Impact of students’ behavior on continuous assessment in Higher Education

Abstract

The aim of this study is to analyze students’ academic results following the introduction of a continuous assessment system in Higher Education. This study examines a large sample that consists of third-year students’ grades across nine subjects in the Bachelor of Business Administration program at the University of Castilla-La Mancha (Spain). Specifically, this paper studies the relations among three types of grades (i.e., final exam grades, continuous assessment activities grades (excluding the final exam) and global assessment grades, which are calculated as the weighted averages of the final exam grades and the continuous assessment activities grades) and the influence of the number of students who actively participate in a subject and the date of the final exam on students’ grades. Generally, this study reveals a positive effect of continuous assessment activities on students’ academic success. Furthermore, there is a statistically significant positive relation between the number of students who actively follow a subject and their global assessment grades. Finally, the earlier the students completed the final exam, the higher their grades were on this exam.

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Impact of students’ behavior on continuous
assessment in Higher Education
María de la O González, Francisco Jareño & Raquel López
To cite this article: María de la O González, Francisco Jareño & Raquel López (2015) Impact of
students’ behavior on continuous assessment in Higher Education, Innovation: The European
Journal of Social Science Research, 28:4, 498-507, DOI: 10.1080/13511610.2015.1060882
To link to this article: http://dx.doi.org/10.1080/13511610.2015.1060882
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Impact of students’behavior on continuous assessment in Higher
Education
María de la O González
a
, Francisco Jareño
a,b
*and Raquel López
a
a
Department of Economic Análisis and Finances, University of Castilla-La Mancha, Albacete,
Spain;
b
Department of Economic Analysis and Finance, Faculty of Economic and Business Sciences,
Plaza de la Universidad, 1, 02071, Albacete, Spain
(Received 20 October 2014; final version received 8 June 2015)
The aim of this study is to analyze students’academic results following the introduction
of a continuous assessment system in Higher Education. This study examines a large
sample that consists of third-year students’grades across nine subjects in the
Bachelor of Business Administration program at the University of Castilla-La
Mancha (Spain). Specifically, this paper studies the relations among three types of
grades (i.e. final exam grades (FEGs), continuous assessment activities grades
(excluding the final exam) and GAGs, which are calculated as the weighted averages
of the FEGs and the continuous assessment activities grades) and the influence of the
number of students who actively participate in a subject and the date of the final
exam on students’grades. Generally, this study reveals a positive effect of
continuous assessment activities on students’academic success. Furthermore, there is
a statistically significant positive relation between the number of students who
actively follow a subject and their GAGs. Finally, the earlier the students completed
the final exam, the higher their grades were on this exam.
Keywords: Continuous assessment; grades; Higher Education; students’academic
results; academic results and continuous assessment in Higher Education
Introduction
The European Higher Education Area (EHEA) focuses on the continuous evaluation of
students’knowledge and competences (i.e. abilities, skills and attitudes) according to
the administration of various evaluation tests (Ariza et al. 2013; Bengoetxea and Buela-
Casal 2013).
1
This system of evaluation differs from a traditional system, which is
based exclusively on a final exam. Previous studies have analyzed the academic results
of students who were engaged in the same subject but who experienced two different
systems of evaluation (i.e. continuous vs. traditional evaluation) during one academic
year (see Gracia and Pinar 2009; Carrillo-de-la-Peña and Pérez 2012). Former research
has also examined the results of students who were engaged in a specific subject across
two academic years, which reflects both before and after the establishment of a continuous
evaluation system (see Jareño 2007; López et al. 2007; Mingorance 2008).
