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World Journal of Educational Research
ISSN 2375-9771 (Print) ISSN 2333-5998 (Online)
Vol. 4, No. 2, 2017
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257
A Comparative Study on the Academic Performance of Students
in Bachelor’s Degree of Information Technology Having Arts
and Science Background in Uganda
Faruque A. Haolader1*, Walusimbi Hakim1, Kalinaki Kassim1 & Hamisi Ramadhan Mubarak1
1 Department of Technical and Vocational Education (TVE), Islamic University of Technology (IUT), a
subsidiary organ of the Organisation of Islamic Cooperation (OIC), Dhaka, Bangladesh
* Faruque A. Haolader, E-mail: haolader@iut-dhaka.edu
Received: February 28, 2016 Accepted: March 16, 2016 Online Published: March 10, 2017
doi:10.22158/wjer.v4n2p257 URL: http://dx.doi.org/10.22158/wjer.v4n2p257
Abstract
Variations in the academic performance among students at all levels of education are one issue for
years now that has attracted the attention of many researchers across the globe. This has prompted
researchers and educationists to find out what factors or reasons can be attributed to these variations.
Numerous studies have been conducted to determine the various reasons to explain this cause. The
purpose of this study therefore, was to compare the academic performance of students in the Bachelor’s
degree of Information Technology (BIT) having Arts and Science backgrounds in universities of
Uganda. In order to achieve the objective of this study, a sample of 202 final year BIT students were
purposively selected from two universities in Uganda. These students were categorized on the basis of
their A’ level backgrounds (130 Arts and 72 Sciences). A descriptive approach employing the Welch’s
t-test was used to determine the difference between the performance of the two groups and a simple
linear regression analysis was used to examine the correlation among students’ performance between
semesters. The results indicated that there’s a significant difference in the academic performance of the
two groups, with the science group outperforming arts. However, it was found that there is a more
linear increase in the performance of Arts students from semester one through semester five.
Furthermore, Arts students performed slightly better than Science counterparts in some course units.
Thus the study concludes that Science students perform better than Arts students in the overall semester
final examination with Arts students having room for improvement in their performance.
Keywords
academic performance, universities in Uganda, Bachelor of Information Technology, arts & science
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1. Introduction
Education is a vital investment for human and economic development for any nation. It is a crucial
sector in that, being a major investment in human capital development, it plays a critical role in
long-term productivity and growth both at micro and macro levels (Cavalevu, 1979). Education at all
levels and in all its forms constitutes a vital tool for addressing virtually all global problems. Education
takes place as a learning process conducted in institutions of learning referred to as Schools as well as
institutions of higher learning (Universities, Polytechnics and College) (Adeyemi, 2014).
Among other indicators of development of a nation, education has been found to be the basis upon
which any nation advances. Hence the ultimate venture that any country can make is to educate its
people by equipping them with the right skills, knowledge and attitude to the survival of its society. It is
relevant therefore, that institutions of learning at all levels consider the need to integrate into their
educational programmes elements that would enhance knowledge, moral conduct, rationality and
useful practical skill for living. It is important that these should have some bearing effects on the lives
of individuals who seek answers to the various problems of the society.
University education as the top level of formal education in Uganda is facing extraordinary demands to
prepare professionals for what is usually referred to as the ‘‘knowledge society’’ (Shen et al., 2007).
University education is therefore very important to national economy, both as a significant industry in
its own right and as a source of trained and educated personnel for the rest of the economy. To achieve
this, a wide range of courses are offered for different fields of life in which students make their choices
of study according to their previous academic subjects and performance along with their interests
among other factors. These fields include but not limited to, General education, Vocational, Liberal Arts,
Law, Engineering and Professional higher Education.
To be enrolled in any university course, a student has to fulfill the entry requirements or standards for
the particular university; universities have General Entrance (or “matriculation”) requirements, basic
minimum set of qualifications that all students must have for example language requirements. In
addition, each course also has its own entry requirements, both in terms of subjects a student must
already have studied and attained the examination grades required for entry. If a student has the right
subjects, the grades required will vary between universities and also between courses.
