08832323.2015.1110554
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Self-graded homework: Some empirical tests of
efficacy
Mark Simkin & Debra Stiver
To cite this article: Mark Simkin & Debra Stiver (2016) Self-graded homework:
Some empirical tests of efficacy, Journal of Education for Business, 91:1, 52-58, DOI:
10.1080/08832323.2015.1110554
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Date: 11 January 2016, At: 18:56
JOURNAL OF EDUCATION FOR BUSINESS
2016, VOL. 91, NO. 1, 52–58
http://dx.doi.org/10.1080/08832323.2015.1110554
Self-graded homework: Some empirical tests of efficacy
Mark Simkin and Debra Stiver
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:56 11 January 2016
University of Nevada, Reno, Nevada, USA
ABSTRACT
KEYWORDS
Allowing students to grade their own homework promises many advantages. But can students
perform such grading tasks honestly and accurately? Also, do such assessments vary by gender? To
answer these questions, the authors analyzed the homework scores of 266 students in seven
introductory programming classes. The statistical results were favorable to the hypothesis that
students graded themselves fairly. Gender differences were slight, and more likely attributable to
factors not connected with grading equity.
collaborative learning;
grading equity; selfassessment; self-grading
In collaborative learning (CL), students assume responsibility for some of the educational activities of their
courses. Researchers suggest that CL positively influences
student commitment to courses, positively influences
attendance, and improves student performance (Koppenhaver, 2006; Vander Schee, 2011). They also claim
that CL can increase student engagement and the
amount of learning (Dungan & Mundhenk, 2006; Terezini, Cabrera, Colbeck, Parente, & Bjorklund, 2001).
One interesting dimension of CL is a system that
allows students to grade their own homework. This
approach would seem to challenge instructors in courses
requiring integrative analyses, theory syntheses, or interpretive skills. But, empirically, this system appears to be
more viable in business courses when students receive
grading rubrics and guidance in deducting points for
common errors (Boud, 1989; Panadero & Jonsson,
2013). Similarly, such procedures may be more feasible
where homework problems have unambiguous right
answers (e.g., in accounting or the science, technology,
engineering, and math disciplines).
Self-grading promises benefits to both students and
instructors. One benefit to students is the potential to
enhance interest and commitment to the learning goals of
a course (Dungan & Mundhenk, 2006; Sadler & Good,
2006). If grading takes place on the day it is due, self-grading also offers immediate feedback—a factor believed to
positively influence learning and retention (Edwards,
2007). Student self-grading can also reinforce and deepen
comprehension by helping students understand why one
answer is wrong or another answer is better (Sadler &
Good, 2006). Empirical evidence also suggests that selfgrading improves class attendance, makes the classroom
experience a friendlier, more productive cooperative environment, and provides a shared sense of ownership for
the learning process (Strong, Davis, & Hawks, 2004). Yet
another benefit is the finding that self-grading can
enhance student self-esteem and confidence, and therefore increases positive attitudes about the efficacy of a
course (McVarish & Solloway, 2002). Finally, studies suggest that student self-assessment has the potential to
transform a student’s view of education from a passive,
external experience to an internalized value of lifelong
self-learning (Dungan & Mundhenk, 2006).
Self-grading also offers several potential advantages for
instructors (Foyle, 1988). For example, Sadler and Good
(2006) note that self-grading saves instructional grading
time. Similarly, self-graded homework may increase the
ability to assign more homework (a significant benefit in
large classes) and assign homework that instructors
might otherwise not require—a policy that Chickering
and Gamson (1987) list as one of seven best practices in
undergraduate education (see also Geide-Stevenson,
2009). Another potential advantage is that it may increase
student engagement and transform students from passive
listeners to active evaluators and motivated learners
(Mahlberg, 2015; Stefani, 1992). In the lead author’s
experience, in-class grading also potentially allows for
novel solutions that online software might grade as incorrect. Finally, self-graded homework has potential in
online education, where the lack of grading resources
limits what can be done by a single instructor (Ohland
CONTACT Mark Simkin
[email protected]
University of Nevada, Department of Information Systems, College of Business/026, Reno, NV 89557,
USA.
Color versions of one or more figures in this article are available online www.tandfonline.com/vjeb.
© 2016 Taylor and Francis Group, LLC
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SELF-GRADED HOMEWORK: TESTS OF EFFICACY
et al., 2012). For example, Udacity is a major massive
online open course provider that includes self-grading
among its standard assessments (Boyde, 2013).
Just because instructors can allow students to grade
their own homework does not mean that instructors
should adopt the policy. Again, not all university classes
lend themselves to self-grading. Some students balk at
such tasks, and some instructors feel that in-class tests
adequately motivate students to do whatever work is necessary to master course materials. An additional concern
is the amount of expertise required in the grading process
itself. Several authors believe that students lack such
capabilities (Andrade & Du, 2007; Kirby & Downs,
2007), but a growing body of empirical evidence suggests
the opposite. Both Falchikov and Boud (1989) and Stefani (1992) report that most student marks agreed with
those of their teachers. Similarly, Leach (2012) found no
statistical difference between the mean student (selfassessed) grade and the mean teacher grade in her classes.
Another concern is honesty. If instructors include
homework when computing final course grades, there is
incentive for students to be generous, or worse, to cheat
(Andrade & Du, 2007; Kirby & Downs, 2007). Some
experts believe that this explains why self-assessments are
not more widely used in higher education (Kirby &
Downs, 2007; Thompson, Pilgrim, & Oliver, 2005). Several empirical investigations confirm this concern. Sadler
and Good (2006) compared student homework evaluations with teacher grades for the same work in four of
their general-science classes and found that lower performing students tended to inflate their own low scores. A
study by Leach (2012) of 472 students made these same
observations for lower achieving students, but also found
that higher achieving students tended to underrate themselves. A third study by Strong et al. (2004) of 480 students
in their history classes also found statistical evidence of
grade inflation: 57% of self-assessments resulted in A
grades, compared to 31% of A grades when only teachers
assigned grades. Finally, an anonymous survey by
Edwards (2007) found that up to 20% of the students in a
social statistics class reported they had actually seen other
people cheating, even though up to 95% of these same students claimed that they, themselves, had never cheated.
There are several aspects of CL that have yet to be investigated. One pertains to venue. Empirical studies of student
self-assessment have mostly been conducted in the social
sciences, while similar studies in engineering, computer
science, or business are notable for their absence.
It is also currently not known whether the effectiveness or trustworthiness of self-assessments vary by gender. Past studies of student performance suggest that
they do. For example, earlier research indicates that
women tend to act more ethically than men (Keith,
53
Perrealt, Chin, & Keith, 2009; Kuo, Lin, & Hsu, 2007;
Ones & Viswesvaran, 1998; Pierce, 2014; Wang & Calvano, 2015). This difference in behavior could also apply
when male and female students grade their own homework—a question best answered empirically.
A new study
To examine these matters empirically, the lead author
allowed the students in a set of introductory information
systems (IS) classes in a public, western land-grant university business college to grade their own homework.
The assignment due each day required students to create
as many as five small programming applications in
Visual Basic (Microsoft Corporation, Redmond, WA)
from the end-of-chapter exercises in Schneider (2011,
2014). The number of homework sets varied each semester, but was never less than 20. The experiment ran for
seven consecutive semesters. At the start of each semester, the instructor told students they were on the honor
system, and also noted he would not challenge their selfassigned grades.