The present study differs from the previous literature in two ways. First, the current
research analyzes the results for third-year students in the Bachelor of Business Adminis-
tration (BBA) program at the University of Castilla-La Mancha (UCLM) in Spain across
© 2015 ICCR Foundation
*Corresponding author. Email: francisco.jareno@uclm.es
Innovation: The European Journal of Social Science Research, 2015
Vol. 28, No. 4, 498–507, http://dx.doi.org/10.1080/13511610.2015.1060882
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nine subjects according to a continuous assessment system (see Vidal 2003; Gijón-Puerta
and Crisol-Moya 2012, for detailed information regarding the transition process when
establishing the EHEA at Spanish universities). Second, this study examines three types
of grades, which are final exam grades (FEGs), continuous assessment grades (CAGs)
and global assessment grades (GAGs).
2
The GAGs are the result of the weighted averages
of the FEGs and the continuous assessment activities grades.
This research analyzes several aspects of the students’academic results. First, it exam-
ines the correlation between the continuous assessment activities grades, as ranked by per-
centiles, and the FEGs. Previous studies have documented the positive effect of continuous
assessment on students’academic success (Carrillo-de-la-Peña et al. 2009; Peterson and
Siadat 2009). However, these studies examined the development of the continuous assess-
ment activities but not the grades that students obtained from these assessments. The
current research analyzes the relation between the CAGs and the FEGs.
Second, it calculates the percentage of students who passed the subject according to the
percentiles of the CAGs. Some previous studies document that the percentage of students
passing a subject is higher under the continuous assessment system than under the traditional
system (Mingorance 2008; Gracia and Pinar 2009; Carrillo-de-la-Peña and Pérez 2012).
Third, it assesses the relations among the number of students who actively participated
in the subject and the three types of grades considered in this research. The relation
between the number of students who actively follow a subject and the grade obtained in
that subject is unknown. On the one hand, a teacher’s dedication to each student may be
lower when teaching subjects with a large number of students compared to a small
number of students (Kokkelenberga, Dillona, and Christy 2008). On the other hand, the
difficulty level associated with passing a subject may be lower for the subjects with a
large number of students compared to a small number of students. The call effect may
lead to a greater number of students enrolling in a subject.
Finally, this research analyzes the relation between the final exam date and the grades
obtained.
3
A large number of teachers believe that there is a negative relation between the
date on which the final exam for a subject is scheduled within the examination period and
the grades obtained on the exam (Herrera-Torres and Lorenzo-Quiles 2009).
Method
Participants
The study sample consists of third-year students in the BBA program in the Faculty of Econ-
omic and Business Sciences at the UCLM in Albacete (Spain) during the 2011–2012 aca-
demic year.
4
Both morning and evening teaching groups are included in this sample.
Instruments
The analyzed variables include the FEGs, the CAGs and the GAGs for the nine academic
subjects. The GAG variable is calculated as follows:
GAG =
n
i=1
wCAGiCAGi+wFEGFEG,(1)
where n
i=1wCAGi+wFEG =1; wCAGiis the weight of the i-th CAG, CAG
i
, in GAG; wFEG
is the weight of the FEG in the GAG; and nis the number of continuous assessment
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activities in addition to the final exam. For every subject, only grades greater than zero for
the final exam and for at least one of the continuous assessment activities during the ordin-
ary session are included.
5
The subject name, the weight of the various continuous assess-
ment activities on the GAGs and the number of observations for each subject are presented
in Table A1 of the Appendix.
Procedure
This research analyzes the relations among the variables using Pearson’s correlation coef-
ficients and Student’st-tests to determine statistical significance. In addition, this study
divides the CAG variable into the following three percentiles:
Lower percentile (CAG below the 20th percentile): consists of 20% of students with
lower grades on the continuous assessment.
Intermediate percentile (CAG above the 20th percentile and below the 80th percentile):
consists of students with grades on the continuous assessment that are greater (lower) than
20% of students with lower (greater) CAGs.
Higher percentile (CAG above the 80th percentile): consists of 20% of students with
higher grades on the continuous assessment.
This study uses three percentiles to investigate whether the relation between CAG and
FEG holds not only for the whole sample, but also distinguishing between students with
the highest CAG and students with the lowest CAG. Therefore, it provides evidence
about the relation in the tail ends of the distribution.
