.
doi:10.4219/jaa-2008-869
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of
university students' academic performance: A systematic review and meta-
analysis. Psychological Bulletin, 138(2), 353-387. doi:10.1037/a0026838
Rosenthal, R. (1979). The file drawer problem and tolerance for null results.
Psychological Bulletin, 86(3), 638.
Sadler, D. R. (1989). Formative assessment and the design of instructional systems.
Instructional Science, 18(2), 119-144. doi:10.1007/bf00117714
Samuelstuen, M. S., & Bråten, I. (2007). Examining the validity of self-reports on
scales measuring students' strategic processing. British Journal of Educational
Psychology, 77(2), 351-378. doi:10.1348/000709906x106147
Schunk, D. H. (1996). Goal and self-evaluative influences during children’s cognitive
skill learning. American Educational Research Journal, 33(2), 359-382.
doi:10.2307/1163289

SELF-ASSESSMENT EFFECTS ON SRL AND SE
49
*Schunk, D. H., & Ertmer, P. A. (1999). Self-regulatory processes during computer
skill acquisition: Goal and self-evaluative influences. Journal of Educational
Psychology, 91(2), 251-260. doi:10.1037/0022-0663.91.2.251
Schunk, D. H., & Usher, E. L. (2011). Assessing self-efficacy for self-regulated
learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-
regulation of learning and performance (pp. 282-297). New York: Routledge.
Sitzmann, T., Ely, K., Brown, K. G., & Bauer, K. N. (2010). Self-assessment of
knowledge: A cognitive learning or affective measure? Academy of Management
Learning & Education, 9(2), 169-191.
Topping, K. J. (2003). Self and peer assessment in school and university: Reliability,
validity and utility. In M. Segers, F. Dochy, & E. Cascallar (Eds.), Optimising
new modes of assessment: In search of qualities and standards (Vol. 1, pp. 55-
87): Springer Netherlands.
Veenman, M. (2011). Alternative assessment of strategy use with self-report
instruments: A discussion. Metacognition and Learning, 6(2), 205-211.
doi:10.1007/s11409-011-9080-x
Wall, S. M. (1982). Effects of systematic self-monitoring and self-reinforcement in
children's management of test performances. The Journal of Psychology, 111(1),
129-136.
Whitebread, D., Bingham, S., Grau, V., Pino Pasternak, D., & Sangster, C. (2007).
Development of metacognition and self-regulated learning in young children:
Role of collaborative and peer-assisted learning. Journal of Cognitive Education
and Psychology, 6(3), 433-455. doi:10.1891/194589507787382043

SELF-ASSESSMENT EFFECTS ON SRL AND SE
50
Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M.
Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp.
531-566). Orlando, FL: Academic Press.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In
M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation
(pp. 13-40). San Diego, California: Academic Press.
*Zimmerman, B. J., & Kitsantas, A. (1996). Self-regulated learning of a motoric skill:
The role of goal setting and self-monitoring. Journal of Applied Sport
Psychology, 8(1), 60-75. doi:10.1080/10413209608406308

SELF-ASSESSMENT EFFECTS ON SRL AND SE
51
Table 1
Summary of included studies
First Author
(Year)
Sample
Size
%
Female
Subject
Educational
level
SRL variable
SRL
instrument
Design
SSA
intensity
Average
Effect
Size
Schunk & E.
(1999)
22
91
Computer
education
Higher
education
Learning
SRL
Questionnaire
Experimental
Low-
medium
-.15
+.70
88
+.57
Panadero et
al. (2012)
20
53
Geography
Secondary
school
Learning
SRL
Questionnaire
Experimental
High
+.73
-.17
+.17
-.94
Panadero et
al. (2013)
49
84
Computer
education
Higher
education
Learning
SRL
Questionnaire
Experimental
High
+.13
DiGiacomo
(2014)
30
50
Mathematics
Secondary
school
Learning
SRL
Questionnaire
Experimental
Medium
+.24
Kahrizi et al.
(2014)
40
73
EFL
Higher
education
Learning
SRL
Questionnaire
Experimental
High
+.25