Each class began with students viewing a suggested
solution to each problem and a table of maximum points
to award for each exercise. Students could award themselves partial credit for work if they wished, and some
did so in as little as tenths of a point. The instructor also
stressed the importance of completing the homework
because learning how to use a procedural programming
language was a primary learning objective of the class.
For this reason, and to encourage students to do it, the
unchallenged, self-graded homework counted for either
20% or 25% of a student’s final grade in each semester.
We comment on this policy further in the Observations
section.
Table 1 provides statistics for the seven sample classes.
Of 266 total students, 80 were women and 186 were men.
The average total number of points for all homework
points per class was slightly more than 255 points. Problem point totals ranged from one to eight points. Most
homework assignments included problems from end-ofchapter exercises and harder, bonus problems created by
the instructor. Final homework scores were computed as
total homework points (including bonus points) divided
by total nonbonus points. Thus, if a student in Class 1
earned a total of 200 points, his or her homework score
would be 200/192 D 104%. Table 1 displays the highest
potential homework scores for each class in row 7.
Some statistical tests
One way to examine the concern for overly-generous
homework marks is to look at minimum scores. If
54
M. SIMKIN AND D. STIVER
Table 1. Selected class statistics.
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Number of students
Number of women
Number of men
Maximum total homework points
Total nonbonus homework points
Highest potential homework score
Homework as percent of final grade
Class 1
Class 2
Class 3
Class 4
Class 5
Class 6
Class 7
36
8
28
286
192
149%
20%
31
5
26
229
193
119%
20%
39
14
25
253.5
175
145%
20%
39
13
26
256
191
134%
25%
40
13
27
271.5
186
146%
25%
40
13
27
256
188
136%
25%
41
14
27
234.5
189
124%
25%
students are charitable in their homework grading, then
one might expect to see high minimum scores. Table 2
provides minimum homework scores for each of the
seven classes in this study. The table suggests that,
despite the ease with which students could achieve good
homework scores, some participants still found ways to
perform poorly on the homework. Men and women
shared the lowest rankings, with men claiming this dubious honor for four classes and women having the lowest
scores for the remaining three classes. Again, these are
statistics for those students who completed the entire
course (see again Table 1).
Given how easy it was to earn 100% or more on the
homework, it is also interesting to ask what proportion
of students earned at least this grade. Across the entire
sample, 97 students (37%) reached this amount, while
169 students (63%) had homework scores of less than
100%. In the opinion of the authors, it is difficult to
describe the homework grading as generous in view of
these statistics.
Table 2 focuses on the poorest performers in each
class. What about homework performance for the rest of
each class? Table 3 provides homework averages for each
of the seven classes, classified by gender. For all 266 students in the study, the overall class average for homework was about 87%—again, a value that many would
describe as noninflated given students could earn more
or inflate what they had earned.
Across the entire sample of seven classes, men (average homework score of 88.1%) performed better than
women (average score of 84.5%). The difference was statistically significant (p < .01). However, this superior
performance by men was not uniform across all classes—in classes 1, 2, and 5, female students outperformed
male students. Table 3 also suggests that, if students
inflated their homework scores, they were careful about
it: both men and women averaged less than 100% in six
of the seven classes.
Finally, it is useful to examine the range of homework
scores in the sample data. If students contrived the values
in their homework assessments, we would expect values
to cluster around 100%. Alternately, if homework scores
are legitimate, we would anticipate broader ranges. One
way to examine this matter is to observe the sample data
values falling in the interquartile range (IQR) or 25th to
75th percentile. Such an approach allows researchers to
ignore outliers in the data sets and examine the middle
values, which may be more representative of typical student performance.
Table 4 presents the lowest values in Quartile 2 and
the highest value in Quartile 3. Thus, the 25th percentile
value across classes ranged from a low of 43.98% to a
high of 90.05%, while the 75th percentile value ranged
from a low of 76.92% to a high of 120.31%. The differences in these pairs of numbers are the range values
reported in the last row of Table 4. If students were inaccurate graders, the authors expected narrow ranges
because weaker students tend to inflate their scores while
stronger students tend to overpenalize themselves
(Leach, 2012; Sadler & Good, 2006).
Table 4 reports broad ranges that we felt were uncharacteristic of dishonest behavior, but we also wanted to
compare these results with a benchmark class in which
the homework was graded by a professor. The authors
used a prerequisite statistics class for the experimental
classes discussed here as a comparative benchmark.
Accordingly, the authors examined the aggregate homework scores from three such statistics classes.
To perform the comparison, we scaled the student
homework scores for each of the seven IS classes to
Table 2. Minimum homework scores for male and female students in each class.
Table 3. Average homework scores for male and female students
in each class.
Class
1
2
3
4
5
6
7
Men
10.4% 27.5% 29.6% 38.7% 27.2% 52.4% 56.1%
Women
84.9% 71.0% 8.7% 52.9% 64.0% 23.7% 13.2%
Entire class 10.4% 27.5% 8.7% 38.7% 27.2% 23.7% 13.2%
Entire sample
10.4%
8.7%
8.7%
Class
1
2
3
4
5
6
7
Men
101.3% 77.5% 69.9% 89.3% 84.3% 97.1% 94.9%
Women
113.4% 94.8% 53.1% 88.7% 94.0% 84.9% 82.8%
Entire class 104.0% 80.3% 63.9% 89.1% 87.5% 93.2% 90.8%
Entire sample
88.1%
84.5%
87.0%
SELF-GRADED HOMEWORK: TESTS OF EFFICACY
55
Table 4. Homework ranges for the second and third quartiles.
Class:
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Quartile 2 (low)
Quartile 3 (high)
Range
1
2
3
4
5
6
7
Entire sample
90.05%
120.31%
30.26%
60.36%
106.74%
46.37%
43.98%
76.92%
32.94%
71.20%
104.19%
32.98%
71.51%
102.15%
30.65%
80.32%
105.32%
25.00%
74.07%
110.58%
36.51%
71.20%
104.78%
33.58%
100%, and then computed the comparative statistics
shown in Table 5. The range for the IS classes were
smaller in Table 5 than in Table 4 because the scores in
the latter table were scaled to 100% for comparative
purposes.
As might be predicted, the means, medians, and
modes differed between these two classes. By any measure, the homework scores in the IS classes were more
dispersed than those of the benchmark statistics classes.
In the IS classes, the minimum score was lower, the sample standard deviation was larger, and the coefficient of
variation of 29.2% was almost three times larger than the
statistics class value of 10.3%.
As noted previously, the key statistic of interest to the
authors was the interquartile range for the two classes.
Again, the authors believe that evidence of generous student self-grading would include narrow ranges, while
honest self-grading would result in the opposite. The
computed IQR of 33.58% for the unscaled sample values
and 25.2% for the scaled values suggest that there is significant dispersion in the homework scores. The statistics
in Table 5 confirm this belief: the IQR for the benchmark
statistics classes was 13.5%—nearly half that of the
experimental classes
We note that our choice of benchmark statistics classes
here is an imperfect control group for the task at hand.