Results and discussion
Correlation between the CAGs and FEGs
This section analyzes the relation between the CAGs and the FEGs. On one hand, this
study collects the correlation coefficients for the whole sample and by percentile. On the
other hand, this research investigates whether FEG at the higher percentile differs from
FEG at the lower percentile.
Table 1 presents the Pearson’s correlation coefficients for the nine subjects. For the
whole sample, it shows that there is a positive statistically significant correlation
between the CAG and FEG in seven out of nine subjects. This evidences the relevance
of the continuous assessment process to succeed in the final exam. However, to analyze
in more depth this issue, this study distinguishes between three students’profiles: students
with excellent, standard and low grades in CAG.
By percentile, the lower percentile shows no statistically significant correlations
between the CAG and FEG for any subject. For the higher percentile, there is a statistically
significant negative correlation of 54% between the two types of grades for one subject.
Finally, for the intermediate percentile, there is a statistically significant positive correlation
between the CAG and FEG for three of the nine subjects, which have correlation coefficients
between 23% and 49%. Therefore, for the intermediate percentile, the results for one-third of
the subjects support the proposal that students’success is related to both the development of
continuous assessment activities and students’success when completing these activities.
Previous studies have documented this positive effect of continuous assessment on students’
academic success (Carrillo-de-la-Peña et al. 2009; Peterson and Siadat 2009). However,
these studies examined the development of the continuous assessment activities, whereas
this research takes into account the students’grades obtained from these assessments.
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Table 1. Correlations between the CAGs and FEGs for the whole sample and by percentile for each subject (AA, CA, BCM, FM, OM, SE, BT, SIIE and MR).
Subjects
AA CA BCM FM OM SE BT SIIE MR
Whole sample
r0.34 0.39 0.06 0.38 0.17 0.45 0.26 0.22 0.23
t-test (H
0
:r= 0) 2.96*** 3.77*** 0.66 3.60*** 1.65 4.31*** 2.13** 1.71* 2.28**
Lower percentile: CAG below the 20th percentile
r0.04 0.13 0.00 0.17 −0.26 −0.19 0.39 −0.26 0.09
t-test (H
0
:r= 0) 0.15 0.55 −0.03 0.65 −1.14 −0.72 1.43 −0.80 0.35
Intermediate percentile: CAG above the 20th percentile and below the 80th percentile
r0.24 0.49 −0.04 −0.04 0.23 −0.04 0.31 0.10 0.15
t-test (H
0
:r= 0) 1.60 3.74*** −0.34 −0.30 1.72* −0.28 1.98* 0.62 1.11
Higher percentile: CAG above the 80th percentile
r0.18 −0.54 0.23 0.32 −0.09 0.32 −0.07 NA
a
−0.13
t-test (H
0
:r= 0) 0.65 −2.46* 1.01 1.21 −0.38 1.24 −0.25 NA −0.56
Note: rshows the Pearson’s correlation coefficient. The null hypothesis for the t-test is that the correlation coefficient is equal to zero.
*p< .10.
**p< .05.
***p< .01.
a
The correlation coefficient cannot be calculated because the CAG higher than the 80th percentile has the same value for all of the students.
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According to the Pearson’s correlation coefficients, there is no evidence about a posi-
tive statistically significant relation between the CAG and FEG in the tail ends of the dis-
tribution. Therefore, this study further investigates whether there are statistically significant
differences in means between FEG at the higher percentile (i.e. FEG for students with the
highest CAG) and FEG at the lower percentile (i.e. FEG for students with the lowest
CAG). Table 2 shows that the mean of FEG at the higher percentile is significantly
higher than the mean of FEG at the lower percentile for eight out of nine subjects. This
result evidences that students with the highest CAG obtain significantly higher grades in
the final exam than students with the lowest CAG. This finding supports the hypothesis
that a positive relation between CAG and FEG is present in the tail ends of the distribution.