That being said, some universities of Uganda offer courses such as the Bachelor’s Degree in
Information Technology (BIT) which has flexible entry requirements for subjects of both Arts and
Sciences. As a result, two groups of students with different backgrounds (Arts and Science) are
admitted into the same course. Difference in the general performance in terms of GPA scores and in
particular course units especially those with programming and mathematics concepts have raised
concerns as to whether this difference is partially among other reasons caused by the difference in the
A’ level backgrounds of the students. The programme has been selected for investigation because of the
following reasons:
1) The BIT programme has general entry requirements for students from both Arts and Science
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background that include At least two principal passes at the same sitting in Uganda Advanced
Certificate of Education (UACE) in any two subjects (Mathematics, Physics, economics, Chemistry,
Biology, Geography, Literature, Entrepreneurship, Technical drawing and Fine Art). Subjects for both
Arts and Science follow the same grouping and weighting procedure.
2) The course design and the learning materials of both groups are the same. Moreover, the materials
are designed and delivered by same group of teaching staff.
3) The course material is quite balanced for both groups, that is, it includes practical and science based
course units which give an opportunity to those with science background to perform better. However, it
differs from a Computer Science degree, which concentrates much on the scientific aspects of
computing. In this course (BIT) students are also expected to study management and information
science, and there are reduced requirements for mathematics, which gives a fair ground to those with
Arts background to competitively compete.
4) The assessment criteria of both groups are exactly the same. All students are required to participate
in the same examination to provide a fair evaluation.
With this rationale, the program becomes an appropriate object for conducting a comparative study of
the two groups of students in the research. This study therefore intended to find the comparative
performance of students in the BIT degree programme having Arts and Science background from their
A’ level.
In order to address the above mentioned issues, the following questions were formulated:
1) Is there any difference in academic performance of students in the BIT course having Arts and
Science backgrounds?
2) Is there any correlation among students’ performance as they progress from one semester to another.
For the above questions to be answered, the study carried out with the following two hypotheses:
Hypothesis 0 (H0):
There is no statistically significant difference in students’ performance in BIT with respect to their Arts
and Science background from High School (HSC).
Hypothesis 1 (H1):
Students’ performance in one semester is positively corelated to the performance in another semester.
2. Related Literature on Factors Affecting Student Performance
Numerous studies have examined the factors that influence academic performance in primary and
secondary education as well as at tertiary level, with the purpose of enhancing learning at these stages
and reducing drop-out rates. Understanding different parameters which contribute to low or high
achievement is a frequent topic.
Personal characteristics have been recognized by a number of studies as one of the factors affecting
academic performance. These include sex, age, ability, parenthood, housing expenditures, social
background, time spent on studies, time spent on paid work and motivation (Bugge & Wıkan, 2013). In
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addition, Nyikahadzoi (n.d.) states that gender is important in explaining academice performance of
students. Likewise, Islam (2014) comfirmed gender of the students showed significant independent
effect on CGPA. However other results from different studies have showed no significant effect on
academic performance with regard to gender (Odeh, 2007; Bugge & Wıkan, 2013; Kyoshaba, 2009).
Other studies identified students’ attitude/interest in the course as one of the factors which have got a
profound effect on the academic achievement (Ali, 2013; Fenollar et al., 2007; Mekonnen, 2014). Kraft
and Singhapakdi (1991) as cited by Osaikhiuwu (2014) confirmed that students with strong work ethics
(which are influenced by attitude/interest) are strongly committed to their work, more dedicated,
focused and tend to perform better than their peers. Furthermore, Thamavithya (n.d.) identified other
personal issues influencing academic achievement: (a) Financial difficulties, (b) physical illness, health
problems, injury, (c) use of alcohol or other substance abuse, (d) pressure, stress, tension and anxiety, (e)
loneliness, lack of emotional control, (f) can’t find meaning for anything, no motivation, and (g)
conflicts with social obligations/activities.
According to Thamavithya (n.d.) lack of study skills, difficult subjects, too heavy course load, pressure,
stress, tension and anxiety have a noteworthy influence on the academic performance. A student’s
pre-admission achievement according to Islam (2014) and Martha (2009) has a significant determinant
effect on his/her educational achievement. The time spent in study, particularly time spent more than 14
hours per week showed significant positive effect on academic performance.
