The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies

Abstract

Peer assessment has been the subject of considerable research interest over the last three decades, with numerous educational researchers advocating for the integration of peer assessment into schools and instructional practice. Research synthesis in this area has, however, largely relied on narrative reviews to evaluate the efficacy of peer assessment. Here, we present a meta-analysis (54 studies, k = 141) of experimental and quasi-experimental studies that evaluated the effect of peer assessment on academic performance in primary, secondary, or tertiary students across subjects and domains. An overall small to medium effect of peer assessment on academic performance was found (g = 0.31, p < .001). The results suggest that peer assessment improves academic performance compared with no assessment (g = 0.31, p = .004) and teacher assessment (g = 0.28, p = .007), but was not significantly different in its effect from self-assessment (g = 0.23, p = .209). Additionally, meta-regressions examined the moderating effects of several feedback and educational characteristics (e.g., online vs offline, frequency, education level). Results suggested that the effectiveness of peer assessment was remarkably robust across a wide range of contexts. These findings provide support for peer assessment as a formative practice and suggest several implications for the implementation of peer assessment into the classroom.

Full text

META-ANALYSIS
The Impact of Peer Assessment on Academic
Performance: A Meta-analysis of Control Group Studies
Kit S. Double
1
&Joshua A. McGrane
1
&Therese N. Hopfenbeck
1
#The Author(s) 2019
Abstract
Peer assessment has been the subject of considerable research interest over the last three
decades, with numerous educational researchers advocating for the integration of peer
assessment into schools and instructional practice. Research synthesis in this area has,
however, largely relied on narrative reviews to evaluate the efficacy of peer assessment.
Here, we present a meta-analysis (54 studies, k= 141) of experimental and quasi-
experimental studies that evaluated the effect of peer assessment on academic performance
in primary, secondary, or tertiary students across subjects and domains. An overall small to
medium effect of peer assessment on academic performance was found (g=0.31,p< .001).
The results suggest that peer assessment improves academic performance compared with no
assessment (g=0.31,p= .004) and teacher assessment (g= 0.28, p= .007), but was not
significantly different in its effect from self-assessment (g=0.23,p= .209). Additionally,
meta-regressions examined the moderating effects of several feedback and educational
characteristics (e.g., online vs offline, frequency, education level). Results suggested that
the effectiveness of peer assessment was remarkably robust across a wide range of contexts.
These findings provide support for peer assessment as a formative practice and suggest
several implications for the implementation of peer assessment into the classroom.
Keywords Peer assessment .Meta-analysis .Experimental design .Effect size .Feedback .
Formative assessment
Feedback is often regarded as a central component of educational practice and crucial to students’
learning and development (Fyfe & Rittle-Johnson, 2016; Hattie and Timperley 2007; Hays, Kornell,
&Bjork,2010;Paulus,1999). Peer assessment has been identified as one method for delivering
Educational Psychology Review
https://doi.org/10.1007/s10648-019-09510-3
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10648-019-
09510-3) contains supplementary material, which is available to authorized users.
*Kit S. Double
kit.double@education.ox.ac.uk
1
Department of Education, University of Oxford, Oxford, England
(2020) 32:481–509
Published online: 10 December 2019
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feedback efficiently and effectively to learners (Topping 1998; van Zundert et al. 2010). The use of
students to generate feedback about the performance of their peers is referred to in the literature using
various terms, including peer assessment, peer feedback, peer evaluation, and peer grading. In this
article, we adopt the term peer assessment, as it more generally refers to the method of peers
assessing or being assessed by each other, whereas the term feedback is used when we refer to the
actual content or quality of the information exchanged between peers. This feedback can be
delivered in a variety of forms including written comments, grading, or verbal feedback (Topping
1998). Importantly, by performing both the role of assessor and being assessed themselves, students’
learning can potentially benefit more than if they are just assessed (Reinholz 2016).
Peer assessments tend to be highly correlated with teacher assessments of the same students
(Falchikov and Goldfinch 2000;Lietal.2016; Sanchez et al. 2017). However, in addition to
establishing comparability between teacher and peer assessment scores, it is important to deter-
mine whether peer assessment also has a positive effect on future academic performance. Several
narrative reviews have argued for the positive formative effects of peer assessment (e.g., Black
and Wiliam 1998a; Topping 1998; van Zundert et al. 2010) and have additionally identified a
number of potentially important moderators for the effect of peer assessment. This meta-analysis
will build upon these reviews and provide quantitative evaluations for some of the instructional
features identified in these narrative reviews by utilising them as moderators within our analysis.
Evaluating the Evidence for Peer Assessment
Empirical Studies
Despite the optimism surrounding peer assessment as a formative practice, there are relatively few
control group studies that evaluate the effect of peer assessment on academic performance (Flórez and
Sammons 2013; Strijbos and Sluijsmans 2010). Most studies on peer assessment have tended to focus
on either students’or teachers’subjective perceptions of the practice rather than its effect on academic
performance (e.g., Brown et al. 2009; Young and Jackman 2014). Moreover, interventions involving
peer assessment often confound the effect of peer assessment with other assessment practices that are
theoretically related under the umbrella of formative assessment (Black and Wiliam 2009). For
instance, Wiliam et al. (2004) reported a mean effect size of .32 in favor of a formative assessment
intervention but they were unable to determine the unique contribution of peer assessment to students’
achievement, as it was one of more than 15 assessment practices included in the intervention.
However, as shown in Fig. 1, there has been a sharp increase in the number of studies related to
peer assessment, with over 75% of relevant studies published in the last decade. Although it is still
far from being the dominant outcome measure in research on formative practices, many of these
recent studies have examined the effect of peer assessment on objective measures of academic
performance (e.g., Gielen et al. 2010a; Liu et al. 2016;Wangetal.2014a). The number of studies of
peer assessment using control group designs also appears to be increasing in frequency (e.g., van
Ginkel et al. 2017;Wangetal.2017). These studies have typically compared the formative effect of
peer assessment with either teacher assessment (e.g., Chaney and Ingraham 2009;Sippeland
Jackson 2015;vanGinkeletal.2017) or no assessment conditions (e.g., Kamp et al. 2014;L.Li
and Steckelberg 2004; Schonrock-Adema et al. 2007). Given the increase in peer assessment
research, and in particular experimental research, it seems pertinent to synthesise this new body of
research, as it provides a basis for critically evaluating the overall effectiveness of peer assessment
and its moderators.
