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Practical Assessment, Research, and Evaluation Practical Assessment, Research, and Evaluation
Volume 26 Article 16
2021
Variability In The Accuracy Of Self-Assessments Among Low, Variability In The Accuracy Of Self-Assessments Among Low,
Moderate, And High Performing Students In University Education Moderate, And High Performing Students In University Education
Samuel Parra León
University of Jaén, Spain
Antonio Pantoja Vallejo
University of Jaén, Spain
James Byron Nelson
University of the Basque Country, Spain
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León, Samuel Parra; Pantoja Vallejo, Antonio; and Nelson, James Byron (2021) "Variability In The Accuracy
Of Self-Assessments Among Low, Moderate, And High Performing Students In University Education,"
Practical Assessment, Research, and Evaluation
: Vol. 26 , Article 16.
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High Performing Students In University Education High Performing Students In University Education
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This work was supported by the by the Spanish Ministry of Economy and Competitiveness under the
Grant PSI2014-52263-C2-1-P; Junta de Andalucía under the Grant HUM642; Eusko Jaurlaritza
(Government of the Basque Country) under the Grant IT1341-19 from
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vol26/iss1/16
Practical Assessment, Research & Evaluation, Vol 26 No 16 Page 1
León, Vallejo, & Nelson, Validity in Self-Assessment
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Volume 26 Number 16, June 2021 ISSN 1531-7714
Variability In The Accuracy Of Self-Assessments Among Low,
Moderate, And High Performing Students In University
Education
Samuel Parra León, University of Jaén, Spain
Antonio Pantoja Vallejo, University of Jaén, Spain
James Byron Nelson, University of the Basque Country, Spain
The present work empirically examines the validity of Student Self-Assessment (SSA) as an
educational assessment in higher education. We briefly review the principle methodological factors
that could affect SSA validity, as well as the main findings identified in the literature. One empirical
study is presented that compares student-self evaluations on a test with the evaluation made by the
course instructor while controlling for students' experience with SSA, criteria, rubric, and scales used
by the student and teacher, and that the teacher was blind. Results show a strong correlation overall
between the SSA and the instructor’s evaluation and show that lower-performing students tend to
over-estimate their performance while higher-performing students under-estimate their performance.
The results support that SSA is valid for the average student, but less so for those that deviate above
and below average in the absence of measurements of potentially mediating variables. The need to
consider metacognitive factors in SSA is proposed.
Introduction
Evaluation has always been an essential part of the
instructional processes, as it measures and assigns value
to achievement in the teaching and learning process (e.g.,
Ainscow, 1988; Ysseldyke & Matson, 1988). Of existing
types of evaluation, Student Self-Assessment (SSA,
hereafter) has aroused substantial interest in the research
community. Panadero et al. (2016, p. 2) describe SSA as
“…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”.
SSA has been an important topic in the analysis of
teaching and learning processes for decades. The first
important SSA review (Boud & Falchikov, 1989)
provided answers to many doubts that the SSA practice
raised at that time. However, the review raised a series
of unresolved questions about factors that influence
SSA. Since 1989, the popularity of self-assessment has
increased considerably, becoming common practice for
many teachers (e.g., Berry 2011; Black & Wiliam, 1998).
Panadero et al. (2014) showed that 90% of the
professors in their survey had used SSA in their courses
and that 90% indicated a positive experience.
The increasing use of this evaluation technique has
led to heterogeneity in how it is defined and understood.
Currently up to 20 different categories of SSA can be
found in the literature (see Panadero et al., 2016 for
review). These different categories are organized from
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practices that seek the student's summative and general
SSA without any established criteria (e.g., Stanton, 1978),
to those that view SSA as part of the student's self-
regulation process (e.g., Panadero & Alonso-Tapia,
2014). Roberts et al (2019, p. 79) describe the latter as
“… the ability to set, monitor, and reflect on goals and
then set new goals to monitor and reflect upon. This
cycle of learning is needed to be an efficient and effective
active participant in one’s own learning.” In that regard,
self-regulation would represent the highest level of
acquisition achieved by the student within his or her own
learning process and, consequently, of self-evaluation. It
is necessary that teachers stimulate reflection in the
classroom and provide tools and strategies for self-
regulated learning (Torres & Tackett, 2016), which also
includes self-assessment.