SELF-ASSESSMENT EFFECTS ON SRL AND SE
52
Panadero &
R. (2014)
218
87
Learning and
Development
Higher
education
Learning
SRL
Questionnaire
Quasi-
experimental
Medium
+.20
Mahlberg
(2015)
186
55
Various
Higher
education
Learning
SRL
Questionnaire
Quasi-
experimental
Low-
medium
+.39
Goodrich
(1996)
40
58
Biology
Secondary
school
SRL
measured
qualitative
Think-aloud
protocols
Quasi-
experimental
Medium-
high
.00
Panadero et
al. (2012)
80
53
Geography
Secondary
school
SRL
measured
qualitative
Think-aloud
protocols
Experimental
High
+1.96
Panadero &
R. (2014)
218
87
Learning and
Development
Higher
education
SRL
measured
qualitative
Open
questions
Quasi-
experimental
Medium
+.93
Panadero et
al. (2012)
20
53
Geography
Secondary
school
Negative
SRL
Questionnaire
Experimental
High
-1.59
+.56
-2.58
-1.42
Panadero et
al. (2013)
49
84
Computer
education
Higher
education
Negative
SRL
Questionnaire
Experimental
High
-.20
Panadero &
R. (2014)
218
87
Learning and
Development
Higher
education
Negative
SRL
Questionnaire
Quasi-
experimental
Medium
+.53

SELF-ASSESSMENT EFFECTS ON SRL AND SE
53
Zimmerman
& K. (1996)
20
100
Dart
Secondary
school
Self-efficacy
Questionnaire
Experimental
Medium
+1.00
+.51
Kitsantas &
Z. (1998)
20
100
Dart
Secondary
school
Self-efficacy
Questionnaire
Experimental
Low
+2.10
+4.89
+2.70
+4.97
Schunk & E.
(1999)
22
91
Computer
education
Higher
education
Self-efficacy
Questionnaire
Experimental
Low-
medium
+1.37
+.20
+.36
Olina & S.
(2002)
120
Psychology
Secondary
school
Self-efficacy
Questionnaire
Experimental
Low-
medium
+.05
Kitsantas et
al. (2004)
24
57
Computer
education
Secondary
school
Self-efficacy
Questionnaire
Experimental
Low-
medium
+.05
+1.19
-.31
+.64
Olina & S.
(2004)
170
Psychology
Secondary
school
Self-efficacy
Questionnaire
Experimental
Low-
medium
+.71

SELF-ASSESSMENT EFFECTS ON SRL AND SE
54
Coronado-
Aliegro
(2007)
104
54
Spanish as a
Foreign
Language
Higher
education
Self-efficacy
Questionnaire
Quasi-
experimental
Medium
+.10
Ramdass &
Z. (2008)
42
52
Mathematics
Primary
school
Self-efficacy
Questionnaire
Experimental
Medium
+.19
Andrade et
al. (2009)
268
63
Essay
writing
Primary and
secondary
school
Self-efficacy
Questionnaire
Quasi-
experimental
High
+.22
Nbina & V.
(2010)
192
53
Chemistry
Secondary
school
Self-efficacy
Questionnaire
Quasi-
experimental
Low-
medium
+3.02
Panadero et
al. (2012)
20
53
Geography
Secondary
school
Self-efficacy
Questionnaire
Experimental
High
+.22
-.21
-.04
+.53
Alishah & D.
(2013)
54
-
EFL/ESL
Higher
education
Self-efficacy
Questionnaire
Quasi-
experimental
Low-
medium
+.07
Panadero et
al. (2013)
49
84
Computer
education
Higher
education
Self-efficacy
Questionnaire
Experimental
High
+.50
Baleghizadeh
& M. (2014)
57
100
EFL
Higher
education
Self-efficacy
Questionnaire
Quasi-
experimental
Low-
medium
+.53

SELF-ASSESSMENT EFFECTS ON SRL AND SE
55
Panadero &
R. (2014)
218
87
Learning and
Development
Higher
education
Self-efficacy
Questionnaire
Quasi-
experimental
Medium
+.13