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Previous Reviews
Efforts to synthesise peer assessment research have largely been limited to narrative reviews, which
have made very strong claims regarding the efficacy of peer assessment. For example, in a review of
peer assessment with tertiary students, Topping (1998) argued that the effects of peer assessment are,
‘as good as or better than the effects of teacher assessment’(p. 249). Similarly, in a review on peer
and self-assessment with tertiary students, Dochy et al. (1999) concluded that peer assessment can
have a positive effect on learning but may be hampered by social factors such as friendships,
collusion, and perceived fairness. Reviews into peer assessment have also tended to focus on
determining the accuracy of peer assessments, which is typically established by the correlation
between peer and teacher assessments for the same performances. High correlations have been
observed between peer and teacher assessments in three meta-analyses to date (r= .69, .63, and .68
respectively; Falchikov and Goldfinch 2000;H.Lietal.2016; Sanchez et al. 2017). Given that peer
assessment is often advocated as a formative practice (e.g., Black and Wiliam 1998a; Topping
0
1000
2000
3000
1995 2000 2005 2010 2015 2020
Ye a r
Records
Fig. 1 Number of records returned by year. The following search terms were used: ‘peer assessment’or ‘peer
grading or ‘peer evaluation’or ‘peer feedback’. Data were collated by searching Web of Science (www.
webofknowledge.com) for the following keywords: ‘peer assessment’or ‘peer grading’or ‘peer evaluation’or
‘peer feedback’and categorising by year
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1998), it is important to expand on these correlational meta-analyses to examine the formative effect
that peer assessment has on academic performance.
In addition to examining the correlation between peer and teacher grading, Sanchez et al.
(2017) additionally performed a meta-analysis on the formative effect of peer grading (i.e., a
numerical or letter grade was provided to a student by their peer) in intervention studies. They
found that there was a significant positive effect of peer grading on academic performance for
primary and secondary (grades 3 to 12) students (g= .29). However, it is unclear whether their
findings would generalise to other forms of peer feedback (e.g., written or verbal feedback)
and to tertiary students, both of which we will evaluate in the current meta-analysis.
Moderators of the Effectiveness of Peer Assessment
Theoretical frameworks of peer assessment propose that it is beneficial in at least two respects.
Firstly, peer assessment allows students to critically engage with the assessed material, to compare
and contrast performance with their peers, and to identify gaps or errors in their own knowledge
(Topping 1998). In addition, peer assessment may improve the communication of feedback, as peers
may use similar and more accessible language, as well as reduce negative feelings of being evaluated
by an authority figure (Liu et al. 2016). However, the efficacy of peer assessment, like traditional
feedback, is likely to be contingent on a range of factors including characteristics of the learning
environment, the student, and the assessment itself (Kluger and DeNisi 1996; Ossenberg et al.
2018). Some of the characteristics that have been proposed to moderate the efficacy of feedback
include anonymity (e.g., Rotsaert et al. 2018;YuandLiu2009), scaffolding (e.g., Panadero and
Jonsson 2013), quality and timing of the feedback (Diab 2011), and elaboration (e.g., Gielen et al.
2010b). Drawing on the previously mentioned narrative reviews and empirical evidence, we now
briefly outline the evidence for each of the included theoretical moderators.
Role
It is somewhat surprising that most studies that examine the effect of peer assessment tend to only
assess the impact on the assessee and not the assessor (van Popta et al. 2017). Assessing may confer
several distinct advantages such as drawing comparisons with peers’work and increased familiarity
with evaluative criteria. Several studies have compared the effect of assessing with being assessed.
Lundstrom and Baker (2009) found that assessing a peer’s written work was more beneficial for
their own writing than being assessed by a peer. Meanwhile, Graner (1987) found that students who
were receiving feedback from a peer and acted as an assessor did not perform better than students
who acted as an assessor but did not receive peer feedback. Reviewing peers’work is also likely to
help students become better reviewers of their own work and to revise and improve their own work
(Rollinson 2005). While, in practice, students will most often act as both assessor and assessee
during peer assessment, it is useful to gain a greater insight into the relative impact of performing
each of these roles for both practical reasons and to help determine the mechanisms by which peer
assessment improves academic performance.
Peer Assessment Type
The characteristics of peer assessment vary greatly both in practice and within the research literature.
Because meta-analysis is unable to capture all of the nuanced dimensions that determine the type,
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intensity, and quality of peer assessment, we focus on distinguishing between what we regard as the
most prevalent types of peer assessment in the literature: grading, peer dialogs, and written
assessment. Each of these peer assessment types is widely used in the classroom and often in
various combinations (e.g., written qualitative feedback in combination with a numerical grade).
While these assessment types differ substantially in terms of their cognitive complexity and
comprehensiveness, each has shown at least some evidence of impactive academic performance
(e.g., Sanchez et al. 2017; Smith et al. 2009; Topping 2009).
Freeform/Scaffolding
Peer assessment is often implemented in conjunction with some form of scaffolding, for example,
rubrics, and scoring scripts. Scaffolding has been shown to improve both the quality peer assessment
and increase the amount of feedback assessors provide (Peters, Körndle & Narciss, 2018). Peer
assessment has also been shown to be more accurate when rubrics are utilised. For example,
Panadero, Romero, & Strijbos (2013) found that students were less likely to overscore their peers.
Online
Increasingly, peer assessment has been performed online due in part to the growth in online learning
activities as well as the ease by which peer assessment can be implemented online (van Popta et al.
2017). Conducting peer assessment online can significantly reduce the logistical burden of
implementing peer assessment (e.g., Tannacito and Tuzi 2002). Several studies have shown that peer
assessment can effectively be carried out online (e.g., Hsu 2016;LiandGao2016). Van Popta et al.
(2017) argue that the cognitive processes involved in peer assessment, such as evaluating, explaining,
and suggesting, similarly play out in online and offline environments. However, the social processes
involved in peer assessment are likely to substantively differ between online and offline peer
assessment (e.g., collaborating, discussing), and it is unclear whether this might limit the benefits
of peer assessment through one or the other medium. To the authors’knowledge, no prior studies
have compared the effects of online and offline peer assessment on academic performance.
Anonymity
Because peer assessment is fundamentally a collaborative assessment practice, interpersonal
variables play a substantial role in determining the type and quality of peer assessment
(Strijbos and Wichmann 2018). Some researchers have argued that anonymous peer assess-
ment is advantageous because assessors are more likely to be honest in their feedback, and
interpersonal processes cannot influence how assessees receive the assessment feedback
(Rotsaert et al. 2018). Qualitative evidence suggests that anonymous peer assessment results
in improved feedback quality and more positive perceptions towards peer assessment (Rotsaert
et al. 2018; Vanderhoven et al. 2015). A recent qualitative review by Panadero and Alqassab
(2019) found that three studies had compared anonymous peer assessment to a control group
(i.e., open peer assessment) and looked at academic performance as the outcome. Their review
found mixed evidence regarding the benefit of anonymity in peer assessment with one of the
included studies finding an advantage of anonymity, but the other two finding little benefit of
anonymity. Others have questioned whether anonymity impairs the development of cognitive
and interpersonal development by limiting the collaborative nature of peer assessment (Strijbos
and Wichmann 2018).
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Frequency
Peers are often novices at providing constructive assessment and inexperienced learners tend to
provide limited feedback (Hattie and Timperley 2007). Several studies have therefore suggested
that peer assessment becomes more effective as students’experience with peer assessment
increases. For example, with greater experience, peers tend to use scoring criteria to a greater
extent (Sluijsmans et al. 2004). Similarly, training peer assessment over time can improve the
quality of feedback they provide, although the effects may be limited by the extent of a student’s
relevant domain knowledge (Alqassab et al. 2018). Frequent peer assessment may also increase
positive learner perceptions of peer assessment (e.g., Sluijsmans et al. 2004). However, other
studies have found that learner perceptions of peer assessment are not necessarily positive
(Alqassab et al. 2018). This may suggest that learner perceptions of peer assessment vary
depending on its characteristics (e.g., quality, detail).