Indeed, any alternate class taught by a different professor
and on a different subject is easily challenged. On the
other hand, almost all of the students taking the prerequisite statistics classes also took the prerequisite IS classes
used in this study. Because of the independent grading,
we also expected to find greater dispersion in homework
Table 5. Comparative homework statistics for the students taking
the study is classes versus a prerequisite statistics class.
Count (n)
Mean
Sample variance
Sample standard deviation
Coefficient of variation
Minimum
Maximum
Range
First quartile
Median
Third quartile
Interquartile range
IS
STAT
266
67.1%
381.2%
19.5%
29.2%
7.0%
100.0%
93.0%
55.6%
68.8%
80.8%
25.2%
103
86.8%
78.3%
8.9%
10.3%
64.8%
99.2%
34.4%
80.3%
88.6%
93.8%
13.5%
distribution of these alternate classes and were surprised
to find the opposite. Again, we infer that most students
graded themselves fairly in the experimental classes.
A linear regression analysis
The final examinations in the IS classes were 50-question, multiple-choice tests that varied little from one class
to another (the instructor collected test booklets at the
end of each final exam). Table 6 provides average-score
statistics for these tests, summarized by gender.
If students had accurately and honestly graded themselves, we would expect to see a significant relationship
between homework performance and final exam performance. To test the relationship, the Pearson correlation
coefficient was computed. For the sample data, this value
was r D .326. The analysis confirmed a positive, significant linear relationship with a t value of 5.61 (p D .0000).
In order to include gender as an explanatory variable,
the authors estimated the regression coefficients of a linear regression model that used final exam grade as the
dependent variable and gender and total homework
score as independent variables (for similar methodology,
see Geide-Stevenson, 2009; Grove & Wasserman, 2006).
The estimated coefficients for the linear regression (all
significant at p D .000) were:
Final exam D .057 ¡ .073 * Gender + .191 * Homework
(t D 17.01)
(t D 3.63)
(t D 5.51)
Adjusted R2 D .142, F D 23.02.
The homework coefficient showed a statistically significant positive, relationship with final exam scores. If
students cheated on their homework grading, one might
expect this relationship to be less significant and have
less predictive capability. The estimated gender coefficient of ¡.073 means, on average, women scored about
7% lower on their final examinations than did men. This
result contrasts with Picou (2011) who also tested gender
Table 6. Average final examination scores for male and female
students in each class.
Class
1
2
3
4
5
6
7
Men
73.4% 77.4% 77.4% 76.6% 75.6% 71.9% 72.6%
Women
72.8% 70.8% 63.7% 67.1% 66.2% 59.2% 73.6%
Entire class 73.3% 76.4% 72.5% 73.4% 72.5% 67.8% 72.9%
Entire sample
75.0%
67.6%
72.7%
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56
M. SIMKIN AND D. STIVER
as a possible predictor of test performance but found no
statistical significance for this variable.
The adjusted R2 value of .142 indicates that our independent variables collectively explained less than 15% of
the variance of the dependent variable. While such
results are commonly observed in similar behavioral
models, its low magnitude suggests that much of what
explains student achievement on the final examinations
in this class is probably attributable to other factors not
included in our model (e.g., prior programming experience or native intelligence). We speculate that using a
surrogate variable for intelligence such as grade point
average, even if self-reported, might have increased this
value (Bacon & Bean, 2006). Additional variables that
might also affect test performance include commitment
to or interest in the class, work ethic, study skill abilities,
or test anxiety.
We note that some of the questions on the final examination required students to apply what they should have
learned—for example, by identifying an error in unfamiliar code segments or selecting the correct code sequence
for a task from a set of possibilities. These questions
were probably harder than those requiring simple
explanations of terminology, and are likely to especially
affect those students who mostly rely on rote memorization when preparing for tests. None of these variables
were included in the study but are potential criteria for
future research.
Finally, we looked for potential outliers, which we
expected to find in the lower, right portion of Figure 1
marked A—representing students with high homework
scores and low exam scores. We did find outliers, but not
where expected. Most were in area B—an area characterized by low homework scores but high final-exam scores.
How could this be? Examining them individually, the
instructor identified area-B students as capable students
who simply were not inclined to do much homework.
For example, some of them were computer science
majors taking this (business) course as a casual elective.
In total, there were five such students, whose individual
priorities were not completely known to the authors.
Figure 1. A scatter diagram of the sample data.
Observations and additional comments
Four matters worthy of further comment are (a) the
questionable policy of counting self-graded homework as
a sizable percentage of a student’s final grade, (b) the statistical viability of data that have the potential to fall
within narrow ranges, (c) the appropriateness of student
self-grading in programming classes, and (d) the
mechanics of in-class grading.
The lead author’s experience was that counting selfgraded homework in the final course grade was not as risky
as he originally feared. The practice began when students
complained that the homework was a lot of work and
wanted credit for it. The disbursed homework scores and
perhaps student realization that large disconnects between
homework and in-class exam scores were likely to attract
further inquiry made this policy less of a concern. We also
note that the median age for students at this university was
26, suggesting a more mature student body.
With regard to the narrow-range problem, a potential
concern in statistical analyses is a possible lack of variability in the data for independent variables in linear
regression work. This is a problem because low variability in the sample data makes it difficult to estimate
regression parameters with any statistical degree of precision. As noted earlier, and at least for the sample used
here, this fear was (surprisingly) unrealized. In class 1,
for example, aggregate homework scores ranged from a
low of 10.4% to a high of 149.0%. Each of the other classes displayed similarly wide ranges. The scatter diagram
in Figure 1 suggests that low variability was not an issue.
In the subject domain of the present study, there are
usually several methods for achieving a given programming objective, and perhaps the most common question
from students during the grading portion of a given class
period was the appropriateness of alternate solutions. In
many cases, we found that students used novel, and often
creative, approaches to solve homework problems. In our
opinion, the ensuing class discussions about the viability
of such alternate tactics were one of the most educational
portions of the course—especially for the instructor!
Finally, we make several brief qualitative observations
about the mechanics of the self-grading process itself. First,
we note that in-class grading takes valuable class time—an
opportunity cost that some instructors may not wish to
incur. Second, we confirm what others have already noted
about the psychological impact of self-grading. Comments
on end-of-semester class evaluations from students suggested that most liked the process, appreciated the vote of
confidence in their integrity, and believed they were honest
graders. We also note that, when the grading happens at
the beginning of class, the process encourages students to
come to class prepared and on time.
SELF-GRADED HOMEWORK: TESTS OF EFFICACY
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Caveats and directions for future research
There was little evidence for student fudging on homework grading in the sample data of the present study.
Nonetheless, the absence of statistical evidence for grade
misrepresentation does not necessarily mean that none
occurred. One caveat, therefore, is the possibility that
generous self-grading occurred but that our statistical
analyses failed to find it.
We also recognize that, for the study classes at hand,
students could obtain help from such outside sources as
parents, students from prior classes, or each other. In
addition, we observed that the principal debugging technique of some students was trial and error, resulting in
little understanding but (after a sufficient number of
attempts) successful programs. The end result was completed homework deserving of high grades for achievement, but perhaps with little comprehension. This is an
important concern for future research, inasmuch as most
prior studies of student assessments have used achievement as a surrogate for comprehension. This may also
explain why our own regression analysis did not yield
better results. For this reason, future researchers may
prefer to use independent tests of student comprehension, rather than parallel grading by teachers, when measuring the effectiveness of self-grading processes.