Among other factors identified to influence academic performance include factors related to career
issues. However, according Thamavithya (n.d.), career issues such as unsure of major, unsure of minor,
no clear career goals or plans, unsure of interests, skills and abilities, do not have a strong impact on the
academic performance.
Social related factors have also been recogonised by a number of studies to have notable effects on
academic performance (Thamavithya, (n.d.); Umar, Shaib, Aituisi, Yakubu, & Bada, 2010). University
life can be stressful, though it is without doubt one of the most memorable experiences in a student’s
life. It is characterized as a critical developmental period for both late adolescents and young adults.
Social factors such as romantic relationships, organizations and clubs (societies) (Umar et al., 2010),
social networks, sports activities among others have been found to have effects on students’ academic
performance. These factors have an effect on academic performance in terms of time demanded and the
psychological state they may cause. The problem is how one handles a balance between the stressful
academic achievement and social activities. Work commitments, family orientated learning, situation
characteristics like a supportive environment, separation from home, family and friends, housing and
roommate issues are reported to have an impact on the academic performance (Thamavithya, n.d.).
Institutions are engines for economic growth dedicated to developing skilled workforce (Osaikhiuwu,
2014). The environment present at the institution, academic or non-academic has got a bearing on the
academic performance of students. If the institution provides an accomodating and conducive
environment, it is most likely to improve the performance of it’s students otherwise the performance
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will be negatively affected.
Institutional academic factors that have been identified to affect performance inlcude; physical facilities,
institutional policy on class attendance, Library facilities, enrollment percentages, availabilty and
qualification of institutional academic staff, teaching methods and the evaluation system (Ali, 2013;
Haolader, Ali, & Foysol, 2015; Haolader & Nickolaus, 2012). The institutional variables such as
unfavourable learning conditions, interrupted water supply, poorly equipped library, overcrowded exam
time table, incessant strike and closure of school among other variables do not have any significant
impact on students’ performance (Osaikhiuwu, 2014). Romer (1993) as cited by Osiakhiuwu (2014),
recognized the importance of class attendance in enhancing students’ performance. He found that in his
economics class, students who attended class regularly made the highest grades. The geographical
location of the educational institutions also influence academic performance where by institutions
located in Urban areas record students with a higher academic performance than their rural
counterparts.
Family factors have also been reported to have a bearing on the academic performance of the students.
Some studies have established that Social Economic Status (SES) (Farooq et al., 2011) of the family
has got a significant impact on performance. Demographic variables, family member with a degree,
household size, own room, own text book and access to internet at home are indirect means of the
resources family background offer to the student (Nyikahadzoi, n.d.).
A study was conducted on the factors affecting academic performance of undergraduate students in
Uganda. The results indicated that there is a significant relationship between parents’ social economic
status and academic performance (Martha, 2009). This is the same finding by Farooq et al. ( 2011) on a
study carried out in Pakistan which revealed that Social Economic Status (SES) and parents’ education
have a significant effect on students’ overall academic achievement as well as achievement in the
subjects of Mathematics and English. He added that the high and average socio-economic level affects
the performance more than the lower level. To their research, parents’ education meant more than their
occupation in relation to their children’s academic performance at school. Parents’ level of education as
well as the level of involvment in their childrens’ (student’s) education have been reported to have
significant effects on academic performance (Islam, 2014).
Ali (2012) stated that students from monogamous (small size) families perform better than the students
from polygamous (large size) families and he pointed out the reasons that are responsible for this:
i) The students in monogamous homes have more time to read their books and study in most cases,
because there are fewer people to send on errand. But in polygamous families, the reverse is the case;
ii) The students from polygamous homes have more tendencies to be social deviants due to lack of
care and adequate supervision;
iii) Since there are more people in a polygamous family, they exert a lot of pressure or influences that
are negative, this will adversely affect the students.
However, other findings present a different view on the issue of family, reporting that it does not have a
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significant effect on student’s performance as per the findings from research conducted in Bangkok
University, Bangkok (Thamavithya, n.d.). In addition, Islam (2014) found out that the economic status
of the family measured by home ownership and household possessions showed no significant effects on
academic achievement from his research. Bugge and Wıkan (2013) also argued that parent’s education
seems to be of little importance regarding the students’ performance. Parenthood (students with
children) does not seem to influence performance significantly.