Current Study
Given the previous reliance on narrative reviews and the increasing research and teacher
interest in peer assessment, as well as the popularity of instructional theories advocating for
peer assessment and formative assessment practices in the classroom, we present a quantitative
meta-analytic review to develop and synthesise the evidence in relation to peer assessment.
This meta-analysis evaluates the effect of peer assessment on academic performance when
compared to no assessment as well as teacher assessment. To do this, the meta-analysis only
evaluates intervention studies that utilised experimental or quasi-experimental designs, i.e.,
only studies with control groups, so that the effects of maturation and other confounding
variables are mitigated. Control groups can be either passive (e.g., no feedback) or active (e.g.,
teacher feedback). We meta-analytically address two related research questions:
Q1 What effect do peer assessment interventions have on academic performance relative to
the observed control groups?
Q2 What characteristics moderate the effectiveness of peer assessment?
Method
Working Definitions
The specific methods of peer assessment can vary considerably, but there are a number of
shared characteristics across most methods. Peers are defined as individuals at similar (i.e.,
within 1–2 grades) or identical education levels. Peer assessment must involve assessing or
being assessed by peers, or both. Peer assessment requires the communication (either written,
verbal, or online) of task-relevant feedback, although the style of feedback can differ markedly,
from elaborate written and verbal feedback to holistic ratings of performance.
We took a deliberately broad definition of academic performance for this meta-analysis
including traditional outcomes (e.g., test performance or essay writing) and also practical skills
(e.g., constructing a circuit in science class). Despite this broad interpretation of academic
performance, we did not include any studies that were carried out in a professional/
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organisational setting other than professional skills (e.g., teacher training) that were being
taught in a traditional educational setting (e.g., a university).
Selection Criteria
To be included in this meta-analysis, studies had to meet several criteria. Firstly, a study needed
to examine the effect of peer assessment. Secondly, the assessment could be delivered in any
form (e.g., written, verbal, online), but needed to be distinguishable from peer-coaching/peer-
tutoring. Thirdly, a study needed to compare the effect of peer assessment with a control group.
Pre-post designs that did not include a control/comparison group were excluded because we
could not discount the effects of maturation or other confounding variables. Moreover, the
comparison group could take the form of either a passive control (e.g., a no assessment
condition) or an active control (e.g., teacher assessment). Fourthly, a study needed to examine
the effect of peer assessment on a non-self-reported measure of academic performance.
In addition to these criteria, a study needed to be carried out in an educational context or be
related to educational outcomes in some way. Any level of education (i.e., tertiary, secondary,
primary) was acceptable. A study also needed to provide sufficient data to calculate an effect
size. If insufficient data was available in the manuscript, the authors were contacted by email to
request the necessary data (additional information was provided for a single study). Studies
also needed to be written in English.
Literature Search
The literature search was carried out on 8 June 2018 using PsycInfo,Google Scholar,and
ERIC. Google Scholar was used to check for additional references as it does not allow for the
exporting of entries. These three electronic databases were selected due to their relevance to
educational instruction and practice. Results were not filtered based on publication date, but
ERIC only holds records from 1966 to present. A deliberately wide selection of search terms
was used in the first instance to capture all relevant articles. The search terms included ‘peer
grading’or ‘peer assessment’or ‘peer evaluation’or ‘peer feedback’, which were paired with
‘learning’or ‘performance’or ‘academic achievement’or ‘academic performance’or ‘grades’.
All peer assessment-related search terms were included with and without hyphenation. In
addition, an ancestry search (i.e., back-search) was performed on the reference lists of the
included articles. Conference programs for major educational conferences were searched.
Finally, unpublished results were sourced by emailing prominent authors in the field and
through social media. Although there is significant disagreement about the inclusion of
unpublished data and conference abstracts, i.e., ‘grey literature’(Cook et al. 1993), we opted
to include it in the first instance because including only published studies can result in a meta-
analysis over-estimating effect sizes due to publication bias (Hopewell et al. 2007). It should,
however, be noted that none of the substantive conclusions changed when the analyses were
re-run with the grey literature excluded.
The database search returned 4072 records. An ancestry search returned an additional 37
potentially relevant articles. No unpublished data could be found. After duplicates were
removed, two reviewers independently screened titles and abstracts for relevance. A kappa
statistic was calculated to assess inter-rater reliability between the two coders and was found to
be .78 (89.06% overall agreement, CI .63 to .94), which is above the recommended minimum
levels of inter-rater reliability (Fleiss 1971). Subsequently, the full text of articles that were
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deemed relevant based on their abstracts was examined to ensure that they met the selection
criteria described previously. Disagreements between the coders were discussed and, when
necessary, resolved by a third coder. Ultimately, 55 articles with 143 effect sizes were found
that met the inclusion criteria and included in the meta-analysis. The search process is depicted
in Fig. 2.
Data Extraction
A research assistant and the first author extracted data from the included papers. We took an
iterative approach to the coding procedure whereby the coders refined the classification of each
variable as they progressed through the included studies to ensure that the classifications best
characterised the extant literature. Below, the coding strategy is reviewed along with the
classifications utilised. Frequency statistics and inter-rater reliability for the extracted data
for the different classifications are presented in Table 1. All extracted variable showed at least
moderate agreement except for whether the peer assessment was freeform or structured, which
showed fair agreement (Landis and Koch 1977).