Numerous studies (e.g., Dochy et al., 1999) suggest
a relationship between the SSA process and other factors
regarding learning, such as a) improvement in both the
effectiveness and quality of learning (e.g., Brown &
Harris, 2013; Ramdass & Zimmerman, 2008; Topping,
2003); b) the use of self-regulation strategies for learning
(Kostons et al., 2012; Panadero et al., 2016) and c) self-
efficacy (Olina & Sullivan, 2004; Ramdass &
Zimmerman, 2008; c.f., Andrade et al., 2009). SSA is
also deemed important in that it produces active learning
by involving students in the evaluation process (Black &
William, 1998; Nicol & McFarlane-Dick, 2006; Tan,
2012; Taras, 2010).
The role that SSA has taken in educational research,
combined with the heterogeneity in its conception
regarding its purpose, its execution, and even its
interpretation, has produced several methodological
approaches to its study. Consequently, different ideas are
associated with the validity of SSA results (e.g., Boud &
Falchikov, 1989; Falchikov & Boud, 1989; Gordon,
1991; Panadero & Alonso-Tapia, 2013; Ros, 2006; Ward
et al., 2002). The range ways to implement or develop
experiences with SSA requires a theoretical framework
that facilitates its effective implementation.
Ross (2006) has reviewed the reliability, validity, and
usefulness of SSA in education. He highlights some of
the characteristics of SSA that are relevant to effectively
implementing it in the classrooms, such as a) its
reliability as a technique, b) its validity as evidence of the
student's performance, c) its formative nature, and d) its
usefulness as an evaluation technique. Similarly,
Panadero et al., (2016) proposed different factors that
could impact the validity of SSA. These are: a) the
medium of SSA, b) the delay between SSA and
instruction, c) expectations of students, and d) whether
or not criteria for evaluation are provided.
When considering the variables proposed to affect
the validity of SSA, it is necessary to define what is meant
with regard to validity. In general terms, validity refers
to the evidence provided to support or refute the
meaning or explanation given to the evaluation data or
results; “To validate a proposed interpretation or use of
test scores is to evaluate the claims being based on the
test scores. The specific mix of evidence needed for
validation depends on the inferences being drawn and
the assumptions being made.” (Kane, 2006, p. 131). SSA
Validity has been defined as the degree of agreement,
rapprochement, or consistency that exists between the
student’s evaluations and those of the teachers (e.g.,
Andrade, 2019; Gordon, 1991; Ros, 2006).
In the case of SSA, the evaluation made by the
teacher is considered as an expert evaluation against
which to compare the scores that students provide
during SSA. In accord with Kane (23006), the validity
of SSA should be greater the more consistent student
achievement evaluations are with teacher evaluations.
Data in the literature on SSA validity appear
inconclusive. Authors such as Boud and Falchikov
(1989) indicated that, although there was a moderate
consensus between SSA and the judgments expressed by
professors, most of the literature suffered from a
number of errors; methodological, conceptual, and
interpretative, making a general picture of the findings
complex. Some of the main limitations are summarized
below:
• The evaluation scales used were not specific
(e.g., Boud & Tyree, 1979).
• Students and teachers used different evaluation
criteria (Doleys & Renzaglia, 1963; Gaier, 1961;
Keefer, 1971; Mueller, 1970; Murstein, 1965;
Sumner, 1932).
• The dependent variables measure more than
knowledge gained. For example, Davis and
Rand (1980) asked students to report their
overall course performance, without making a
clear distinction between performance and effort
expended. Thus, the dependent variable
sometimes reflected the effort invested by the
student, providing a high degree of subjectivity
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and variability (e.g., Filene, 1969; Davis & Rand,
1980).
• Abuse of reporting correlations between SSA
and teacher evaluation (e.g., Gaier, 1961; Doleys
& Renzaglia, 1963; Morton & Macbeth, 1977).
The correlation coefficient is sensitive to atypical
scores, and is not the best technique when
assessing a group that may not be heterogeneous
with respect to variables that could affect the
correlation. Interpretations that compare SSA
and teacher evaluation studies assume that
individuals within the group share the same SSA
ability (e.g., Ward et al., 2002).