In addition to the aforementioned factors, high school background/previous academic record (Guàrdia
et al., 2006) has also been mentioned to affect academic achievement. The academic achievement in a
course is affected significantly by high school specialization, the average mark at high school (Odeh,
2007; Kyoshaba, 2009). Furthermore, Bugge and Wikan (2013) established that there is a correlation
between results from upper secondary school and performance at university level. However, according
to Mlambo (2011), there is no significant effect of the previous academic record on the education
achievement of the students.
Also, a recent study shows a significant relationship between computer ownership and the use of
technology which enhances students’ academic performance (AbduRahman et al., 2013).
All the above literature reveals that many studies have been conducted on the factors that affect
students’ academic performance in general. Factors that have been pointed out are generally concerned
with the students’ personal characteristics, family social-economic backgrounds, institutional issues,
career related, social factors as well as high school background (specialization and grade achieved). But
the purpose of this study is to investigate whether high school background specifically Arts and Science
has a got a bearing on the academic performance of students in the programmeme of Bachelor’s degree
in Information Technology (BIT) within the higher educational institutions of Uganda. Although some
research has been carried out to study the effect of high school background on academic performance
(Kyoshaba, 2009; Guàrdia et al., 2006; Bugge & Wikan, 2013; Odeh, 2007), none has been directed
towards finding out whether Arts and Science backgrounds specifically affect students’ academic
performance in a given course.
3. Methodology
In this research, an approach of comparing the academic performance between two groups of students,
studying in BIT one having Arts and the other having Science background, has been adopted in order to
determine if there’s any difference between these two groups.
The pupolation of the study comprised of students (male and female) in the BIT course from two
purposively selected Universities in Uganda, namely: Islamic University in Uganda-Kampala Campus
(IUIU-KC) and Makerere University (MUK). At the time this study was conducted, the database from
the National Council for Higher Education indicated that in Uganda there are 33 licensed public and
private universities (6 public and 27 private universities) (Ministry of Education, 2012), most of which
offer the BIT course. The criteria for the selection of the universities was; first of all it should be
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offering the BIT programmeme, then both Arts and Science students are admitted into this
programmeme. This is because some universities like Kyambogo University admit only Science
students in this programmeme (Kyambogo University, 2014).
4. Sample Size
The study sample include all final (3rd) year students in the BIT course from two selected universities.
As depicted in Table 1, the number of students from arts background is far larger than their science
background counterparts. This is attributed to the fact that most students prefer arts to science subjects
at A-level due to the difficulty found in taking science subjects. Also, due to the general requirements
of the BIT program allowing students from both backgrounds to apply and with the increasing demand
of ICT jobs, students from arts background use is as a window of opportunity to enroll for this
otherwise science-oreinted program.
Table 1. Summary Details of the Sample Data
Name of the University Total No. of Students Arts Sciences
Islamic University of Uganda 62 52 10
Makerere University 140 78 62
Total 202 130 72
Percent (%) 100 64.36 35.64
5. Data Collection and Analysis Methods
With permission from the respective university authorities, the authors were granted access to copies of
students’ results for five semesters.
A quantitative approach of comparing the academic performance of students is used to determine the
performance of students having Arts and Science backgrounds in the BIT course, the following
statistical methods were employed.
Welch’s t-test was used to examine whether there’s any significant difference in the academic
performance between Arts and Sciences students in the BIT course at 0.05 significance level. Welch’s
t-test is used here because it does not take the assumption of equal variances unlike Student’s t-test, and
it is more robust than Student’s t-test and maintains type I error rates close to normal for unequal
variances and for unequal sample sizes (Ruxton, 2006). However, Student’s t-tests were also applied
for cross checking the results. In addition, Mean score and Standard deviation were determined to
measure whether Arts students perform better, the same, or worse than Science students. A simple
linear regression analysis was used to determine the correlation between semester final exam results for
both groups. All the analysis was done using the Statistical Package for Social Sciences (SPSS v20.0).
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6. Research Results
6.1 Analysis of the Null Hypothesis (H0)
H0: There is no statistically significant difference in students’ performance in BIT with respect to their
Arts and Science background from HSC.