Records idenfied through
database searching
(n = 4,072 )
ScreeningIncluded
Eligibility
noitacifitnedI
Addional records idenfied
through other sources
(n = 7 )
Records aer duplicates removed
(n = 3,736 )
Records screened
(n = 3,736)
Records excluded
(n = 3,483 )
Full-text arcles assessed
for eligibility
(n = 253 )
Full-text arcles excluded,
with reasons
(n = 198 )
Studies included in
quantave synthesis
(meta-analysis)
(n = 55 )
Fig. 2 Flow chart for the identification, screening protocol, and inclusion of publications in the meta-analyses
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Table 1 Frequencies of extracted variables
Count Proportion Count Proportion
Studies Effect sizes
Publication type (kappa = 1)
Conference 1 1.85% 1 0.71%
Dissertation 8 14.81% 14 9.93%
Journal 43 79.63% 123 87.23%
Report 2 3.7% 3 2.13%
Education level (kappa = 1)
Tertiary 29 54.72% 83 59.29%
Secondary 13 24.53% 22 15.71%
Primary 11 20.75% 35 25%
Subject
Accounting 1 1.85% 12 8.51%
Education 4 7.41% 8 5.67%
IT 59.26%85.67%
Language 3 5.56% 21 14.89%
Medicine 2 3.70% 7 4.96%
Performing Arts 1 1.85% 1 0.71%
Politics 1 1.85% 1 0.71%
Psychology 2 3.70% 3 2.13%
Reading 1 1.85% 6 4.26%
Research Methods 1 1.85% 3 2.13%
Science 8 14.81% 19 13.48%
Statistics 3 5.56% 4 2.84%
Writing 2240.74%4834.04%
Role (kappa = .59)
Both 49 89.09% 109 78.42%
Reviewee 2 3.64% 10 7.19%
Reviewer 4 7.27% 20 14.39%
Comparison group (kappa = .62)
No assessment 23 35.95% 59 42.14%
Self-assessment 10 15.62% 16 11.43%
Teacher assessment 31 48.44% 65 46.43 %
Written(kappa=.45)
No 20 35.71% 60 42.55%
Yes 3664.29%8157.45%
Dialog (kappa = .57)
No 36 65.45% 92 65.25%
Yes 19 34.55 49 34.75%
Grading (kappa = .52)
No 18 32.73% 46 32.62%
Yes 3767.27%9567.38%
Freeform (kappa = .22)
No 45 83.33% 112 79.43%
Yes 9 16.67% 29 20.57%
Online (kappa = .92)
No 32 59.26% 102 72.34%
Yes 2240.74%3927.66%
Anonymous (kappa = .40)
No 29 55.77% 77 57.04%
Yes 2344.23%5842.96%
Frequency (kappa = .55)
Multiple 34 61.82% 98 69.50%
Single 21 38.18% 43 30.50%
Transfer (kappa = . 43)
Far 1828.12%2618.44%
Near 23 35.94% 64 45.39%
None 23 435.94% 51 36.17%
Allocation (kappa = .56)
Classroom 41 75.93% 107 75.89%
Individual 11 20.37% 31 21.99%
Year/semester 2 3.70% 3 2.13%
Note: different count totals for some variables are the result of missing data. Kappa correlation coefficients are
displayed for each category, which indicate the degree of inter-rater reliability for the data extraction stage
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Publication Type
Publications were classified into journal articles, conference papers, dissertations, reports, or
unpublished records.
Education Level
Education level was coded as either graduate tertiary, undergraduate tertiary, secondary, or
primary. Given the small number of studies that utilised graduate samples (N= 2), we
subsequently combined this classification with undergraduate to form a general tertiary
category. In addition, we recorded the grade level of the students. Generally speaking, primary
education refers to the ages of 6–12, secondary education refers to education from 13–18, and
tertiary education is undertaken after the age of 18.
Age and Sex
The percentage of students in a study that were female was recorded. In addition, we recorded
the mean age from each study. Unfortunately, only 55.5% of studies recorded participants’sex
and only 18.5% of studies recorded mean age information.
Subject
The subject area associated with the academic performance measure was coded. We also
recorded the nature of the academic performance variable for descriptive purposes.
Assessment Role
Studies were coded as to whether the students acted as peer assessors, assessees, or both
assessors and assessees.
Comparison Group
Four types of comparison group were found in the included studies: no assessment, teacher
assessment, self-assessment, and reader-control. In many instances, a no assessment condition
could be characterised as typical instruction; that is, two versions of a course were run—one
with peer assessment and one without peer assessment. As such, while no specific teacher
assessment comparison condition is referenced in the article, participants would most likely
have received some form of teacher feedback as is typical in standard instructional practice.
Studies were classified as having teacher assessment on the basis of a specific reference to
teacher feedback being provided.
Studies were classified as self-assessment controls if there was an explicit reference to a
self-assessment activity, e.g., self-grading/rating. Studies that only included revision, e.g.,
working alone on revising an assignment, were classified as no assessment rather than self-
assessment because they did not necessarily involve explicit self-assessment. Studies
where both the comparison and intervention groups received teacher assessment (in
addition to peer assessment in the case of the intervention group) were coded as no
assessment to reflect the fact that the comparison group received no additional assessment
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compared to the peer assessment condition. In addition, Philippakos and MacArthur
(2016) and Cho and MacArthur (2011) were notable in that they utilised a reader-
control condition whereby students read, but did not assess peers’work. Due to the small
frequency of this control condition, we ultimately classified them as no assessment
controls.
Peer Assessment Type
Peer assessment was characterised using coding we believed best captured the theoretical
distinctions in the literature. Our typology of peer assessment used three distinct components,
which were combined for classification:
1. Did the peer feedback include a dialog between peers?
2. Did the peer feedback include written comments?
3. Did the peer feedback include grading?
Each study was classified using a dichotomous present/absent scoring system for each of the
three components.
Freeform
Studies were dichotomously classified as to whether a specific rubric, assessment script, or
scoring system was provided to students. Studies that only provided basic instructions to
students to conduct the peer feedback were coded as freeform.
Was the Assessment Online?
Studies were classified based on whether the peer assessment was online or offline.
Anonymous
Studies were classified based on whether the peer assessment was anonymous or identified.
Frequency of Assessment
Studies were coded dichotomously as to whether they involved only a single peer assessment
occasion or, alternatively, whether students provided/received peer feedback on multiple
occasions.
Transfer
The level of transfer between the peer assessment task and the academic performance measure
was coded into three categories:
1. No transfer—the peer-assessed task was the same as the academic performance measure.
For example, a student’s assignment was assessed by peers and this feedback was utilised
to make revisions before it was graded by their teacher.
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2. Near transfer—the peer-assessed task was in the same or very similar format as the
academic performance measure, e.g., an essay on a different, but similar topic.
3. Far transfer—the peer-assessed task was in a different form to the academic performance
task, although they may have overlapping content. For example, a student’s assignment
was peer assessed, while the final course exam grade was the academic performance
measure.
Allocation
We recorded how participants were allocated to a condition. Three categories of allocation were
found in the included studies: random allocation at the class level, at the student level, or at the year/
semester level. As only two studies allocated students to conditions at the year/semester level, we
combined these studies with the studies allocated at the classroom level (i.e., as quasi-experiments).
Statistical Analyses of Effect Sizes
Effect Size Estimation and Heterogeneity
A random effects, multi-level meta-analysis was carried out using R version 3.4.3 (R Core
Team 2017). The primary outcome was standardised mean difference between peer assessment
and comparison (i.e., control) conditions. A common effect size metric, Hedge’sg,was
calculated. A positive Hedge’s g value indicates comparatively higher values in the dependent
variable in the peer assessment group (i.e., higher academic performance). Heterogeneity in the
effect sizes was estimated using the I2statistic. I2is equivalent to the percentage of variation
between studies that is due to heterogeneity (Schwarzer et al. 2015). Large values of the I2
statistics suggest higher heterogeneity between studies in the analysis.
Meta-regressions were performed to examine the moderating effects of the various factors
that differed across the studies. We report the results of these meta-regressions alongside sub-
groups analyses. While it was possible to determine whether sub-groups differed significantly
from each other by determining whether the confidence interval around their effect sizes
overlap, sub-groups analysis may also produce biased estimates when heteroscedasticity or
multicollinearity are present (Steel and Kammeyer-Mueller 2002). We performed meta-
regressions separately for each predictor to test the overall effect of a moderator.
Finally, as this meta-analysis included students from primary school to graduate school, which
are highly varied participant and educational contexts, we opted to analyse the data both in complete
form, as well as after controlling for each level of education. As such, we were able to look at the
effect of each moderator across education levels and for each education level separately.