Additional analysis
Relation between the CAGs and the percentage of students who passed a given subject
The present study analyzes the percentage of students who passed a given subject (GAG >
4.9) for the three CAG percentiles.
6
According to the results presented in Table 3, as the
percentile of CAG increases, the percentage of students who passed a given subject also
increases. Thus, as success in the development of continuous assessment activities
increases, success in passing the subject also increases. Moreover, the increase in the per-
centage of students who passed a given subject is greater during the transition from the
lower to the intermediate percentile than during the transition from the intermediate to
the higher percentile.
This finding extends previous evidence (Mingorance 2008; Gracia and Pinar 2009;
Carrillo-de-la-Peña and Pérez 2012), because it shows that passing a subject is positively
related not only to following a continuous assessment system, but also to the success
achieved in this system.
Correlation between the number of students and grades (CAG, FEG and GAG)
Table 4 presents the Pearson’s correlation coefficients for the number of students in each
subject and the average grades that they obtained (CAG, FEG and GAG). There is evi-
dence of a positive relation between the number of students and the three types of
grades, although this relation is only statistically significant for the GAG variable. Thus,
this finding only supports the call effect hypothesis for GAGs.
7
This result is in line
with Hattie (2005), who defends that reducing the number of students of a subject has
an inconclusive effect on students’grades. Normally, smaller classes would allow teachers
to have more individualized and frequent feedback with their students. However, it does
not guarantee higher students’grades.
Relation between the final exam dates and FEGs
This section presents the relation between the final exam dates for each subject and the
average grades obtained in the exams in Figure 1, distinguishing between the first-term
(panel A) and second-term (panel B) subjects. These results validate the negative relation
between these two variables mainly for the second term, which supports the hypothesis that
the earlier the students complete the final exam, the higher their grades are in this exam due
to increases in their ability to concentrate and their dedication to the subject.
8
Therefore,
this finding indicates that students’exam results may decline as the distance to the final
502 M. de la O González et al.
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Table 2. Mean of FEG at the higher and lower percentiles and t-test for the equality of means for each subject (AA, CA, BCM, FM, OM, SE, BT, SIIE and MR).
Subjects
AA CA BCM FM OM SE BT SIIE MR
(1) Mean FEG higher percentile 6.54 6.13 7.35 6.51 7.1 5.43 6.46 6.00 4.65
(2) Mean FEG lower percentile 5.15 4.25 7.04 5.43 4.51 2.16 5.53 3.67 3.72
t-test (H
0
: 1 = 2) 2.70*** 3.41*** 0.49 2.05** 3.34** 5.13*** 1.74* 1.89** 1.74**
The null hypothesis of the t-test is that the mean of FEG at the higher percentile equals the mean of FEG at the lower percentile.
*p< .10 (one-tailed tests).
**p< .05 (one-tailed tests).
***p< .01 (one-tailed tests).
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exam date increases (and vice versa). This is in line with the evidence in Herrera-Torres and
Lorenzo-Quiles (2009), who find that third-year university students organize their study
time based on the proximity of the final exam date.
Conclusions
The present study examines three types of grades (i.e. FEGs, continuous assessment activi-
ties grades and GAGs) and analyzes the relations among them, including the effects that
specific aspects (e.g. the number of students who actively participate in a subject and
the date of the final exam) have on the students’grades. This study assesses students’
Table 3. Percentage of students passing a subject by percentile of CAGs for each subject (AA, CA,
BCM, FM, OM, SE, BT, SIIE and MR).
Subjects
AA CA BCM FM OM SE BT SIIE MR
Lower percentile: CAG below the 20th percentile
92% 75% 100% 26% 63% 0% 46% 36% 58%
Intermediate percentile: CAG above the 20th percentile and below the 80th percentile
90% 93% 100% 77% 85% 37% 97% 37% 100%
Higher percentile: CAG above the 80th percentile
100% 100% 95% 100% 94% 73% 100% 72% 100%
Table 4. Correlations between the number of students and average grades (CAG, FEG and GAG).