To prove the above hypothesis, both the Student’s and Welch’s t-test were used so that the difference in
the results of the two tests can be identified. Data was tested for normality distribution and all results
for the different semesters depicted a close to normal distribution. In addition, the mean scores of the
results of the students are used as a measure of whether Arts students performed better, the same, or
worse than their Science counterparts.
6.2 Semester-Wise Comparison between Two Groups
Table 2 shows the mean and standard deviation of GPAs obtained by students (Arts and Science
separately), and the significance level values of all five semesters. In all semesters, the results indicate
that there is a significant difference between the Arts and Science students as the sig. values are less
than 0.05 in both the students’t-test and the Welch’s t-test with only some slight differences between the
two tests. Thus, hypothesis H0, could not be proved, i.e., there are significant differences between the
Arts and Science students. The figures in Table imply that the Science students perform better than
their Arts counter parts.
Table 2. Summary Results of the Overall Semester Performance
Background Mean STD. Deviation Mean Difference Student’s T-Test (SIG.A) Welch’s T-Test (SIG.)
Sem1 Arts 3.3294 .63668 0.3413 0.000264 0.000232
Sciences 3.6707 .60444
Sem2 Arts 3.5992 .61487 0.314 0.000293 0.000145
Sciences 3.9132 .51050
Sem3 Arts 3.7258 .59215 0.4296 0.000000 0.000000
Sciences 4.1554 .38960
Sem4 Arts 3.8824 .51920 0.2658 0.000157 0.000033
Sciences 4.1482 .36276
Sem5 Arts 3.9484 .52106 0.2291 0.001389 0.000590
Sciences 4.1775 .39807
The mean values of the Arts students range from 3.33 (Sem1) to 3.95 (Sem5), and the Science students
range from 3.67 (Sem1) to 4.18 (Sem5). Their ranges are 0.62 (Arts) and 0.51 (Sciences), respectively.
These figures illustrate that the overall mean values of Science students (4.01) is higher than the Arts
students (3.7). The standard deviations values of Arts students range from .51920 (Sem4) to .63668
(Sem1), and those of the Science students range from .36276 (Sem4) to .60444 (Sem1). Their ranges
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are 0.11748 (Arts) and 0.24168 (Sciences), respectively. These figures show that the overall standard
deviation value of the Arts students (0.576792) is generally higher than the Science students (0.453074).
This suggests that the Science students generally have a stronger central tendency towards the mean
value in a particular semester.
7. Comparison of Students’ Performance in Selected Course Units
Students’ performance data (grade point) from four purposively selected course units was also analyzed
to determine the difference in the academic score of students with respect to their Arts or Science
backgrounds. The selected course units are as follows:
Table 3. Selected Subject Details
Subject Code Subject Name
BIT001 Computational Mathematics
BIT002 Structured Programming
BIT003 Database Management Systems
BIT004 Computer Networks and Data communications
Table 4. Summary Results of per Selected Course Unit Performance
Course Background N Mean STD. Deviation Mean Difference Student’s T-text (SIG.A) Welch’s T-Test (SIG.)
BIT001 Arts 130 64.57 9.861 .569 0.678 0.663
Sciences 72 64.00 8.270
BIT002
Arts 130 67.87 10.49 .564 0.691 0.666
Sciences 72 67.31 7.84
BIT003 Arts 130 66.68 10.33 2.86 0.045 0.033
Sciences 72 64.00 8.27
BIT004 Arts 130 68.47 8.97 1.52 0.045 0.033
Sciences 72 69.99 9.45
Computational Mathematics (BIT001) introduces students to ideas and techniques from mathematics
that are widely used in Information Technology. The course aims to present these ideas “in action”.
Each topic is geared towards a specific significant application in information technology. The results in
the Table above show that there’s no significant difference in the performance of students in this course
unit as the significance values 0.678 (Student’s t-test) and 0.663 (Welch’s t-test) are both greater than
0.05 (P>0.05), thus null hypothesis is accepted. The mean difference between the two groups is 0.569
with the mean value of Arts students (64.57) slightly higher compared to that of Science students
(64.00). The analysis also shows that Science students have a stronger central tendency towards the
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mean since their standard deviation value (8.270) is lower than that of the Arts students (9.861).