We also note that, in the present study, students used
a grading rubric and sometimes, also asked the instructor
about points to deduct for partially-right answers. We
realize that not every class easily accommodates such
practices or that every instructor might feel that class
time is best used for such tasks. Either way, student selfgrading becomes more problematic without such items.
Finally, we note that the data for this study came from
the classes of one instructor and at one university. While
we feel that the students in our sample were typical of
those at alternate institutions, we recognize that this factor affects the generalizability of our results. Future
research would be useful not only to replicate this experiment, but also to find superior methods of implementing
self-grading processes.
Summary and conclusions
One dimension of collaborative learning is a policy that
allows students to grade their own homework. The present study used the aggregate homework and final examination scores from 266 students taking seven entry-level
programming classes to examine the efficacy of this policy. Despite the opportunity for extra credit, we found
many examples of low homework scores, imperfect
homework averages, and wide homework ranges in each
class. Similarly, a comparison of the homework scores of
57
the students in the experimental IS classes with those of
a prerequisite statistics class found that the self-graded
scores were more dispersed, and thus less likely to be
contrived scores.
Using a linear regression model, we found a positive,
statistically significant relationship between homework
and the student’s final examination, suggesting that, if
students misrepresented their homework scores in any
substantive way, it was not statistically identifiable. If students had been generous with their home grading, we
expected to find students with high homework scores
but low exam scores. Instead, we found the opposite.
Gender did not emerge as a distinguishing factor in
our analyses. In our linear regression, this variable was
statistically significant, but of small effect. Our overall
conclusion is that, within the confines of our study, both
male and female students can and do grade their homework honestly.
References
Andrade, H., & Du, Y. (2007). Student responses to criteriareferenced self-assessment. Assessment & Evaluation in
Higher Education, 32, 159–181.
Bacon, D. R., & Bean, B. (2006). GPA in research studies: An
invaluable but neglected opportunity. Journal of Marketing
Education, 28, 35–42.
Boud, D. (1989). The role of self-assessment in student grading.
Assessment and Evaluation in Higher Education, 14, 20–30.
Boyde, E. (2013, March 11). Massive open online courses:
Time and a little money are a worthy investment. Financial
Times. Retrieved from http://www.ft.com/cms/s/2/c101d4cc800f-11e2-96ba-00144feabdc0.html#axzz2Y6TGqKVE.
Chickering, A. W., & Gamson, Z. F. (1987). Seven principles
for good practice in undergraduate education. AAHE Bulletin, 39(7), 3–7.
Dungan, A. T., & Mundhenk, L. G. (2006). Student self-assessment: a tool for engaging management students in their
learning. Teaching and Learning, 3, 54–73.
Edwards, N. M. (2007). Student self-grading in social statistics.
College Teaching, 55, 72–76.
Falchikov, N., & Boud, D. (1989). Student self-assessment in
higher education: a meta-analysis. Review of Educational
Research, 59, 395–430.
Foyle, H. C. (1988, January). Homework: Suggestions for educators. Paper presented at the meeting of the Hutchinson
Chapter of Phi Delta Kappa, McPherson, KS. Retrieved
from http://eric.ed.gov/?id=ED294504
Geide-Stevenson, D. (2009). Does collecting and grading
homework assignments impact student achievement in an
introductory economics course? Journal of Economics and
Economic Education Research, 10(3), 3–14.
Grove, W. A., & Wasserman, T. (2006). Incentives and student
learning: A natural experiment with economics problem
sets. American Economic Review, 96, 447–452.
Keith, N. K., Perrealt, H. R., Chin, M., & Keith, M. (2009). The
effect of gender on the importance of business ethics and
managerial decisions: A student perspective. Delta Pi Epsilon Journal, 51, 125–136.
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:56 11 January 2016
58
M. SIMKIN AND D. STIVER
Kirby, N., & Downs, C. (2007). Self-assessment and the disadvantaged student: Potential for encouraging self-regulated learning.
Assessment & Evaluation in Higher Education, 32, 475–494.
Koppenhaver, G. D. (2006). Absent and accounted for: Absenteeism and cooperative learning Decision Sciences Journal of
Innovative Education, 4, 29–49.
Kuo, F., Lin, C., & Hsu, M. (2007). Assessing gender differences in computer professionals’ self-regulatory efficacy concerning information privacy practices. Journal of Business
Ethics, 73, 145–160.
Leach, L. (2012). Optional self-assessment: Some tensions and
dilemmas. Assessment & Evaluation in Higher Education,
37, 137–147.
Mahlberg, J. (2015). Formative self-assessment college classes
improves self-regulation and retention in first/second year
community college students. Community College Journal of
Research and Practice, 39, 772–783.
McVarish, J., & Solloway, S. (2002). Self-evaluation: Creating a
classroom without unhealthy competitiveness. Educational
Forum, 66, 253–260.
Ohland, M. W., Loughry, M. L., Woehr, D. J., Bullard, L. G.,
Felder, R. M., Finelli, C. J., … Schmucker, D. G. (2012). The
comprehensive assessment of team member effectiveness:
Development of a behaviorally anchored rating scale for
self-and peer evaluation. Academy of Management Learning
& Education, 11, 609–630.
Ones, D. S., & Viswesvaran, C. (1998). Gender, age, and
race differences on overt integrity tests: Results across four
large-state job data sets. Journal of Applied Psychology, 83,
35–42.
Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics
for formative assessment purposes revisited: A review. Educational Research Review, 9, 129–144.
Picou, A. (2011). Does gender, GPA or age influence test
performance in the introductory finance class? A study
using linked questions. Review of Business Research, 11
(4). Retrieved from http://www.freepatentsonline.com/
article/Review-Business-Research/272616380.html
Pierce, J. R. (2014). Sex & gender in ethical decision making: A
critical review and recommendations for future research.
Academy of Management Annual Meeting Proceedings,
2014, 977–982.
Sadler, P. M., & Good, E. (2006). The impact of self-and peergrading on student learning. Educational Assessment, 11,
1–31.
Schneider, D. I. (2011). An introduction of programming using
visual basic 2010. Boston, MA: Prentice Hall.
Schneider, D. I. (2014). An introduction of programming using
visual basic 2012. Boston, MA: Prentice Hall.
Stefani, L. A. (1992). Comparison of collaborative self, peer and
tutor assessment in a biochemistry practical. Biochemical
Education, 20, 148–151.
Strong, B., Davis, M., & Hawks, V. (2004). Self-grading in
large general education classes. College Teaching, 52,
52–57.
Terezini, P. T., Cabrera, A. F., Colbeck, C. L., Parente, J. M., &
Bjorklund, S. A. (2001). Collaborative learning vs. lecture/
discussion: Students’ reported learning gains. Journal of
Engineering Education, 90, 123–130.
Thompson, G., Pilgrim, A., & Oliver, K. (2005). Self-assessment and reflective learning for first-year geography students: A simple guide or simply misguided? Journal of
Geography in Higher Education, 29, 403–420.
Vander Schee, B. A. (2011). Let them decide: student performance and self-selection of weights distribution. Journal of
Education for Business, 86, 352–356.
Wang, L., & Calvano, L. (2015). Is business ethics education
effective? An analysis of gender, personal ethical perspectives, and moral judgment. Journal of Business Ethics, 126,
591–602.