CAG FEG GAG
r0.27 0.51 0.65
t-test (H
0
:r= 0) 0.75 1.59 2.23*
Note: rshows the Pearson’s correlation coefficient. The null hypothesis for the t-test is that the correlation
coefficient is equal to zero.
*p< .10.
**p< .05.
***p< .01.
Figure 1. Relations between the final exam dates and the FEGs for each subject (AA, CA, BCM,
FM, OM, SE, BT, SIIE and MR). Note: the number of students whose grades are included in this
study for each subject is as follows: 69 (AA), 78 (CA), 102 (BCM), 75 (FM), 93 (OM), 75 (SE),
63 (BT), 57 (SIIE) and 88 (MR).
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grades across nine subjects from their third year in the BBA program at the UCLM (Spain)
during the 2011–2012 academic year.
The results reveal the following: (a) a significant positive correlation between the con-
tinuous assessment activities grades and the FEGs; (b) a positive relation, in general terms,
between the continuous assessment activities grades and the percentage of students passing
a given subject; (c) a significant positive relation between the number of students in a given
subject and the GAGs; and (d) a negative relation between the final exam dates and the
FEGs, mainly for second-term subjects.
These results have relevant implications for the assessment system in Higher Edu-
cation. Thus, the continuous assessment process contributes to the success of students in
the final exam, and hence this process should be supported in Higher Education. Further-
more, the number of students enrolled in a subject may not be a crucial factor in order to
pass it. Finally, academic institutions should take into account the timetable of the final
exams because this schedule might impact on the FEGs of students.
Notes
1. The Bologna Declaration of 19 June 1999 posited the foundation of the EHEA to be established
before 2010 (see http://www.ehea.info/).
2. According to regulations regarding student assessments at the UCLM, the assessments evaluat-
ing the students’academic progress include, among others, useful participation in class, external
training, theoretical essays, self-assessment activities and progress and final exams (see http://
www.uclm.es/cr/EUP-ALMADEN/pdf/normativa/ReglamentoEvaluacionGradoMasterUCLM.
pdf).
3. Other evaluation issues are analyzed in Jareño and López (2015).
4. The transition of the BBA to the EHEA occurred during the 2009–2010 academic year, so the
students of the 2011–2012 academic year are the first ones in reaching the third course.
5. During the ordinary session, the continuous assessment activities and the final exam occur
within a short time period; therefore, the correlation between these variables seems stronger.
6. GAG > 4.9 refers to a subject who was passed.
7. Further support of this hypothesis would imply to have data from previous academic years for
comparison purposes. Unfortunately, these data are not available.
8. The final exam grade depends on several factors. This section of the paper only analyses the
relations between the final exam dates and the grades obtained in the final exam, considering
the other variables as ceteris paribus.
ORCID
Francisco Jareño http://orcid.org/0000-0001-9778-7345
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Appendix
Table A1. Subject names, weights of the continuous assessment activities on the GAGs and the
number of observations.
Weight of continuous assessment activities on GAG
Obs.
Individual
activities
a
Activities
in group
b
Conference
attendance
Participation
in class
Progress
exam
Final
exam
Accounting
Analysis (AA)
20% 10% 70% 69
Cost Accounting
(CA)
10% 15% 5% 70% 78
Business
Commercial
Management
(BCM)
15% 25% 60% 102
Financial
Management
(FM)
20% 10% 70% 75
Operations
Management
(OM)
10% 20% 70% 93
Spanish
Economics (SE)
25% 15% 60% 75
Business Taxation
(BT)
75% 25% 63
Statistical
Inference and
Introductory
Econometrics
(SIIE)
10% 20% 70% 57
Market Research
(MR)
20% 30% 50% 88
a
Individual activities include the resolution of problems or cases, elaboration on training essays and self-
evaluation and peer-evaluation activities.
b
Activities in groups consist of writing essays in a group.
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