BIT002 provides students a strong base in the principles and practice of structured programming. A
high level programming language is used to explain the principles of programming and provide
students with hands on practical skills. The results show that Arts students perform slightly better in
this course unit with a mean value 67.87 higher than that of Science students (67.31) and a mean
difference of 0.564. Although the results show a slight difference in the mean value, however, this
difference is not statistically significant since the significance value 0.69 (Student’s t-test) and 0.66
(Welch’s t-test) are both greater than 0.05 (P>0.05). In addition, the standard deviation value of Arts
students (10.49) is also higher than that of the Science students (7.84), which implies that Science
students have stronger central tendency towards mean as compared to their Arts counterparts.
BIT003 introduces the basic theoretical and practical concepts of a database to students, its setup,
implementation, use and maintenance in a typical business organization. The results show that Arts
students perform slightly better in this course unit with a mean value 66.68 higher than that of Science
students (64.00) with a mean difference of 2.86. The results indicate that the difference is statistically
significant since the significance value is less than 0.05 (P<0.05) both in Student’s t-test (0.045) and
Welch’s t-test (0.033), thus the null hypothesis is rejected. The standard deviation value of Arts students
(10.33) is also higher than that of the Science students (8.27), which implies that Science students have
stronger central tendency towards mean as compared to their Arts counterparts.
BIT004 touches all contemporary business function in many ways. Marketing functions, accounting,
administrative, and almost all functions of business depend heavily on data communication
infrastructure for transfer of data and information between people, departments, sites, etc., with that in
mind this course is designed to provide students with technical knowledge and practical skills to handle
data communication and network infrastructure at their future workplace. The results show that Science
students perform slightly better in this course unit with a mean value 69.99 higher than that of Arts
students (68.47) with a mean difference of 1.52. The results indicate that the difference is statistically
significant since the significance values 0.045 (Student’s test) and 0.033 (Welch’s t-test) are both less
than 0.05 (P<0.05). The standard deviation value of Arts students (8.97) is also lower than that of the
Science students (9.45), which implies that Arts students have stronger central tendency towards mean
as compared to their Science colleagues.
7.1 Proof of Hypothesis 1 (H1)
(H1) There is a positive corelation in students’ performance as they progress from one semester to
another.
7.2 Correlation of Students’ Performance in Different Semesters
To determine the correlation, a simple linear regression analysis was performed, this is because linear
correlation (Spearman’s/Pearson’s) does not provide information about the variability between the
dependent and independent variables, and cannot be used to predict the value of the dependent variable
from the known value of independent variable.
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In the Table below, the ANOVA column presents values (Figure and sig.) which are used to tell whether
the independent variable has a significant impact on the dependent variable. The coefficients column
presents values (Beta (β) and sig.), sig. shows whether the independent variable can be used to predict
the value of the dependent variable, and β shows the amount of change in the dependent variable when
there’s a unit change in the independent variable.
Table 5. Correlation Coefficients for Arts and Science Students from Semester 1-5
Background Independent Variable Dependent Variable Correlation (R) Adjusted Square Anova Coefficients
F SIG. Β SIG.
Arts Sem1 Sem2 0.527 .273 49.33 .000 .509 .000
Sem2 Sem3 .514 .258 45.91 .000 .495 .000
Sem3 Sem4 .759 .589 185.7 .000 .675 .000
Sem4 Sem5 .709 .491 125.3 .000 .706 .000
Sciences Sem1 Sem2 .352 .111 9.87 .002 .297 .002
Sem2 Sem3 .663 .423 55.02 .000 .506 .000
Sem3 Sem4 .679 .453 59.9 .000 .632 .000
Sem4 Sem5 .718 .508 74.4 .000 .788 .000
7.3 Correlation between Semesters One and Two
Using a simple linear regression technique, the model formulated reported a positive and moderate
correlation (r = 0.527) with a substantial but small relationship existing between the semester one and
semester two performances for Arts students. On the other hand, low correlation (r = 0.352) which
indicated a definite but small relationship for Science students. This implies that if a student performs
well in first semester, he or she will perform well in second semester. The adjusted r square value of
0.273 (Arts) and 0.111 (Sciences) indicates that 27.3% and 11.1% respectively of the variation in the
performance in second semester can be explained by the variation in the semester one results. In the
ANOVA Table, the model produced a sig. value of 0.00 (Arts) and 0.002 (Sciences), thus the null
hypothesis is rejected as P<0.05 in both cases, which implies that student performance in semester one
plays a significant role on the performance in the second semester (Table 5).