Robust Variance Estimation
Often meta-analyses include multiple effect sizes from the same sample (e.g., the effect of peer
assessment on two different measures of academic performance). Including these dependent effect
sizes in a meta-analysis can be problematic, as this can potentially bias the results of the analysis in
favour of studies that have more effect sizes. Recently, Robust Variance Estimation (RVE) was
developed as a technique to address such concerns (Hedges et al. 2010). RVE allows for the
modelling of dependence between effect sizes even when the nature of the dependence is not
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specifically known. Under such situations, RVE results in unbiased estimates of fixed effects when
dependent effect sizes are included in the analysis (Moeyaert et al. 2017). A correlated effects
structure was specified for the meta-analysis (i.e., the random error in the effects from a single paper
were expected to be correlated due to similar participants, procedures). A rho value of .8 was
specified for the correlated effects (i.e., effects from the same study) as is standard practice when the
correlation is unknown (Hedges et al. 2010). A sensitivity analysis indicated that none of the results
varied as a function of the chosen rho. We utilised the ‘robumeta’package (Fisher et al. 2017)to
perform the meta-analyses. Our approach was to use only summative dependent variables when
they were provided (e.g., overall writing quality score rather than individual trait measures), but to
utilise individual measures when overall indicators were not available. When a pre-post design was
used in a study, we adjusted the effect size for pre-intervention differences in academic performance
as long as there was sufficient data to do so (e.g., ttests for pre-post change).
Results
Overall Meta-analysis of the Effect of Peer Assessment
Prior to conducting the analysis, two effect sizes (g= 2.06 and 1.91) were identified as outliers
and removed using the outlier labelling rule (Hoaglin and Iglewicz 1987). Descriptive
characteristics of the included studies are presented in Table 2. The meta-analysis indicated
that there was a significant positive effect of peer assessment on academic performance (g=
0.31, SE = .06, 95% CI = .18 to .44, p< .001). A density graph of the recorded effect sizes is
provided in Fig. 3. A sensitivity analysis indicated that the effect size estimates did not differ
with different values of rho. Heterogeneity between the studies’effect sizes was large, I2=
81.08%, supporting the use of a meta-regression/sub-groups analysis in order to explain the
observed heterogeneity in effect sizes.
Meta-Regressions and Sub-Groups Analyses
Effect sizes for sub-groups are presented in Table 3. The results of the meta-regressions are
presentedinTable4.
Education Level
A meta-regression with tertiary students as the reference category indicated that there
was no significant difference in effect size as a function of education level. The effect of
peer assessment was similar for secondary students (g=.44, p< .001) and primary
school students (g=.41, p= .006) and smaller for tertiary students (g= .21, p=.043).
There is, however, a strong theoretical basis for examining effects separately at different
education levels (primary, secondary, tertiary), because of the large degree of heteroge-
neity across such a wide span of learning contexts (e.g., pedagogical practices, intellec-
tual and social development of the students). We therefore will proceed by reporting the
data both as a whole and separately for each of the education levels for all of the
moderators considered here. Education level is contrast coded such that tertiary is
compared to the average of secondary and primary and secondary and primary are
compared to each other.
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Comparison Group
A meta-regression indicated that the effect size was not significantly different when comparing peer
assessment with teacher assessment, than when comparing peer assessment with no assessment (b=
Table 2 Descriptive characteristics of the included studies
Authors Year Pub. type Subject Country Ed. level
Hwang et al. 2018 Journal Science Taiwan Primary
Gielen et al. 2010 Journal Writing Belgium High school
Wang et al. 2017 Journal IT Taiwan High school
Hwang et al. 2014 Journal Science Taiwan Primary
Khonbi & Sadeghi 2013 Journal Education Iran Undergraduate
Karegianes et al. 1980 Journal Writing USA High school
Philippakos & MacArthur 2016 Journal Writing USA Primary
Cho & MacArthur 2011 Journal Science USA Undergraduate
Benson 1979 Dissertation Writing USA High school
Liu et al. 2016 Journal Writing Taiwan Primary
Wang et al. 2014 Journal Writing Taiwan Primary
Sippel & jackson 2015 Journal Language USA Undergraduate
Erfani & Nikbin 2015 Journal Writing Iran Undergraduate
Crowe et al. 2015 Journal Research methods USA Undergraduate
Anderson & Flash 2014 Journal Science USA Undergraduate
Papadopoulos et al. 2012 Journal IT Undergraduate
Hussein & Al Ashri 2013 Report Writing Egypt High school
Demetriadis et al. 2011 Journal IT Germany Undergraduate
Olson 1990 Journal Writing USA Primary
Diab 2011 Journal Writing Lebanon Undergraduate
Enders et al. 2010 Journal Statistics USA Undergraduate
Rudd II et al. 2009 Journal Science USA Undergraduate
Chaney & Ingraham 2009 Journal Accounting USA Undergraduate
Xie et al. 2008 Journal Politics USA Undergraduate
Schönrock-Adema 2007 Journal Medicine Netherlands Undergraduate
Li & Steckelberg 2004 Conference IT USA Undergraduate
McCurdy & Shapiro 1992 Journal Reading USA Primary
van Ginkel et al. 2017 Journal Science Netherlands Undergraduate
Kamp et al. 2014 Journal Science Netherlands Undergraduate
Kurihara 2017 Journal Writing Japan High school
Ha & Storey 2006 Journal Writing China Undergraduate
van den Boom 2007 Journal Psychology Netherlands Undergraduate
Ozogul et al. 2008 Journal Education USA Undergraduate
Sun et al. 2015 Journal Statistics USA Undergraduate
Li & Gao 2016 Journal Education USA Undergraduate
Sadler & Good 2006 Journal Science USA High school
Califano 1987 Dissertation Writing USA Primary
Farrell 1977 Dissertation Writing USA High school
AbuSeileek & Abualsha’r 2014 Journal Writing Undergraduate
Bangert 1996 Dissertation Statistics USA Undergraduate
Birjandi & Tamjid 2012 Journal Writing Undergraduate
Chang et al. 2012 Journal Science Taiwan Undergraduate
English et al. 2006 Journal Medicine UK Undergraduate
Hsia 2016 Journal Performing Arts Taiwan High school
Hsu 2016 Journal IT High school
Lin 2009 Dissertation Writing Taiwan Undergraduate
Montanero et al. 2014 Journal Writing Spain Primary
Bhullar 2014 Journal Psychology USA Undergraduate
Prater & Bermudez 1993 Journal Writing USA Primary
Rijlaarsdam & Schoonen 1988 Report Writing Netherlands High school
Ruegg 2018 Journal Writing Japan Undergraduate
Sadeghi & Khonbi 2015 Journal Education Iran Undergraduate
Horn 2009 Dissertation Writing USA Primary
Pierson 1966 Dissertation Writing USA High school
Wise 1992 Dissertation Writing USA High school
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.02, 95% CI −.26 to .31, p= .865). The difference between peer assessment vs. no assessment and
peer assessment vs. self-assessment was also not significant (b=−.03, CI −.44to.38,p= .860), see
Tab le 4. An examination of sub-groups suggested that peer assessment had a moderate positive
effect compared to no assessment controls (g=.31, p= .004) and teacher assessment (g=.28, p=
.007) and was not significantly different compared with self-assessment (g=.23, p= .209). The
meta-regression was also re-run with education level as a covariate but the results were unchanged.