There is a consensus that a moderate correlation
between SSA and teacher evaluations exists, but there
are also cases of a lack of correspondence that could call
into question the validity of SSA. Some studies show
trends towards overvaluation or undervaluation
(precision errors). The variables that conclusively predict
these trends are not known (Boud & Falchikov, 1989).
The most common precision error reported is
overestimation. It is generally assumed, with some
exceptions, that students' SSA are higher than that of the
teachers. There are also results that indicate that
cognitive ability relates to precision errors.
Overestimation occurs in young children, for example,
and that has been attributed to the absence of the ability
to assess achievement based on a criterion (Butler, 1990).
Students who usually achieve better marks tend to
be more precise/realistic (e.g., Cochran & Spears, 1980;
Doleys & Renzaglia, 1963; Keefer, 1971; Murstein,
1965), or even underestimate (e.g., Sumner, 1932) their
performance, while students who usually achieve worse
marks tend to overvalue (e.g., Daines, 1978; Moreland et
al., 1981). When analyzing the relationship between
students' academic achievement and SSA accuracy,
students tend to be grouped into two groups, high and
low academic achievement. This clustering may be
overshadowing part SSA/Teacher evaluation
relationship. Analyzing the relationship by grouping
students into finer-grained levels of academic
achievement may provide greater sensitivity to changes
in SSA accuracy.
A variable that consistently affects accuracy with
regard to the scores given by teachers is the experience
or ability that the student has with the subject to be
evaluated. Results show better accuracy in SSA with an
increase of experience or mastery of the subject (e.g.,
Ross et al., 1999; Sung et al., 2005; Longhurst & Norton,
1997; Ross, 1998).
Knowledge of the evaluation criteria is also an
important variable. When students do not know the
evaluation criteria, the SSA is less accurate (Panadero &
Romero, 2014). This relationship implies that the
students' experience with SSA and their previous
knowledge about the evaluation criteria, as well as the
instruments used for SSA, will help to achieve more
accurate SSA relative to the teacher (expert) evaluation.
Also, accuracy improves when the criteria are simple
(Pakaslahti & Keltikangas-Järvinen, 2000), or when the
students have participated in the development of
evaluation criteria (Brown & Harris, 2013). However,
the effect of this latter variable is inconsistent, and
contradictory results have been found (Andrade et al.,
2010; Orsmond et al., 2000).
Overall levels of education do not necessarily have
much impact on SSA accuracy. Accuracy in primary
school students (Brown & Harris, 2013; Finn &
Metcalfe, 2014; Ross, 2006) and higher education (Boud
& Falchikov, 1989; Falchikov & Boud, 1989) is very
similar (.30 < r < .50). SSA can be affected by
motivational factors, such whether the SSA affects, or
not, the final grade (e.g., Boekaerts, 2011; Dunning et al.,
2004; Tejeiro et al., 2012). Tejeiro et al., (2012) find that
when the SSA does not influence the grade, the SSA is
very similar to that of the teacher. When the SSA
influences the grade, the discrepancy increases notably
with overestimates becoming more likely (Boud &
Falchikov, 1998). Moreover, different motivational
components can influence SSA. In a meta-analysis,
Sitzmann et al., (2010) concluded that affective factors
(e.g., satisfaction with the evaluation outcomes) have a
greater impact on SSA than those associated with
cognitive learning, which they determined to be only
moderately related to SSA accuracy.
Although the studies discussed so far have shown a
moderate level of precision of SSA, the literature shows
varied results on the validity of this test (e.g., Brown &
Harris, 2014; Dunning et al., 2004; Eva & Regher, 2005;
Lew et al., 2010). These works show that the agreement
between SSA and other measures (test grade, expert
judgments, etc...) is moderate only in the best of cases
(e.g., Brown & Harris, 2013). Though correlations vary
from .2 to .8, there are few studies that report
correlations higher than 0.6 (e.g., Brown & Harris, 2013;
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Falchikov & Boud, 1989). Nevertheless, Panadero et al.,
(2016) propose that, even being an imprecise measure
with an unknown amount of error, its use continues to
be beneficial for teaching practice (Andrade, 2010;
McMillan & Hearn, 2008).