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Self-graded homework: Some empirical tests of
efficacy
Mark Simkin & Debra Stiver
To cite this article: Mark Simkin & Debra Stiver (2016) Self-graded homework:
Some empirical tests of efficacy, Journal of Education for Business, 91:1, 52-58, DOI:
10.1080/08832323.2015.1110554
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Date: 11 January 2016, At: 18:56
JOURNAL OF EDUCATION FOR BUSINESS
2016, VOL. 91, NO. 1, 52–58
http://dx.doi.org/10.1080/08832323.2015.1110554
Self-graded homework: Some empirical tests of efficacy
Mark Simkin and Debra Stiver
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University of Nevada, Reno, Nevada, USA
ABSTRACT
KEYWORDS
Allowing students to grade their own homework promises many advantages. But can students
perform such grading tasks honestly and accurately? Also, do such assessments vary by gender? To
answer these questions, the authors analyzed the homework scores of 266 students in seven
introductory programming classes. The statistical results were favorable to the hypothesis that
students graded themselves fairly. Gender differences were slight, and more likely attributable to
factors not connected with grading equity.
collaborative learning;
grading equity; selfassessment; self-grading
In collaborative learning (CL), students assume responsibility for some of the educational activities of their
courses. Researchers suggest that CL positively influences
student commitment to courses, positively influences
attendance, and improves student performance (Koppenhaver, 2006; Vander Schee, 2011). They also claim
that CL can increase student engagement and the
amount of learning (Dungan & Mundhenk, 2006; Terezini, Cabrera, Colbeck, Parente, & Bjorklund, 2001).
One interesting dimension of CL is a system that
allows students to grade their own homework. This
approach would seem to challenge instructors in courses
requiring integrative analyses, theory syntheses, or interpretive skills. But, empirically, this system appears to be
more viable in business courses when students receive
grading rubrics and guidance in deducting points for
common errors (Boud, 1989; Panadero & Jonsson,
2013). Similarly, such procedures may be more feasible
where homework problems have unambiguous right
answers (e.g., in accounting or the science, technology,
engineering, and math disciplines).
Self-grading promises benefits to both students and
instructors. One benefit to students is the potential to
enhance interest and commitment to the learning goals of
a course (Dungan & Mundhenk, 2006; Sadler & Good,
2006). If grading takes place on the day it is due, self-grading also offers immediate feedback—a factor believed to
positively influence learning and retention (Edwards,
2007). Student self-grading can also reinforce and deepen
comprehension by helping students understand why one
answer is wrong or another answer is better (Sadler &
Good, 2006). Empirical evidence also suggests that selfgrading improves class attendance, makes the classroom
experience a friendlier, more productive cooperative environment, and provides a shared sense of ownership for
the learning process (Strong, Davis, & Hawks, 2004). Yet
another benefit is the finding that self-grading can
enhance student self-esteem and confidence, and therefore increases positive attitudes about the efficacy of a
course (McVarish & Solloway, 2002). Finally, studies suggest that student self-assessment has the potential to
transform a student’s view of education from a passive,
external experience to an internalized value of lifelong
self-learning (Dungan & Mundhenk, 2006).
Self-grading also offers several potential advantages for
instructors (Foyle, 1988). For example, Sadler and Good
(2006) note that self-grading saves instructional grading
time. Similarly, self-graded homework may increase the
ability to assign more homework (a significant benefit in
large classes) and assign homework that instructors
might otherwise not require—a policy that Chickering
and Gamson (1987) list as one of seven best practices in
undergraduate education (see also Geide-Stevenson,
2009). Another potential advantage is that it may increase
student engagement and transform students from passive
listeners to active evaluators and motivated learners
(Mahlberg, 2015; Stefani, 1992). In the lead author’s
experience, in-class grading also potentially allows for
novel solutions that online software might grade as incorrect. Finally, self-graded homework has potential in
online education, where the lack of grading resources
limits what can be done by a single instructor (Ohland
CONTACT Mark Simkin
[email protected]
University of Nevada, Department of Information Systems, College of Business/026, Reno, NV 89557,
USA.
Color versions of one or more figures in this article are available online www.tandfonline.com/vjeb.
© 2016 Taylor and Francis Group, LLC
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SELF-GRADED HOMEWORK: TESTS OF EFFICACY
et al., 2012). For example, Udacity is a major massive
online open course provider that includes self-grading
among its standard assessments (Boyde, 2013).
Just because instructors can allow students to grade
their own homework does not mean that instructors
should adopt the policy. Again, not all university classes
lend themselves to self-grading. Some students balk at
such tasks, and some instructors feel that in-class tests
adequately motivate students to do whatever work is necessary to master course materials. An additional concern
is the amount of expertise required in the grading process
itself. Several authors believe that students lack such
capabilities (Andrade & Du, 2007; Kirby & Downs,
2007), but a growing body of empirical evidence suggests
the opposite. Both Falchikov and Boud (1989) and Stefani (1992) report that most student marks agreed with
those of their teachers. Similarly, Leach (2012) found no
statistical difference between the mean student (selfassessed) grade and the mean teacher grade in her classes.
Another concern is honesty. If instructors include
homework when computing final course grades, there is
incentive for students to be generous, or worse, to cheat
(Andrade & Du, 2007; Kirby & Downs, 2007). Some
experts believe that this explains why self-assessments are
not more widely used in higher education (Kirby &
Downs, 2007; Thompson, Pilgrim, & Oliver, 2005). Several empirical investigations confirm this concern. Sadler
and Good (2006) compared student homework evaluations with teacher grades for the same work in four of
their general-science classes and found that lower performing students tended to inflate their own low scores. A
study by Leach (2012) of 472 students made these same
observations for lower achieving students, but also found
that higher achieving students tended to underrate themselves. A third study by Strong et al. (2004) of 480 students
in their history classes also found statistical evidence of
grade inflation: 57% of self-assessments resulted in A
grades, compared to 31% of A grades when only teachers
assigned grades. Finally, an anonymous survey by
Edwards (2007) found that up to 20% of the students in a
social statistics class reported they had actually seen other
people cheating, even though up to 95% of these same students claimed that they, themselves, had never cheated.
There are several aspects of CL that have yet to be investigated. One pertains to venue. Empirical studies of student
self-assessment have mostly been conducted in the social
sciences, while similar studies in engineering, computer
science, or business are notable for their absence.
It is also currently not known whether the effectiveness or trustworthiness of self-assessments vary by gender. Past studies of student performance suggest that
they do. For example, earlier research indicates that
women tend to act more ethically than men (Keith,
53
Perrealt, Chin, & Keith, 2009; Kuo, Lin, & Hsu, 2007;
Ones & Viswesvaran, 1998; Pierce, 2014; Wang & Calvano, 2015). This difference in behavior could also apply
when male and female students grade their own homework—a question best answered empirically.
A new study
To examine these matters empirically, the lead author
allowed the students in a set of introductory information
systems (IS) classes in a public, western land-grant university business college to grade their own homework.
The assignment due each day required students to create
as many as five small programming applications in
Visual Basic (Microsoft Corporation, Redmond, WA)
from the end-of-chapter exercises in Schneider (2011,
2014). The number of homework sets varied each semester, but was never less than 20. The experiment ran for
seven consecutive semesters. At the start of each semester, the instructor told students they were on the honor
system, and also noted he would not challenge their selfassigned grades.