The coefficients Table reported significance values of 0.000 (Arts) and 0.002 (Sciences) for the
independent variable (Sem1). In both cases the null hypothesis is rejected at P<0.05 which implies that
semester one performance excluding other factors has a significant role in predicting the students’
performance in semester two. The results explain that for a unit increase in a grade of semester one
results, the GPA of semester two will increase by 0.506 (Arts) and 0.297 (Sciences) holding other
factors constant. In the case of residues, the histogram showed an ideal normal distribution for both
Arts and Sciences.
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7.4 Correlation between Semesters Two and Three
The findings show that there’s a positive and moderate correlation of r = 0.514 and r = 0.663, indicating
a substantial but small relationship existing between the semester two and semester three performances
for Arts and Science students respectively. The results imply that if a student performs well in the
second semester, he or she is likely to perform well in the third semester. The adjusted r square values
0.258 (Arts) and 0.423 (Sciences) indicates that 25.8% and 42.3% respectively can explain the
variation in the students’ performance in third semester.
In the ANOVA Table (Table 5), the model produced a sig. value of 0.000 for both Arts and Science
students, thus in both cases, the null hypothesis is rejected (P<0.05). This implies that students’
performance in semester two has a significant role on the performance in the third semester. The
coefficients Table also reported significance value of 0.000 for the independent variable (Sem2). Thus,
the null hypothesis is rejected at P<0.05 which implies that performance in semester two has got a
significant role in predicting the students’ performance in semester three. In addition, for a unit increase
in semester two results, the GPA in semester three will increase by 0.495 (Arts) and 0.506 (Sciences).
7.5 Correlation between Semesters Three and Four
In this model, the results show a positive and high correlation r = 0.759 together with a moderate
correlation r = 0.679 for Arts and Sciences respectively between the semester three and semester four
performances. The relationship is substantial but small (Arts) and high relationship (Sciences) thus
implying that if a student performs well in the third semester, he or she is likely to perform relatively
well in the fourth semester. The adjusted R square value is 0.589 (Arts) and 0.453 (Sciences), this
indicates that respectively, 58.9% and 45.3% of the variation in the performance in fourth semester can
be explained by the variation in the semester three results.
In the ANOVA Table, the model produced a sig. value of 0.000 for both Arts and Sciences, thus the null
hypothesis is rejected (P>0.05). This implies that students’ performance in semester three has a
significant role on the performance in the fourth semester. The coefficients Table also reported a
significance value of 0.000 for both Arts and Sciences for the independent variable (Sem3). Thus the
null hypothesis is rejected at P<0.05 which implies that the students’ performance in semester three has
a significant role in predicting students’ performance in semester four.
7.6 Correlation between Semesters Four and Five
The model reported a positive and high correlation r = 0.710 (Arts) and r = 0.718 (Sciences) with a
high relationship which existing between semester four and semester five performances. This signifies
that if the student performs well in the fourth semester, he or she will perform well in the fifth semester.
The adjusted r square value of 0.491 (Arts) and 0.508 (Sciences), indicates that 49.1% and 50.8%
respectively of the variation in the performance in fifth semester can be explained by the variation in
the semester four results.
In the ANOVA Table, the model produced a sig. value of 0.000 for both Arts and Sciences, thus the null
hypothesis is rejected (P<0.05), which implies that students’ performance in semester four plays a
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significant role on the performance in the fifth semester. The coefficients Table (Table 5) reported a
significance value 0.000 for both Arts and Sciences for the predictor variable (Sem4). In both cases the
null hypothesis is rejected at P<0.05 which implies that semester four performance excluding other
factors has a significant role in predicting the students’ performance in semester five. This means that
for a unit increase in a grade of semester one results, the GPA for semester two will increase by 0.706
(Arts) and 0.788 (Sciences) holding other factors constant.