SELF-ASSESSMENT EFFECTS ON SRL AND SE
56
Table 2
Sample age
Age
Study
Age extracted from the
publication (SD)
Grade extracted and
the corresponding age
(Country)
Calculated for this
review
Offered by the author
Goodrich (1996)
7th (USA) 12-13
12.5
Zimmerman & K (1996)
15.8
Kitsantas & Z (1998)
15.16
Schunk & E (1999)
21
Olina & S (2002)
The author provided the following info:
“majority of the students in the study were 16
year olds”
Olina & S (2004)
10-11th (Latvia) 16-17
16.5
Kitsantas et. al (2004)
15.2
Coronado-Aliegro (2007)
23
Ramdass & Z (2008)
5-6th (USA) 10-12
11
Andrade et. al (2009)
3-7th (USA) 8-13
10.5
Nbina & V (2010)
Panadero et. al (2012)
15.9 (.92)
Alishah & D (2012)
Panadero et. al (2013)
20.6 (2.1)
Panadero & R (2014)
22.17 (3.92)
Baleghizadeh & M (2014)
26 (3.28)
DiGiacomo (2014)
6-7th (USA) 11-13
12
Kahrizi et. al (2014)
20.5
Mahlberg (2015)
22.96 (7.79)

SELF-ASSESSMENT EFFECTS ON SRL AND SE
57
Table 3
Self-assessment intervention categories
Study
Monitoring
Self-
evaluation
Self-
assessment
instrument
Feedback
and revision
Self-
assessment
training
Self-
assessment
intensity
Goodrich (1996)
0
0
1
0
0
1
Zimmerman & K. (1996)
1
0
0
0
0
1
Kitsantas & Z. (1998)
1
0
0
0
0
1
Schunk & E. (1999)
0
1
0
0
0
1
Olina & S. (2002)
0
0
0
1
0
1
Kitsantas et al. (2004)
1
0
0
0
0
1
Olina & S. (2004)
0
0
0
1
0
1
Coronado-Aliegro (2007)
0
1
0
0
0
1
Ramdass & Z. (2008)
1
1
0
0
0
2
Andrade et al. (2009)
0
0
1
1
0
2
Nbina & V. (2010)
1
0
0
1
1
3
Panadero et al. (2012)
0
0
1
1
0
2
Panadero et al. (2013)
0
0
1
1
0
2
Alishah & D. (2013)
0
1
0
0
0
1
DiGiacomo (2014)
1
1
0
1
0
3
Kahrizi et al. (2014)
0
0
1
1
1
3
Panadero & R. (2014)
0
0
1
1
0
2
Baleghizadeh & M. (2014)
0
1
0
0
0
1
Mahlberg (2015)
0
0
1
0
0
1

SELF-ASSESSMENT EFFECTS ON SRL AND SE
58
Table 4
Moderating variables effects on Learning SRL
Quantitative moderators
Moderating variable
k
QR (1)
B
95% CI
Gender
11
.131 (p = .717)
.003
(-0.011 – 0.016)
Age
12
1.021 (p = .312)
.029
(-0.027 – 0.085)
SA intervention (Total)
12
.872 (p = .351)
-.124
(-0.383 – 0.136)
Categorical moderators
Moderating variable
QB
Group
k
d
95% CI
Ed level *
1.628 (p = .443)
Secondary
University
4
7
-.042
.284
(-0.501 – 0.417)
(0.084 – 0.484)
Implementation agent
.507 (p = .476)
Researcher
Teacher
10
2
.185
.324
(-0.037 – 0.406)
(0.012 – 0.635)
SA intervention categories
Self-evaluation
.246 (p = .620)
Self-evaluation
No self-evaluation
4
8
.325
.206
(-0.097 – 0.747)
(0.000 – 0.412)
SA instrument
.246 (p = .620)
SA instrument
No SA instrument
8
4
.206
.325
(0.000 – 0.412)
(-0.097 – 0.747)
Feedback and revision
2.054 (p =.152)
F&R
No F&R
8
4
.152
.383
(-0.049 – 0.352)
(0.138 – 0.629)
Note: Highlighted in grey moderating variables that will not be discussed in the paper because one of the groups is below k = 2.
* The category with primary students was excluded from this analysis as it was k = 1.
Values in Self-evaluation and SA instrument are the same because the group of studies is the same but reversed.
SA = self-assessment