As we have discussed, SSA precision / validity can
be affected by numerous methodological factors. Some
of those variables described above have been considered
in the analysis of SSA validity. However, we have
identified no studies that explicitly controlled for the
variables already identified in the literature as
"threatening" as a whole, when assessing the accuracy of
the SSA.
The main objective of the study we present is to
assess the validity of SSA as evaluation method. The
study was conducted with a natural university sample
while controlling factors that threaten SSA's validity. We
controlled the evaluation criteria (teacher and students
were familiar with the evaluation criteria); the scale and
rubric used (teacher and students used the same scale
and rubric for the evaluation); all students had previous
SSA experience; the SSA was conducted at the end of
the course when student’s knowledge should be at its
highest; and the examiner was blind to the identity of the
student being evaluated. Finally, we analyzed how the
precision of SSA can vary according to the level of
achievement acquired by the students beyond a simple
pass-fail categorization (cf., Ćukušić, et al., 2014). To
achieve this, we not only focus on students who pass or
fail, but we also set different levels of academic
achievement to ensure sensitivity in the accuracy of self-
assessments made by students, with respect to the
evaluation of the teacher (expert).
Method
Participants
Sixty-four students from the third year of the
Degree in Social Education of the University of Jaén
(Spain) participated in the experiment. The sample
constitutes a natural group. The age of the students was
between 20.54 and 39.62 years (Median 22.48 years). The
sample was 82.54% female and 17.46% male. These
percentages are proportional to the distribution of males
and females in the total population of students in Spain
(Spanish National Institute of Statistics, 2015). One male
student chose not to participate, so the final number of
participants was 63.
Instruments
The test that was used as a basis to evaluate the
validity of the SSA was the final exam of the theoretical
component one of the subjects of the third year of the
degree in Social Education. This test consisted of six
open-ended questions, four of them with a short answer
(e.g., requiring 1 to 2 paragraphs to answer correctly) and
the other two with a long answer (requires 1 to 2 sheets
to respond correctly). The questions were drawn from
the most prevalent theoretical contents of the subject.
An example of a short question is “What are the principles
of guidance? Explain briefly what they are.” An example of a
Table 1. Example of evaluation criteria by rubric.
Degree of achievement
Description of the achievement
Level 1(Excellent) 100%
The student includes the required content and demonstrates mastery of it. The
information is relevant, accurate and written in a coherent manner.
Level 2 (Good) 50-75%
The student includes three parts of the required content and demonstrates mastery
of it. The information is relevant, accurate and written in a coherent manner.
Level 3 (Medium) 25-50%
The student includes half of the required content and demonstrates partial mastery
of the content. The information is partially relevant, accurate.
Level 4 (Poor) 0-25%
The student includes summary information of the required content and does not
demonstrate mastery of it. The information is not relevant, accurate and is not
written in a coherent manner.
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León, Vallejo, & Nelson, Validity in Self-Assessment
long question is “Explain the phases that should be followed in
the development of an Orientation Program (as we saw in topic
3).” The rubric for the evaluation of the questions is
presented in Table 1. To evaluate the validity of the
rubric, 5 expert teachers evaluated 5 exams with the
rubric designed. The intraclass correlation coefficient
was .962.
At the beginning of the written test students were
given instructions to complete the test and provided the
point distribution for the various question types (e.g., up
to one point for each short question answered correctly
and up to 3 points for each long question answered). The
maximum score that could be obtained in the test was
ten. Students were told that once the test was completed,
they should calculate the grade that they expect to obtain
on the test, based on their performance, and write it in a
box titled "Self-Assessment” that appeared in the upper
left of the sheet. In the study, the teacher's score was
assumed to be the accurate one and both evaluations
(expert-teacher and student) followed the same criteria.