Each class began with students viewing a suggested
solution to each problem and a table of maximum points
to award for each exercise. Students could award themselves partial credit for work if they wished, and some
did so in as little as tenths of a point. The instructor also
stressed the importance of completing the homework
because learning how to use a procedural programming
language was a primary learning objective of the class.
For this reason, and to encourage students to do it, the
unchallenged, self-graded homework counted for either
20% or 25% of a student’s final grade in each semester.
We comment on this policy further in the Observations
section.
Table 1 provides statistics for the seven sample classes.
Of 266 total students, 80 were women and 186 were men.
The average total number of points for all homework
points per class was slightly more than 255 points. Problem point totals ranged from one to eight points. Most
homework assignments included problems from end-ofchapter exercises and harder, bonus problems created by
the instructor. Final homework scores were computed as
total homework points (including bonus points) divided
by total nonbonus points. Thus, if a student in Class 1
earned a total of 200 points, his or her homework score
would be 200/192 D 104%. Table 1 displays the highest
potential homework scores for each class in row 7.
Some statistical tests
One way to examine the concern for overly-generous
homework marks is to look at minimum scores. If
54
M. SIMKIN AND D. STIVER
Table 1. Selected class statistics.
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:56 11 January 2016
Number of students
Number of women
Number of men
Maximum total homework points
Total nonbonus homework points
Highest potential homework score
Homework as percent of final grade
Class 1
Class 2
Class 3
Class 4
Class 5
Class 6
Class 7
36
8
28
286
192
149%
20%
31
5
26
229
193
119%
20%
39
14
25
253.5
175
145%
20%
39
13
26
256
191
134%
25%
40
13
27
271.5
186
146%
25%
40
13
27
256
188
136%
25%
41
14
27
234.5
189
124%
25%
students are charitable in their homework grading, then
one might expect to see high minimum scores. Table 2
provides minimum homework scores for each of the
seven classes in this study. The table suggests that,
despite the ease with which students could achieve good
homework scores, some participants still found ways to
perform poorly on the homework. Men and women
shared the lowest rankings, with men claiming this dubious honor for four classes and women having the lowest
scores for the remaining three classes. Again, these are
statistics for those students who completed the entire
course (see again Table 1).
Given how easy it was to earn 100% or more on the
homework, it is also interesting to ask what proportion
of students earned at least this grade. Across the entire
sample, 97 students (37%) reached this amount, while
169 students (63%) had homework scores of less than
100%. In the opinion of the authors, it is difficult to
describe the homework grading as generous in view of
these statistics.
Table 2 focuses on the poorest performers in each
class. What about homework performance for the rest of
each class? Table 3 provides homework averages for each
of the seven classes, classified by gender. For all 266 students in the study, the overall class average for homework was about 87%—again, a value that many would
describe as noninflated given students could earn more
or inflate what they had earned.
Across the entire sample of seven classes, men (average homework score of 88.1%) performed better than
women (average score of 84.5%). The difference was statistically significant (p < .01). However, this superior
performance by men was not uniform across all classes—in classes 1, 2, and 5, female students outperformed
male students. Table 3 also suggests that, if students
inflated their homework scores, they were careful about
it: both men and women averaged less than 100% in six
of the seven classes.
Finally, it is useful to examine the range of homework
scores in the sample data. If students contrived the values
in their homework assessments, we would expect values
to cluster around 100%. Alternately, if homework scores
are legitimate, we would anticipate broader ranges. One
way to examine this matter is to observe the sample data
values falling in the interquartile range (IQR) or 25th to
75th percentile. Such an approach allows researchers to
ignore outliers in the data sets and examine the middle
values, which may be more representative of typical student performance.
Table 4 presents the lowest values in Quartile 2 and
the highest value in Quartile 3. Thus, the 25th percentile
value across classes ranged from a low of 43.98% to a
high of 90.05%, while the 75th percentile value ranged
from a low of 76.92% to a high of 120.31%. The differences in these pairs of numbers are the range values
reported in the last row of Table 4. If students were inaccurate graders, the authors expected narrow ranges
because weaker students tend to inflate their scores while
stronger students tend to overpenalize themselves
(Leach, 2012; Sadler & Good, 2006).
Table 4 reports broad ranges that we felt were uncharacteristic of dishonest behavior, but we also wanted to
compare these results with a benchmark class in which
the homework was graded by a professor. The authors
used a prerequisite statistics class for the experimental
classes discussed here as a comparative benchmark.
Accordingly, the authors examined the aggregate homework scores from three such statistics classes.
To perform the comparison, we scaled the student
homework scores for each of the seven IS classes to
Table 2. Minimum homework scores for male and female students in each class.
Table 3. Average homework scores for male and female students
in each class.
Class
1
2
3
4
5
6
7
Men
10.4% 27.5% 29.6% 38.7% 27.2% 52.4% 56.1%
Women
84.9% 71.0% 8.7% 52.9% 64.0% 23.7% 13.2%
Entire class 10.4% 27.5% 8.7% 38.7% 27.2% 23.7% 13.2%
Entire sample
10.4%
8.7%
8.7%
Class
1
2
3
4
5
6
7
Men
101.3% 77.5% 69.9% 89.3% 84.3% 97.1% 94.9%
Women
113.4% 94.8% 53.1% 88.7% 94.0% 84.9% 82.8%
Entire class 104.0% 80.3% 63.9% 89.1% 87.5% 93.2% 90.8%
Entire sample
88.1%
84.5%
87.0%
SELF-GRADED HOMEWORK: TESTS OF EFFICACY
55
Table 4. Homework ranges for the second and third quartiles.
Class:
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Quartile 2 (low)
Quartile 3 (high)
Range
1
2
3
4
5
6
7
Entire sample
90.05%
120.31%
30.26%
60.36%
106.74%
46.37%
43.98%
76.92%
32.94%
71.20%
104.19%
32.98%
71.51%
102.15%
30.65%
80.32%
105.32%
25.00%
74.07%
110.58%
36.51%
71.20%
104.78%
33.58%
100%, and then computed the comparative statistics
shown in Table 5. The range for the IS classes were
smaller in Table 5 than in Table 4 because the scores in
the latter table were scaled to 100% for comparative
purposes.
As might be predicted, the means, medians, and
modes differed between these two classes. By any measure, the homework scores in the IS classes were more
dispersed than those of the benchmark statistics classes.
In the IS classes, the minimum score was lower, the sample standard deviation was larger, and the coefficient of
variation of 29.2% was almost three times larger than the
statistics class value of 10.3%.
As noted previously, the key statistic of interest to the
authors was the interquartile range for the two classes.
Again, the authors believe that evidence of generous student self-grading would include narrow ranges, while
honest self-grading would result in the opposite. The
computed IQR of 33.58% for the unscaled sample values
and 25.2% for the scaled values suggest that there is significant dispersion in the homework scores. The statistics
in Table 5 confirm this belief: the IQR for the benchmark
statistics classes was 13.5%—nearly half that of the
experimental classes
We note that our choice of benchmark statistics classes
here is an imperfect control group for the task at hand.
Indeed, any alternate class taught by a different professor
and on a different subject is easily challenged. On the
other hand, almost all of the students taking the prerequisite statistics classes also took the prerequisite IS classes
used in this study. Because of the independent grading,
we also expected to find greater dispersion in homework
Table 5. Comparative homework statistics for the students taking
the study is classes versus a prerequisite statistics class.