Assessment Role
Meta-regressions indicated that the participant’s role was not a significant moderator of the
effect size; see Table 4. However, given the extremely small number of studies where
participants did not act as both assessees (n= 2) and assessors (n= 4), we did not perform a
sub-groups analysis, as such analyses are unreliable with small samples (Fisher et al. 2017).
Subject Area
Given that many subject areas had few studies (see Table 1) and the writing subject area made up
the majority of effect sizes (40.74%), we opted to perform a meta-regression comparing writing
with other subject areas. However, the effect of peer assessment did not differ between writing (g=
.30,p= .001) and other subject areas (g=.31,p= .002); b=−.003, 95% CI −.25 to .25, p= .979.
Similarly, the results did not substantially change when education level was entered into the model.
0.0
0.2
0.4
0.6
0.8
−1 10
Effect Size
Density
Fig. 3 A density plot of effect sizes
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Peer Assessment Type
The effect of peer assessment did not differ significantly when peer assessment included a
written component (g=.35,p< .001) than when it did not (g=.20,p=.015) , b=.144, 95%
CI −.10 to .39, p= .241. Including education as a variable in the model did not change the effect
written feedback. Similarly, studies with a dialog component (g=.21,p= .033) did not differ
significantly from those that did not (g=.35,p< .001), b=−.137, 95% CI −.39to.12,p= .279.
Studies where peer feedback included a grading component (g=.37,p< .001) did not
differ significantly from those that did not (g=.17,p= .138). However, when education level
Table 3 Results of the sub-groups analysis
Nk g SE I2p
Publication type
Dissertation 8 14 0.21 0.13 64.65% 0.138
Journal 43 123 0.31 0.07 83.23% < .001
Conference/report 2 3 0.82 0.22 9.08% 0.168
Education level
Primary school 11 35 0.41 0.12 68.36% 0.006
Secondary 13 22 0.44 0.1 69.70% 0.001
Tertiary 29 83 0.21 0.10 85.17% 0.043
Comparison group
Teacher assessment 31 65 0.27 0.09 83.82% 0.007
No assessment 23 59 0.31 0.1 78.02% 0.004
Self-assessment 10 16 0.23 0.17 74.57% 0.209
Written
Yes 3681 0.350.0884.04%<.001
No 20 60 0.2 0.08 68.96% 0.014
Dialog
Yes 1949 0.210.0970.74%0.034
No 36 92 0.35 0.08 84.12% < .001
Grading
Yes 3795 0.370.0783.48%<.001
No 18 46 0.17 0.11 72.60% 0.138
Freeform
Yes 9 29 0.42 0.16 68.68% 0.03
No 45 112 0.29 0.07 82.28% < .001
Online
Yes 2239 0.380.1283.46%0.003
No 33 102 0.24 0.08 80.18% 0.004
Anonymous
Yes 2358 0.270.1182.73%0.019
No 29 77 0.25 0.08 70.97% 0.004
Frequency
Multiple 34 98 0.37 0.07 81.28% < .001
Single 21 43 0.2 0.11 80.69% 0.103
Transfer
Far 18 26 0.2 0.13 89.45% 0.124
Near 23 64 0.42 0.08 72.93% < .001
None 23 51 0.29 0.11 84.19% 0.017
Allocation
Classroom 41 107 0.31 0.07 78.97% < .001
Individual 11 31 0.21 0.13 68.59% 0.14
N= Number of studies, k= number of effects, g=Hedge’s g, SE = standard error in the effect size, I2=
heterogeneity within the group, p=pvalue
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was included in the model, the model indicated significant interaction effect between grading
in tertiary students and the average effect of grading in primary and secondary students (b=
.395, 95% CI .06 to .73, p= .022). A follow-up sub-groups analysis showed that grading was
beneficial for academic performance in tertiary students (g=.55,p= .009), but not secondary
Table 4 Results of the meta-reg ressions
Variable b SE CI low. CI upp. p
Publication type
Intercept 0.3 0.12 0.02 0.57 0.038
Published article 0.02 0.14 −0.29 0.32 0.911
Education level
Intercept 0.21 0.1 0.01 0.41 0.043
Primary 0.2 0.15 −0.12 0.53 0.198
Secondary 0.24 0.14 −0.05 0.53 0.103
Subject
Intercept 0.31 0.09 0.13 0.5 0.002
Writing -.0 03 0.12 −0.25 0.25 0.979
Role
Intercept 0.31 0.07 0.17 0.45 < .001
Reviewee −0.25 0.12 −1.6 1.1 0.272
Reviewer 0.06 0.29 −0.87 1 0.838
Comparison
Intercept 0.31 0.11 0.08 0.53 0.01
Self-assessment −0.03 0.19 −0.44 0.38 0.86
Teacher 0.02 0.14 −0.26 0.31 0.864
Written
Intercept 0.22 0.08 0.04 0.4 0.017
Yes 0.14 0.12 −0.1 0.39 0.241
Dialog
Intercept 0.36 0.08 0.19 0.52 < .001
Yes −0.14 0.12 −0.39 0.12 0.279
Grading
Intercept 0.17 0.11 −0.07 0.41 0.161
Yes 0.21 0.14 −0.07 0.48 0.145
Freeform
Intercept 0.42 0.16 0.06 0.79 0.028
Structured −0.13 0.17 −0.51 0.25 0.455
Online
Intercept 0.25 0.07 0.09 0.4 0.002
Yes 0.16 0.13 −0.1 0.42 0.215
Anonymous
Intercept 0.26 0.08 0.1 0.42 0.002
Yes 0.03 0.12 −0.22 0.28 0.811
Frequency
Intercept 0.37 0.07 0.22 0.52 < .001
Single −0.17 0.14 −0.45 0.11 0.223
Transfer
Intercept 0.16 0.1 −0.05 0.37 0.116
Near 0.27 0.13 0.01 0.52 0.042
None 0.14 0.14 −0.15 0.43 0.334
Allocation
Intercept 0.31 0.07 0.16 0.45 < .001
Individual −0.09 0.16 −0.43 0.24 0.566
Year/ S e m e s t e r 0 . 5 1 0 . 3 −2.47 3.48 0.317
b= unstandardised regression estimate, SE = standard error, CI low/UPP = lower and upper bound of the
confidence interval respectively, p=pvalue.
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school students (g=.002, p= .991) or primary school students (g=−.08, p= .762). When the
three variables used to characterise peer assessment were entered simultaneously, the results
were unchanged.
Freeform
The average effect size was not significantly different for studies where assessment was freeform,
i.e., where no specific script or rubric was given (g=.42,p= .030) compared to those where a
specific script or rubric was provided (g=.29,p< .001); b=−.13, 95% CI −.51 to .25, p= .455.
However, there were few studies where feedback was freeform (n=9,k=29). The results were
unchanged when education level was controlled for in the meta-regression.