Procedure
At the beginning of the course, students were
informed about the evaluation criteria and were told in
the course's practice sessions that SSA would be used to
evaluate the work they gave to the professor. Thus, and
in accordance with the recommendations of the
literature (Doleys & Renzaglia, 1963, Gaier, 1961,
Keefer, 1971, Mueller, 1970, Murstein, 1965, Sumner,
1932), we ensured that students were aware of the
evaluation criteria and practiced in their use. In order to
control the bias caused by lack of knowledge about the
use of SSA, students received instruction on how to
evaluate works using criteria established by a rubric, in
the same way as the teacher would do when evaluating
the students’ exams. Thus, during SSA students were
practiced at evaluating works using rubric-based criteria
and used the same criteria as the teacher, reducing the
influence of factors not related to the knowledge
assessed in the exam (e.g., Filene, 1969; Davis & Rand,
1980). The classes on how to perform SSA were part of
the content of the subject regarding how achievement
can be quantified.
On the day of the written test, the students were
seated, the exams were distributed, and the previously
mentioned instructions were read. Participants were
informed of the purpose of the investigation and that
participation in it was entirely voluntary. Those who did
not want to participate simply left the self-assessment
box blank. Students were informed that any student who
had participated in the study and wanted to receive
information about the findings could email the teacher
to receive the information. The degree of participation
was 98.4%.
Before beginning the evaluation of the exams by the
teacher, the first page of each exam containing the
student's identifying data, the exam questions, and the
SSA response was removed. Thus, the teacher was blind
to the identity of the student and his/her self-
assessment.
Statistical Analysis
The scores resulting from the evaluation by the
teacher (hereinafter called Exam) and the SSA responses
issued by the students (hereinafter called SSA) were
recorded for each student. The results were evaluated
initially using the Pearson correlation coefficient (r) and
linear regression was used to characterize the
relationship between the variables. Participants were
further grouped into Fail (<5), Pass (≥ 5 and <7), Very
Good (≥ 7 and <9) and Outstanding (≤9) categories
based on the Exam. These groupings were not arbitrary
but followed common evaluation practices in university
education (e.g., as defined by Real Decreto 1125/2003,
5 de September). Student’s t-tests were used to compare
each groups Exam score to its’ SSA. Percent agreement
between the SSA and Exam groupings as well as Cohen’s
Kappa were calculated.
Effect sizes were computed using Hedges g, as it is
useful with small sample sizes (Hedges, 1981).
Bootstrapping was used to determine the confidence
intervals around the effect sizes using methods described
by Efron (1992). Five-thousand random samples (with
replacement) were drawn from each Exam grouping.
The bootstrapping, and the associated statistical
estimates, were made with the dabestr R package (Ho et
al., 2018).
Results
Table 2 shows the descriptive statistics for the
Exam and Self-assessment (SSA) variables. As can be
seen in these data, the means of both variables are very
close (6.62 and 6.75). Overall, there was no difference
between SSA and Exam scores, F < 1.
The inter-rater agreement between the SSA and the
teacher’s evaluation with respect to the grouping (Fail,
Pass, Very Good, Outstanding) was good (63.5%, K =
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.61). Figure 1 shows the overall correlation between SSA
(X) and Exam performance (Y) on the left along with
the regression line of best fit predicting Exam grade with
SSA. The points at right show the distribution of the
scores, where it is evident that there were no outliers or
extreme scores in either the SSA or the Exam. Within
Figure 1, at left, the different symbols represent the
scores grouped by exam achievement. Overall, exam
scores were predictable given SA scores, r = .66, r2 = .44,
p < .0001. The SSA scores of failing students were
shifted to the right of the regression line, indicating that
their SSA overestimated their exam score (1.99 points
overestimation, t(7) = 5.39, p = .001). Those who passed
were more accurate in their SA, with scores spanning the
regression line (.292 overestimation, t(24) = 1.21, p =
.24). Those doing “very good” under-estimated their
final grade, with scores being shifted to the left of the
regression line (-.40, t(23) = 5.21, p < .0001), as did
those doing “Outstanding”, who underestimated their
grade, on average, by -.81, t(5) = 2.74, p = .04.
The regression lines within each grouping of exam
performance (dotted lines in Figure 1) suggest little
relationship between SSA and Exam performance,
beyond the achievement (Fail, Pass, Very Good, and
Outstanding) groupings. Examining the relationship
between Exam Grade and SSA within each achievement
grouping would yield weak conclusions due to the small
sample sizes within each group. To remove the
influence of the exam grouping, we subtracted each
grouping’s mean exam score from each exam score so.