Count (n)
Mean
Sample variance
Sample standard deviation
Coefficient of variation
Minimum
Maximum
Range
First quartile
Median
Third quartile
Interquartile range
IS
STAT
266
67.1%
381.2%
19.5%
29.2%
7.0%
100.0%
93.0%
55.6%
68.8%
80.8%
25.2%
103
86.8%
78.3%
8.9%
10.3%
64.8%
99.2%
34.4%
80.3%
88.6%
93.8%
13.5%
distribution of these alternate classes and were surprised
to find the opposite. Again, we infer that most students
graded themselves fairly in the experimental classes.
A linear regression analysis
The final examinations in the IS classes were 50-question, multiple-choice tests that varied little from one class
to another (the instructor collected test booklets at the
end of each final exam). Table 6 provides average-score
statistics for these tests, summarized by gender.
If students had accurately and honestly graded themselves, we would expect to see a significant relationship
between homework performance and final exam performance. To test the relationship, the Pearson correlation
coefficient was computed. For the sample data, this value
was r D .326. The analysis confirmed a positive, significant linear relationship with a t value of 5.61 (p D .0000).
In order to include gender as an explanatory variable,
the authors estimated the regression coefficients of a linear regression model that used final exam grade as the
dependent variable and gender and total homework
score as independent variables (for similar methodology,
see Geide-Stevenson, 2009; Grove & Wasserman, 2006).
The estimated coefficients for the linear regression (all
significant at p D .000) were:
Final exam D .057 ¡ .073 * Gender + .191 * Homework
(t D 17.01)
(t D 3.63)
(t D 5.51)
Adjusted R2 D .142, F D 23.02.
The homework coefficient showed a statistically significant positive, relationship with final exam scores. If
students cheated on their homework grading, one might
expect this relationship to be less significant and have
less predictive capability. The estimated gender coefficient of ¡.073 means, on average, women scored about
7% lower on their final examinations than did men. This
result contrasts with Picou (2011) who also tested gender
Table 6. Average final examination scores for male and female
students in each class.
Class
1
2
3
4
5
6
7
Men
73.4% 77.4% 77.4% 76.6% 75.6% 71.9% 72.6%
Women
72.8% 70.8% 63.7% 67.1% 66.2% 59.2% 73.6%
Entire class 73.3% 76.4% 72.5% 73.4% 72.5% 67.8% 72.9%
Entire sample
75.0%
67.6%
72.7%
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:56 11 January 2016
56
M. SIMKIN AND D. STIVER
as a possible predictor of test performance but found no
statistical significance for this variable.
The adjusted R2 value of .142 indicates that our independent variables collectively explained less than 15% of
the variance of the dependent variable. While such
results are commonly observed in similar behavioral
models, its low magnitude suggests that much of what
explains student achievement on the final examinations
in this class is probably attributable to other factors not
included in our model (e.g., prior programming experience or native intelligence). We speculate that using a
surrogate variable for intelligence such as grade point
average, even if self-reported, might have increased this
value (Bacon & Bean, 2006). Additional variables that
might also affect test performance include commitment
to or interest in the class, work ethic, study skill abilities,
or test anxiety.
We note that some of the questions on the final examination required students to apply what they should have
learned—for example, by identifying an error in unfamiliar code segments or selecting the correct code sequence
for a task from a set of possibilities. These questions
were probably harder than those requiring simple
explanations of terminology, and are likely to especially
affect those students who mostly rely on rote memorization when preparing for tests. None of these variables
were included in the study but are potential criteria for
future research.
Finally, we looked for potential outliers, which we
expected to find in the lower, right portion of Figure 1
marked A—representing students with high homework
scores and low exam scores. We did find outliers, but not
where expected. Most were in area B—an area characterized by low homework scores but high final-exam scores.
How could this be? Examining them individually, the
instructor identified area-B students as capable students
who simply were not inclined to do much homework.
For example, some of them were computer science
majors taking this (business) course as a casual elective.
In total, there were five such students, whose individual
priorities were not completely known to the authors.
Figure 1. A scatter diagram of the sample data.
Observations and additional comments
Four matters worthy of further comment are (a) the
questionable policy of counting self-graded homework as
a sizable percentage of a student’s final grade, (b) the statistical viability of data that have the potential to fall
within narrow ranges, (c) the appropriateness of student
self-grading in programming classes, and (d) the
mechanics of in-class grading.
The lead author’s experience was that counting selfgraded homework in the final course grade was not as risky
as he originally feared. The practice began when students
complained that the homework was a lot of work and
wanted credit for it. The disbursed homework scores and
perhaps student realization that large disconnects between
homework and in-class exam scores were likely to attract
further inquiry made this policy less of a concern. We also
note that the median age for students at this university was
26, suggesting a more mature student body.
With regard to the narrow-range problem, a potential
concern in statistical analyses is a possible lack of variability in the data for independent variables in linear
regression work. This is a problem because low variability in the sample data makes it difficult to estimate
regression parameters with any statistical degree of precision. As noted earlier, and at least for the sample used
here, this fear was (surprisingly) unrealized. In class 1,
for example, aggregate homework scores ranged from a
low of 10.4% to a high of 149.0%. Each of the other classes displayed similarly wide ranges. The scatter diagram
in Figure 1 suggests that low variability was not an issue.
In the subject domain of the present study, there are
usually several methods for achieving a given programming objective, and perhaps the most common question
from students during the grading portion of a given class
period was the appropriateness of alternate solutions. In
many cases, we found that students used novel, and often
creative, approaches to solve homework problems. In our
opinion, the ensuing class discussions about the viability
of such alternate tactics were one of the most educational
portions of the course—especially for the instructor!
Finally, we make several brief qualitative observations
about the mechanics of the self-grading process itself. First,
we note that in-class grading takes valuable class time—an
opportunity cost that some instructors may not wish to
incur. Second, we confirm what others have already noted
about the psychological impact of self-grading. Comments
on end-of-semester class evaluations from students suggested that most liked the process, appreciated the vote of
confidence in their integrity, and believed they were honest
graders. We also note that, when the grading happens at
the beginning of class, the process encourages students to
come to class prepared and on time.
SELF-GRADED HOMEWORK: TESTS OF EFFICACY
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Caveats and directions for future research
There was little evidence for student fudging on homework grading in the sample data of the present study.
Nonetheless, the absence of statistical evidence for grade
misrepresentation does not necessarily mean that none
occurred. One caveat, therefore, is the possibility that
generous self-grading occurred but that our statistical
analyses failed to find it.
We also recognize that, for the study classes at hand,
students could obtain help from such outside sources as
parents, students from prior classes, or each other. In
addition, we observed that the principal debugging technique of some students was trial and error, resulting in
little understanding but (after a sufficient number of
attempts) successful programs. The end result was completed homework deserving of high grades for achievement, but perhaps with little comprehension. This is an
important concern for future research, inasmuch as most
prior studies of student assessments have used achievement as a surrogate for comprehension. This may also
explain why our own regression analysis did not yield
better results. For this reason, future researchers may
prefer to use independent tests of student comprehension, rather than parallel grading by teachers, when measuring the effectiveness of self-grading processes.