Online
Studies where peer assessment was online (g=.38,p= .003) did not differ from studies where
assessment was offline (g=.24,p= .004); b=.16, 95% CI −.10to.42,p= .215. This result
was unchanged when education level was included in the meta-regression.
Anonymity
There was no significant difference in terms of effect size between studies where peer
assessment was anonymised (g=.27,p= .019) and those where it was not (g=.25,p=
.004); b=.03, 95% CI −.22to.28,p= .811). Nor was the effect significant when education
level was controlled for.
Frequency
Studies where peer assessment was performed just a single time (g= .19, p= .103) did not differ
significantly from those where it was performed multiple times (g= .37, p< .001); b=-.17, 95%
CI −.45 to .11, p= .223. Although it is worth noting that the results of the sub-groups analysis
suggest that the effect of peer assessment was not significant when only considering studies that
applied it a single time. The result did not change when education was included in the model.
Transfer
There was no significant difference in effect size between studies utilising far transfer (g= .21, p=
.124) than those with near (g= .42, p< .001) or no transfer (g= .29, p= .017). Although it is worth
noting that the sub-groups analysis suggests that the effect of peer assessment was only significant
when there was no transfer to the criterion task. As shown in Table 4, this was also not significant
when analysed using meta-regressions either with or without education in the model.
Allocation
Studies that allocated participants to experimental condition at the student level (g=.21,p=
.14) did not differ from those that allocated condition at the classroom/semester level (g=.31,
p<.001andg=.79,p= .223 respectively), see Table 4for meta-regressions.
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Publication Bias
Risk of publication bias was assessed by inspecting the funnel plots (see Fig. 4)ofthe
relationship between observed effects and standard error for asymmetry (Schwarzer et al.
2015). Egger’s test was also run by including standard error as a predictor in a meta-regression.
Based on the funnel plots and a non-significant Egger’s test of asymmetry (b=.886,p=.226),
risk of publication bias was judged to be low
Discussion
Proponents of peer assessment argue that it is an effective classroom technique for improving
academic performance (Topping 2009). While previous narrative reviews have argued for the
benefits of peer assessment, the current meta-analysis quantifies the effect of peer assessment
interventions on academic performance within educational contexts. Overall, the results
suggest that there is a positive effect of peer assessment on academic performance in primary,
secondary, and tertiary students. The magnitude of the overall effect size was within the small
to medium range for effect sizes (Sawilowsky 2009). These findings also suggest that that the
benefits of peer assessment are robust across many contextual factors, including different
feedback and educational characteristics.
Recently, researchers have increasingly advocated for the role of assessment in promoting
learning in educational practice (Wiliam 2018). Peer assessment forms a core part of theories
of formative assessment because it is seen as providing new information about the learning
process to the teacher or student, which in turn facilitates later performance (Pellegrino et al.
2001). The current results provide support for the position that peer assessment can be an
effective classroom technique for improving academic performance. The result suggest that
peer assessment is effective compared to both no assessment (which often involved ‘teaching
as usual’) and teacher assessment, suggesting that peer assessment can play an important
Fig. 4 A funnel plot showing the relationship between standard error and observed effect size for the academic
performance meta-analysis
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formative role in the classroom. The findings suggest that structuring classroom activities in a
way that utilises peer assessment may be an effective way to promote learning and optimise the
use of teaching resources by permitting the teacher to focus on assisting students with greater
difficulties or for more complex tasks. Importantly, the results indicate that peer assessment
can be effective across a wide range of subject areas, education levels, and assessment types.
Pragmatically, this suggests that classroom teachers can implement peer assessment in a
variety of ways and tailor the peer assessment design to the particular characteristics and
constraints of their classroom context.
Notably, the results of this quantitative meta-analysis align well with past narrative reviews
(e.g., Black and Wiliam 1998a;Topping1998; van Zundert et al. 2010). The fact that both
quantitative and qualitative syntheses of the literature suggest that peer assessment can be
beneficial provides a stronger basis for recommending peer assessment as a practice. However,
several of the moderators of the effectiveness of peer feedback that have been argued for in the
available narrative reviews (e.g., rubrics; Panadero and Jonsson 2013) have received little
support from this quantitative meta-analysis. As detailed below, this may suggest that the
prominence of such feedback characteristics in narrative reviews is more driven by theoretical
considerations rather than quantitative empirical evidence. However, many of these moderat-
ing variables are complex, for example, rubrics can take many forms, and due to this
complexity may not lend themselves as well to quantitative synthesis/aggregation (for a
detailed discussion on combining qualitative and quantitative evidence, see Gorard 2002).
Mechanisms and Moderators
Indeed, the current findings suggest that the feedback characteristics deemed important by
current theories of peer assessment may not be as significant as first thought. Previously,
individual studies have argued for the importance of characteristics such as rubrics (Panadero
and Jonsson 2013), anonymity (Bloom & Hautaluoma, 1987), and allowing students to
practice peer assessment (Smith, Cooper, & Lancaster, 2002). While these feedback charac-
teristics have been shown to affect the efficacy of peer assessment in individual studies, we
find little evidence that they moderate the effect of peer assessment when analysed across
studies. Many of the current models of peer assessment rely on qualitative evidence, theoretical
arguments, and pedagogical experience to formulate theories about what determines effective
peer assessment. While such evidence should not be discounted, the current findings also point
to the need for better quantitative and experimental studies to test some of the assumptions
embedded in these models. We suggest that the null findings observed in this meta-analysis
regarding the proposed moderators of peer assessment efficacy should be interpreted cautious-
ly, as more studies that experimentally manipulate these variables are needed to provide more
definitive insight into how to design better peer assessment procedures.
While the current findings are ambiguous regarding the mechanisms of peer assessment, it
is worth noting that without a solid understanding of the mechanisms underlying peer
assessment effects, it is difficult to identify important moderators or optimally use peer
assessment in the classroom. Often the research literature makes somewhat broad claims about
the possible benefits of peer assessment. For example, Topping (1998,p.256)suggestedthat
peer assessment may, ‘promote a sense of ownership, personal responsibility, and motiva-
tion…[and] might also increase variety and interest, activity and interactivity, identification
and bonding, self-confidence, and empathy for others’. Others have argued that peer
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assessment is beneficial because it is less personally evaluative—with evidence suggesting that
teacher assessment is often personally evaluative (e.g., ‘good boy, that is correct’)whichmay
have little or even negative effects on performance particularly if the assessee has low self-
efficacy (Birney, Beckmann, Beckmann & Double 2017; Double and Birney 2017,2018;
Hattie and Timperley 2007). However, more research is needed to distinguish between the many
proposed mechanisms for peer assessment’s formative effects made within the extant literature,
particularly as claims about the mechanisms of the effectiveness of peer assessment are often
evidenced by student self-reports about the aspects of peer assessment they rate as useful. While
such self-reports may be informative, more experimental research that systematically manipulates
aspects of the design of peer assessment is likely to provide greater clarity about what aspects of
peer assessment drive the observed benefits.