Table 2. Descriptive statistics for the Exam and Self-assessment variables by level of achievement
1
Range
𝒙
̅
S
S2
Skew
(Tip.
Error.)
Kurtosis
(Tip. Error)
Exam
Total
3.50-10
6.62
1.65
2.74
0.06 (0.3)
-0.75 (0.59)
Fail
3.50-4.5
4.04
0.41
0.17
-0.27 (0.75)
-1.93 (1.48)
Pass
5.00-6.9
5.72
0.62
0.38
0.52 (0.46)
-0.89 (0.90)
Very Good
7.00-8.9
7.71
0.55
0.30
0.84 (0.47)
-0.37 (0.92)
Outstanding
9.00-10
9.52
0.35
0.13
-0.08 (0.85)
-0.29 (1.74)
SSA
Total
4.00-9.7
6.75
1.30
1.69
0.04 (0.3)
-0.74 (0.59)
Fail
4.00-7.5
6.03
1.23
1.52
-0.18 (0.75)
-0.77 (1.48)
Pass
4.50-
8.50
6.01
1.07
1.14
0.85 (0.46)
0.53 (0.90)
Very Good
5.50-
8.60
7.30
0.84
0.70
-0.45 (0.47)
-0.29 (0.92)
Outstanding
7.50-
9.70
8.70
0.73
0.54
-0.52 (0.85)
1.17 (1.74)
1
Note. Exam refers to the variable measured by the Exam score determined by the teacher. SSA refers to the student’s self-assessment. Total
shows the scores by grouping all the students in the class. Fail, Pass, Very Good and Outstanding are classifications according to the score
given by the teacher on the test.
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Figure 1. Exam Grade by Self-Assessment Grade
2
that each group had the same mean (zero). Thus, the
only source of variation remaining in the exam scores
was that within each achievement group. There was no
relationship within achievement categories between the
Exam and the SSA, r = .19, p = .13. The lack of
relationship is, perhaps, not surprising given that there is
a restricted range of exam scores within each exam
achievement category.
Given that the sizes of the achievement groupings were
different, a bootstrap analysis (5000 samples) was
performed to estimate the population effect size of each
comparison. Figure 2 shows the results of the
bootstrapping (F: Fail, P: Pass, V: Very Good, and O:
Outstanding) for difference between the SSA
assessment (subscript s) and the exam score (subscript
e). Positive effect sizes show an overestimation of
performance by SSA and negative effect sizes show an
2
The vertical axis represents the scores obtained by the students in the exam (Exam Grade), on the horizontal axis are Self-Assessment
(SSA) scores. The left part of the figure shows the linear regression between SSA and Exam Grade. The regression equation is shown at
bottom right and the overall line of best fit is shown in black. Scores are shown by group (Fail = open circles, Pass = black triangles, Very
Good = open squares, and Outstanding = black circles) with the lines of best fit within groups shown in dashed lines (Blue for Fail, red for
Pass, green for Very Good and purple for Outstanding). The distributions of the scores for the variable Exam (E) and for the variable Self-
assessment (SSA) are shown to the right of the figure.
under-estimation. Effects near zero indicate similar SSA
and Exam scores. The following effect sizes were
obtained: Fail g = 2.04, [CI95% 0.74, 3.18], Pass, g = .33
[CI95% -0.197, 0.88], Very Good g = -.57, [CI95% -1.12, -
0.05] and Outstanding, g = -1.31, [CI95% -2.45, -0.10]. As
can be seen in the figure, the Fail group shows a robust
overestimation, the Pass and Very Good groups show
an accurate and slight underestimation, respectively, and
the Outstanding group shows a robust underestimation.