SELF-ASSESSMENT EFFECTS ON SRL AND SE
59
Table 5
Moderating variables effects on Negative SRL
Quantitative moderators
Moderating variable
k
QR(1)
B
95% CI
Gender
6
3.322 (p = .068)
.042
(-0.003 – 0.088)
(-0.006 – 0.518)
Age
6
3.673 (p = .055)
.256
Categorical moderators
Moderating variable
QB
Group
k
d
95% CI
Ed level
3.111 (p = .078)
Secondary
University
4
2
-1.198
.180
(-2.158 – -0.238)
(-1.013 – 1.272)
Note: Highlighted in grey moderating variables that will not be discussed in the paper because one of the groups is below k = 3. The
variables self-assessment intervention and all its subcategories are not reported because all the effect sizes shared the same scores
and there was not variability. Regarding the variable implementation was excluded from this analysis as one of the groups was k = 1.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
60
Table 6
Moderating variables effects on SRL measured qualitatively
Quantitative moderators
Moderating variable
k
QR(1)
B
95% CI
Gender
3
.035 (p = .852)
-.009
(-0.108 – 0.089)
Age
3
1.202 (p = .273)
.136
(-0.107 – 0.378)
SA intervention (Total score)
3
2.210 (p = .137)
1.371
(-0.436 – 3.178)
Note: none of the categorical moderators was calculated because all the groups had the same score or one of the
groups was k = 1.
SA = self-assessment.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
61
Table 7
Moderating variables effects on self-efficacy
Quantitative moderators
Moderating variable
k
QR(1)
B
95% CI
Gender
24
5.271 (p = .022)
.026
(0.004 – 0.049)
Age
25
.562 (p = .453)
-.028
(-0.099 – 0.045)
SA intervention (Total)
27
.003 (p = .935)
.019
(-0.604 – 0.641)
Categorical moderators
Moderating variable
QB
Group
k
d
95% CI
Ed level *
3.839 (p = .147)
Primary
Secondary
University
2
17
8
.206
1.106
.390
(-1.084 – 1.496)
(0.624 – 1.589)
(-0.273 – 1.052)
Implementation agent
1.930 (p = .165)
Researcher
Teacher
21
6
.957
.348
(0.525 – 1.389)
(-0.394 – 1.091)
SA intervention categories
Monitoring
12.962 (p = .000)
Monitoring
No monitoring
12
15
1.456
.310
(0.970 – 1.941)
(-0.081 – 0.701)
Self-evaluation
1.818 (p = .178)
Self-evaluation
No self-
evaluation
7
20
.386
.959
(-0.325 – 1.096)
(0.523 – 1.396)
SA instrument
3.912 (p = .048)
SA instrument
No SA
instrument
7
20
.197
1.023
(-0.503 – 0.896)
(0.596 – 1.451)
Feedback and revision
1.284 (p = .257)
F&R
No F&R
10
17
.538
.984
(-0.059 – 1.135)
(0.496 – 1.471)
Note: Highlighted in grey moderating variables that will not be discussed in the paper because one of the groups is k = 2.
* The study with primary students was included because there were two other categories and k was bigger than 1 but caution
needs to be taken when interpreting this results regarding primary education students studies.
SA = self-assessment.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
62
Figure 1. Flowchart of included studies handling.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
63
Figure 2. Funnel plot meta-analysis of self-assessment effects on Learning SRL.

SELF-ASSESSMENT EFFECTS ON SRL AND SE
64
Figure 3. Funnel plot meta-analysis of self-assessment effects on self-efficacy

SELF-ASSESSMENT EFFECTS ON SRL AND SE
65
Figure 4. Forest plot for the meta-analysis self-assessment effects on Learning SRL

SELF-ASSESSMENT EFFECTS ON SRL AND SE
66
Figure 5. Forest plot for the meta-analysis self-assessment effects on Negative SRL

SELF-ASSESSMENT EFFECTS ON SRL AND SE
67
Figure 6. Forest plot for the meta-analysis self-assessment effects on SRL measured qualitatively

SELF-ASSESSMENT EFFECTS ON SRL AND SE
68
Figure 7. Forest plot for the meta-analysis self-assessment effects on Self-efficacy