We also note that, in the present study, students used
a grading rubric and sometimes, also asked the instructor
about points to deduct for partially-right answers. We
realize that not every class easily accommodates such
practices or that every instructor might feel that class
time is best used for such tasks. Either way, student selfgrading becomes more problematic without such items.
Finally, we note that the data for this study came from
the classes of one instructor and at one university. While
we feel that the students in our sample were typical of
those at alternate institutions, we recognize that this factor affects the generalizability of our results. Future
research would be useful not only to replicate this experiment, but also to find superior methods of implementing
self-grading processes.
Summary and conclusions
One dimension of collaborative learning is a policy that
allows students to grade their own homework. The present study used the aggregate homework and final examination scores from 266 students taking seven entry-level
programming classes to examine the efficacy of this policy. Despite the opportunity for extra credit, we found
many examples of low homework scores, imperfect
homework averages, and wide homework ranges in each
class. Similarly, a comparison of the homework scores of
57
the students in the experimental IS classes with those of
a prerequisite statistics class found that the self-graded
scores were more dispersed, and thus less likely to be
contrived scores.
Using a linear regression model, we found a positive,
statistically significant relationship between homework
and the student’s final examination, suggesting that, if
students misrepresented their homework scores in any
substantive way, it was not statistically identifiable. If students had been generous with their home grading, we
expected to find students with high homework scores
but low exam scores. Instead, we found the opposite.
Gender did not emerge as a distinguishing factor in
our analyses. In our linear regression, this variable was
statistically significant, but of small effect. Our overall
conclusion is that, within the confines of our study, both
male and female students can and do grade their homework honestly.
References
Andrade, H., & Du, Y. (2007). Student responses to criteriareferenced self-assessment. Assessment & Evaluation in
Higher Education, 32, 159–181.
Bacon, D. R., & Bean, B. (2006). GPA in research studies: An
invaluable but neglected opportunity. Journal of Marketing
Education, 28, 35–42.
Boud, D. (1989). The role of self-assessment in student grading.
Assessment and Evaluation in Higher Education, 14, 20–30.
Boyde, E. (2013, March 11). Massive open online courses:
Time and a little money are a worthy investment. Financial
Times. Retrieved from http://www.ft.com/cms/s/2/c101d4cc800f-11e2-96ba-00144feabdc0.html#axzz2Y6TGqKVE.
Chickering, A. W., & Gamson, Z. F. (1987). Seven principles
for good practice in undergraduate education. AAHE Bulletin, 39(7), 3–7.
Dungan, A. T., & Mundhenk, L. G. (2006). Student self-assessment: a tool for engaging management students in their
learning. Teaching and Learning, 3, 54–73.
Edwards, N. M. (2007). Student self-grading in social statistics.
College Teaching, 55, 72–76.
Falchikov, N., & Boud, D. (1989). Student self-assessment in
higher education: a meta-analysis. Review of Educational
Research, 59, 395–430.
Foyle, H. C. (1988, January). Homework: Suggestions for educators. Paper presented at the meeting of the Hutchinson
Chapter of Phi Delta Kappa, McPherson, KS. Retrieved
from http://eric.ed.gov/?id=ED294504
Geide-Stevenson, D. (2009). Does collecting and grading
homework assignments impact student achievement in an
introductory economics course? Journal of Economics and
Economic Education Research, 10(3), 3–14.
Grove, W. A., & Wasserman, T. (2006). Incentives and student
learning: A natural experiment with economics problem
sets. American Economic Review, 96, 447–452.
Keith, N. K., Perrealt, H. R., Chin, M., & Keith, M. (2009). The
effect of gender on the importance of business ethics and
managerial decisions: A student perspective. Delta Pi Epsilon Journal, 51, 125–136.
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:56 11 January 2016
58
M. SIMKIN AND D. STIVER
Kirby, N., & Downs, C. (2007). Self-assessment and the disadvantaged student: Potential for encouraging self-regulated learning.
Assessment & Evaluation in Higher Education, 32, 475–494.
Koppenhaver, G. D. (2006). Absent and accounted for: Absenteeism and cooperative learning Decision Sciences Journal of
Innovative Education, 4, 29–49.
Kuo, F., Lin, C., & Hsu, M. (2007). Assessing gender differences in computer professionals’ self-regulatory efficacy concerning information privacy practices. Journal of Business
Ethics, 73, 145–160.
Leach, L. (2012). Optional self-assessment: Some tensions and
dilemmas. Assessment & Evaluation in Higher Education,
37, 137–147.
Mahlberg, J. (2015). Formative self-assessment college classes
improves self-regulation and retention in first/second year
community college students. Community College Journal of
Research and Practice, 39, 772–783.
McVarish, J., & Solloway, S. (2002). Self-evaluation: Creating a
classroom without unhealthy competitiveness. Educational
Forum, 66, 253–260.
Ohland, M. W., Loughry, M. L., Woehr, D. J., Bullard, L. G.,
Felder, R. M., Finelli, C. J., … Schmucker, D. G. (2012). The
comprehensive assessment of team member effectiveness:
Development of a behaviorally anchored rating scale for
self-and peer evaluation. Academy of Management Learning
& Education, 11, 609–630.
Ones, D. S., & Viswesvaran, C. (1998). Gender, age, and
race differences on overt integrity tests: Results across four
large-state job data sets. Journal of Applied Psychology, 83,
35–42.
Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics
for formative assessment purposes revisited: A review. Educational Research Review, 9, 129–144.
Picou, A. (2011). Does gender, GPA or age influence test
performance in the introductory finance class? A study
using linked questions. Review of Business Research, 11
(4). Retrieved from http://www.freepatentsonline.com/
article/Review-Business-Research/272616380.html
Pierce, J. R. (2014). Sex & gender in ethical decision making: A
critical review and recommendations for future research.
Academy of Management Annual Meeting Proceedings,
2014, 977–982.
Sadler, P. M., & Good, E. (2006). The impact of self-and peergrading on student learning. Educational Assessment, 11,
1–31.
Schneider, D. I. (2011). An introduction of programming using
visual basic 2010. Boston, MA: Prentice Hall.
Schneider, D. I. (2014). An introduction of programming using
visual basic 2012. Boston, MA: Prentice Hall.
Stefani, L. A. (1992). Comparison of collaborative self, peer and
tutor assessment in a biochemistry practical. Biochemical
Education, 20, 148–151.
Strong, B., Davis, M., & Hawks, V. (2004). Self-grading in
large general education classes. College Teaching, 52,
52–57.
Terezini, P. T., Cabrera, A. F., Colbeck, C. L., Parente, J. M., &
Bjorklund, S. A. (2001). Collaborative learning vs. lecture/
discussion: Students’ reported learning gains. Journal of
Engineering Education, 90, 123–130.
Thompson, G., Pilgrim, A., & Oliver, K. (2005). Self-assessment and reflective learning for first-year geography students: A simple guide or simply misguided? Journal of
Geography in Higher Education, 29, 403–420.
Vander Schee, B. A. (2011). Let them decide: student performance and self-selection of weights distribution. Journal of
Education for Business, 86, 352–356.
Wang, L., & Calvano, L. (2015). Is business ethics education
effective? An analysis of gender, personal ethical perspectives, and moral judgment. Journal of Business Ethics, 126,
591–602.