Conclusions
The main objective of this work was to analyze the
relationship between academic achievement and SSA
accuracy in university education while ensuring that
teachers and students used the same evaluation criteria
(cf., Admiraal et al., 2015), used the same rubric and
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León, Vallejo, & Nelson, Validity in Self-Assessment
Figure 2. Paired Hedges' g for the 4 groups (F: Fail, P: Pass, V: Very Goods, and O: Outstanding)
3
scales (cf., Alameddine et al., 2018), that students were
experienced in SSA (cf., Bolívar-Cruz, & Verano-
Tacoronte, 2018), and that the teacher was blind to the
authors of the works being evaluated (cf., Aryadoust,
2015). The results showed that students' estimates of
their achievements did not differ, overall, from the
evaluations made by the teacher. That correspondence
demonstrates a degree of SSA validity since, overall,
the students evaluated their performance similarly as
did the teacher. Further, we analyzed SSA precision
with respect to overall academic achievement based on
standard classifications of Fail, Pass, Very Good,
and Outstanding. Those standard classifications
revealed biases in SSA, where lower achievers tended
to over-estimate their performance while higher
achievers underestimated their performance. Defining
validity as the agreement between the evaluations made
by the students and the teacher, our results show that
SSA is a valid assessment of achievement. In Andrade’s
(2019, p.5) terms, there is consistency between the SSA
and teacher’s evaluations as the data show, “the degree
of alignment between students’ and expert raters’
evaluations, avoiding the purer, more rigorous term
accuracy unless it is fitting”.
In the research presented here, all factors
classified by the SSA-accuracy literature as threatening
3
Comparisons are shown in the Cumming estimation plot. The subscript e indicates Exam score; subscript s indicates SSA score. Each
paired mean difference is plotted as a bootstrap sampling distribution. Mean differences are depicted as dots; 95% confidence intervals are
indicated by the ends of the vertical error bars.
have been considered; Knowledge that students have
about the evaluation criteria, the experience that
students have with SSA, and familiarity with the scale,
and standardization of the scale used (e.g., Panadero et
al., 2013; Panadero et al., 2016). Following these
recommendations, our participants had prior
experience and training in evaluating work using
evaluation criteria similar to that used on the SSA.
In the present work the evaluation criteria were
established previously and were provided to the
students before SSA. Additionally, we explained to the
students what SSA is and spent several sessions on its
use to familiarize them with it. To help ensure
maximum objectivity regarding the teacher's
evaluation, a rubric was developed prior to the
evaluation and validated by five experts not involved
in the evaluation (e.g., Brown & Harris, 2013; Panadero
et al., 2012). In addition, the teacher was blind during
the evaluation process to avoid any bias regarding the
score assigned to each participant. The same, familiar,
measurement scale was used both for teacher
evaluation and for SSA.
A common feature in SSA studies is to analyse the
results considering the class as a homogeneous group
(e.g., Bolívar-Cruz, & Verano-Tacoronte, 2018).
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Numerous factors and skills are unevenly distributed
among students in a class (e.g., capacity, performance,
motivation, etc...), and these factors could affect the
ability to assess their own achievements. We provided
additional analysis of the results by grouping students
depending on their test results. The results have shown
that this factor (the level of student achievement within
the course) is related to the ability of students to self-
assess.
The results were analyzed with respect to different
levels of student achievement to enhance sensitivity
beyond the results of a binary pass/fail classification
(c.f., Bolívar-Cruz, & Verano-Tacoronte, 2018). This
decision is consistent with the specialized literature
(e.g., Brown & Harris, 2014; Panadero et al., 2016),
which argues for the importance of considering such
factors in the construction and design of evaluation
instruments as their absence could be detrimental. For
instance, Brown and Harris (2014, p. 23) affirmed “…
awarding grades or basing educational interventions or
changes based on unrealistic or construct-irrelevant
self-assessments is untenable. If self-assessment
processes lead students to conclude wrongly that they
are good or weak in some domain and they base
personal decisions on such false interpretations, harm
could be done, even in classroom settings (e.g., task
avoidance, not enrolling in future subjects)”.
Our findings are consistent with those of Sumer
(1932) showing that better students tend to
underestimate their performance, while those of lesser
skill tend to overestimate (e.g., Daines, 1978; Moreland
et al., 1981). Such an effect is obfuscated when
analysing the class as a whole or using a simple
pass/fail distinction. Despite the over and under
estimation, accuracy was good. Overall, 66.7% of the
students SSA scores were within 1 point of the
teachers’ score, with that portion being 12.5% among
those Failing, 72% among those Passing, 79% for
those classified as Very Good, and 66.7% for those in
the outstanding category.