Our findings did indicate an important role for grading in determining the effectiveness
of peer feedback. We found that peer grading was beneficial for tertiary students but not
beneficial for primary or secondary school students. This finding suggests that grading
appears to add little to the peer feedback process in non-tertiary students. In contrast, a
recent meta-analysis by Sanchez et al. (2017) on peer grading found a benefit for non-
tertiary students, albeit based on a relatively small number of studies compared with the
current meta-analysis. In contrast, the present findings suggest that there may be signif-
icant qualitative differences in the performance of peer grading as students develop. For
example, the criteria students use to assesses ability may change as they age (Stipek and
Iver 1989). It is difficult to ascertain precisely why grading has positive additive effects in
only tertiary students, but there are substantial differences in pedagogy, curriculum,
motivation of learning, and grading systems that may account for these differences. One
possibility is that tertiary students are more ‘grade orientated’and therefore put more
weight on peer assessment which includes a specific grade. Further research is needed to
explore the effects of grading at different educational levels.
One of the more unexpected findings of this meta-analysis was the positive effect of peer
assessment compared to teacher assessment. This finding is somewhat counterintuitive given
the greater qualifications and pedagogical experience of the teacher. In addition, in many of the
studies, the teacher had privileged knowledge about, and often graded the outcome assessment.
Thus, it seems reasonable to expect that teacher feedback would better align with assessment
objectives and therefore produce better outcomes. Despite all these advantages, teacher
assessment appeared to be less efficacious than peer assessment for academic performance.
It is possible that the pedagogical disadvantages of peer assessment are compensated for by
affective or motivational aspects of peer assessment, or by the substantial benefits of acting as
an assessor. However, more experimental research is needed to rule out the effects of potential
methodological issues discussed in detail below.
Limitations
A major limitation of the current results is that they cannot adequately distinguish between
the effect of assessing versus being an assessee. Most of the current studies confound
giving and receiving peer assessment in their designs (i.e., the students in the peer
assessment group both provide assessment and receive it), and therefore, no substantive
conclusions can be drawn about whether the benefits of peer assessment extend from
giving feedback, receiving feedback, or both. This raises the possibility that the benefit of
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peer assessment comes more from assessing, rather than being assessed (Usher 2018).
Consistent with this, Lundstrom and Baker (2009) directly compared the effects of giving
and receiving assessment on students’writing performance and found that assessing was
more beneficial than being assessed. Similarly, Graner (1987) found that assessing papers
without being assessed was as effective for improving writing performance as assessing
papers and receiving feedback.
Furthermore, more true experiments are needed, as there is evidence from these results that
they produce more conservative estimates of the effect of peer assessment. The studies
included in this meta-analysis were not only predominantly randomly allocated at the class-
room level (i.e., quasi-experiments), but in all but one case, were not analysed using appro-
priate techniques for analysing clustered data (e.g., multi-level modelling). This is problematic
because it makes disentangling classroom-level effects (e.g., teacher quality) from the inter-
vention effect difficult, which may lead to biased statistical inferences (Hox 1998). While
experimental designs with individual allocation are often not pragmatic for classroom inter-
ventions, online peer assessment interventions appear to be obvious candidates for increased
true experiments. In particular, carefully controlled experimental designs that examine the
effect of specific assessment characteristics, rather than ‘black-box’studies of the effectiveness
of peer assessment, are crucial for understanding when and how peer assessment is most likely
to be effective. For example, peer assessment may be counterproductive when learning novel
tasks due to students’inadequate domain knowledge (Könings et al. 2019).
While the current results provide an overall estimate of the efficacy of peer assessment in
improving academic performance when compared to teacher and no assessment, it should be
noted that these effects are averaged across a wide range of outcome measures, including
science project grades, essay writing ratings, and end-of-semester exam scores. Aggregating
across such disparate outcomes is always problematic in meta-analysis and is a particular
concern for meta-analyses in educational research, as some outcome measures are likely to be
more sensitive to interventions than others (William, 2010). A further issue is that the effect of
moderators may differ between academic domains. For example, some assessment character-
istics may be important when teaching writing but not mathematics. Because there were too
few studies in the individual academic domains (with the exception of writing), we are unable
to account for these differential effects. The effects of the moderators reported here therefore
need to be considered as overall averages that provide information about the extent to which
the effect of a moderator generalises across domains.
Finally, the findings of the current meta-analysis are also somewhat limited by the fact that
few studies gave a complete profile of the participants and measures used. For example, few
studies indicated that ability of peer reviewer relative to the reviewee and age difference
between the peers was not necessarily clear. Furthermore, it was not possible to classify the
academic performance measures in the current study further, such as based on novelty, or to
code for the quality of the measures, including their reliability and validity, because very few
studies provide comprehensive details about the outcome measure(s) they utilised. Moreover,
other important variables such as fidelity of treatment were almost never reported in the
included manuscripts. Indeed, many of the included variables needed to be coded based on
inferences from the included studies’text and were not explicitly stated, even when one would
reasonably expect that information to be made clear in a peer-reviewed manuscript. The
observed effect sizes reported here should therefore be taken as an indicator of average efficacy
based on the extant literature and not an indication of expected effects for specific
implementations of peer assessment.
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Conclusion
Overall, our findings provide support for the use of peer assessment as a formative practice for
improving academic performance. The results indicate that peer assessment is more effective
than no assessment and teacher assessment and not significantly different in its effect from
self-assessment. These findings are consistent with current theories of formative assessment
and instructional best practice and provide strong empirical support for the continued use of
peer assessment in the classroom and other educational contexts. Further experimental work is
needed to clarify the contextual and educational factors that moderate the effectiveness of peer
assessment, but the present findings are encouraging for those looking to utilise peer assess-
ment to enhance learning.
Acknowledgements The authors would like to thank Kristine Gorgen and Jessica Chan for their help coding the
studies included in the meta-analysis.
Appendix
Effect Size Calculation
Standardised mean differences were calculated as a measure of effect size. Standardised mean
difference (d) was calculated using the following formula, which is typically used in meta-
analyses (e.g., Lipsey and Wilson 2001).
d¼Χ2−Χ1
Spooled
where
Spooled ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n1−1ðÞs2
1þn2−1ðÞs2
2
p
n1þn2−2
As standardized mean difference (d) is known to have a slight positive bias (Hedges 1981), we
applied a correction to bias-correct estimates (resulting in what is often referred to as Hedge’sg).
J¼1−
3
4df −1
with
g¼Jd
For studies where there was insufficient information to calculate Hedge’sgusing the above
method, we used the online effect size calculator developed by Lipsey and Wilson (2001)
available http://www.campbellcollaboration.org/escalc. For pre-post design studies where adjust-
ed means were not provided, we used the critical value relevant to the difference between peer
feedback and control groups from the reported pre-intervention adjusted analysis (e.g., Analysis
Educational Psychology Review (2020) 32:481–509 503
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of Covariances) as suggested by Higgins and Green (2011). For pre-post designs studies where
both pre and post intervention means and standard deviations were provided, we used an effect
size estimate based on the mean pre-post change in the peer feedback group minus the mean pre-
post change in the control group, divided by the pooled pre-intervention standard deviation as
such an approach minimised bias and improves estimate precision (Morris 2008).
Variance estimates for each effect size were calculated using the following formula:
Vd¼n1þn2
n1n2
þd2
2n1þn2
ðÞ
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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