The proportion of students in each performance
category (Fail: 12.60%, Pass: 39.68%, Very Good:
38.10%, Outstanding: 9.52%) is representative of the
distribution of those proportions in the population of
university students in Spain (e.g., Aranda, et al., 2013).
Due to their different sample sizes, we used the
bootstrap analysis to calculate the effect sizes in each
group, which reaffirmed the previous analysis
regarding the over and underestimation of scores by
SSA.
Use of the student's achievement level regarding
the subject matter as a moderating factor when
analysing accuracy in SSA has been proposed in the
literature. However, in contrast with our research,
studies have tended to focus on high and low
competition rankings (e.gr., Brown & Harris, 2013;
Boud & Falchikov, 1989). Although results have
varied, most of these studies relate higher levels of
student achievement with better accuracy in SSA, and
lower levels of student achievement with
overestimation in SSA. The measure of the student’s
capacity or competence is based on a post-hoc measure
(i.e., the exam), that is also the instrument being
compared to the SSA. Additional measures of
competency, based perhaps on pre-test measures of
classroom performance, could further refine the
relationship of competence and accuracy in SSA.
A possible explanation of these results could be
found in the “Dunning-Kruger effect” (Kruger & Dunning,
1999). According to the authors, the effect is a
cognitive bias in which people erroneously assess their
cognitive ability as greater than it is. Kruger and
Dunning (1999) propose that students lack not only
knowledge of the content, they also lack metacognitive
skills to recognize that they do not possess that
knowledge. The cognitive bias, in “less capable
students”, is attributed to an internal illusion about
their own cognitive abilities, leading to overestimations
of performance. In the case of “more capable
students” the cognitive bias may arise from an
erroneous perception about the external assessment of
their competences being more rigorous than it is,
which leads them to underestimate their performance
on the assessment. Our findings suggest that a-priori
assessment of metacognitive skills (e.g., Kallio et al.,
2018) would help to quantify the relationship of SSA
and exam performance.
The role that SSA has in the processes of Self-
Regulation-Learning (SRL) and on Self-efficacy has
been the focus of many investigations (see for a review
Panadero et al., 2017). However, as far as we know,
there are no studies in the literature where, in addition
to evaluating the formative role of the SSA, they assess
the relationship between the accuracy of the students
in SSA and its formative benefits. Knowing what
factors positively or negatively affect the SSA accuracy
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should have an impact on the formative role in the SRL
process.
In summary, the results found in this study show
that the validity SSA as an evaluative test in university
education is somewhat relative to the skills of the
student. When validity of the test was assessed across
the entire group, the data show a general
correspondence between the SSA and the expert
evaluation. However, when students are grouped
according to their achievement in the evaluation by the
teacher, the measurement of validity is compromised
by the student’s expertise in the subject. There is an
overvaluation in the less competent students, while the
more competent students show undervaluation. These
findings suggest that SSA may be valid test for
evaluating group performance, but less so for the
individual in the absence of further knowledge about
that individual. Assessing metacognitive abilities
related to illusion of control may be one way to better
adjust individual SSA evaluations.
Strengths, Limitations, and further research
The present study provides an accurate validation
of SSA with university sample while controlling factors
that the literature indicates influence the accuracy of
SSA. Analysis considered both the whole group and
different academic achievement sub groups, beyond
pass-fail. Limitations include the use of a natural class
resulting in a small sample, requiring the use of a
bootstrap analysis to infer a population effect. Further
research should consider larger and more diverse
samples (e.g. in other educational levels) and the
measurement of other indicators of achievement and
metacognitive factors that might mediate the accuracy
of SSA.
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Citation:
León, S. P., Vallejo, A. P., & Nelson. J. B.. (2021). Variability in the accuracy of self-assessments among low,
moderate, and high performing students in university education. Practical Assessment, Research & Evaluation,
26(16). Available online: https://scholarworks.umass.edu/pare/vol26/iss1/16/
Corresponding Author
Samuel Parra León
University of Jaén, Spain
email: sparra [at] ujaen.es
14
Practical Assessment, Research, and Evaluation, Vol. 26 [2021], Art. 16
https://scholarworks.umass.edu/pare/vol26/iss1/16