7.7 Discussion on Major Findings
The results from the analysis show that Science students perform better than the Arts students in the
overall Semester final exams for all semesters (1–5) with the biggest difference observed in Semester
three (0.423), while in the fifth semester the difference is minimal (0.229). This difference in
performance could be as a result of Science students paying more attention and being more committed
by giving additional time to their studies compared to Arts students. Furthermore, Science students
usually bring more math and science related basic knowledge and skills compared to their counterparts
that may contribute to their performance at higher level study in BIT course. The findings of this study
support this statement too.
The research results also show that Science students have a stronger central tendency towards the mean
value in their examination results. In brief, the variance of the Science students is smaller and they have
a tighter dispersion in examination results in comparison with the Arts students. On the other hand, the
performance of Arts students tend to fall into two extremes.
The results have also indicated that, though there is a significant difference in the overall performance
in the semester final exams with Science students outperforming Arts students, results show that Arts
students perform slightly better than the Science students in BIT001, BIT002 and BIT003. However,
this difference is not statistically significant. Science students performed quite better in BIT004 than
the Arts students. Out of the four sampled course units, Arts students performed better than Science
students in three and Science student outperformed their Arts colleagues in only one. This implies that
perhaps other course units not included in the study sample might have contributed more to the general
semester results of Science students that they outperformed the Arts students.
It is also noticed from the analysis that there is a progressive increase in the performance of both
groups from semester one through semester five with the Arts exhibiting a more linear progress than
the Science students. This implies that depending on the results from the previous semester, students try
to improve on their performance in the next semester. The chart in Figure 1 displays this progressive
increase in the performance.
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Figure 1. Students Performance in Different Semesters
The correlation of students’ performance was positive throughout the five semesters with a moderate
and substantial relationship according to the results from Table 5. The Arts students had a stronger
overall correlation (0.627) within the semesters compared to Science students (0.603). The results also
reveal that variations in the performance in a given semester can be explained by the variations in the
results of the semester before it, this is given by the Adjusted R square value in Table 5. Correlation
results in a given semester also reveal a significant role on the performance in the next semester, which
implies that if a student performs well in a semester, he/she is likely to perform well in the next
semester. This is given by the sig. value in the ANOVA column of Table 5. Student performance for the
next semester can also be predicted basing on the known performance of the previous semester which
is provided by the beta (β) value under the coefficients column of Table 5.
8. Conclusion
Variations in the academic performance among students at all levels of education are one issue that has
attracted the attention of many researchers across the globe. Different studies have reported various
reasons to explain this cause. Similarly in the same line, this study was conducted to compare the
academic performance of students in the BIT course having Arts and Science backgrounds to find out
whether there’s a significant difference between them such that it can be determined whether this
difference can be attributed to their A’ level backgrounds. In the study it was found that there is a
significant different between Arts and Science students with Science students outperforming their Arts
colleagues in the overall semester final examinations. Nevertheless, Arts students slightly perform
better than the Science students in some course units of this programme though the difference is not
statistically significant. Therefore, according to results from the analysis, the researcher concludes that
Science students perform better than Arts students in this course and that the difference is to a certain
extent attributed to students’ A’ level background (Arts/Sciences).
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This study is important as it provides institutional administrators, teachers and curriculum developers
of the BIT program with empirical evidence that requires putting into considerations the variations in
the performance from both backgrounds so as to redesign a curriculum suitable for both groups.
In addition, the results also indicated a positive increase in the performance from semester one through
semester five with Arts students exhibiting a more steady and linear progress than Science students.
This is evident in the low mean difference between these two groups in semester five compared to
semester one. This suggests that Arts students have got the capacity to compete with the Science
students.
Regardless of the above variations in the performance from both backgrounds, students end up with the
same qualifications hence being absorbed in similar positions in different firms. However, students
with outstanding results in all semesters might be preferred by firms that consider specific
performances in selected course units depending on the tasks they will be assigned.
Recommendations
The study was limited to only one batch (final year students) in the BIT course from the two
universities. However, a deep analysis is required for all batches including students from more other
universities in order to generalize the results. In addition, a study on Students’ (Arts and Science) views,
perceptions and experience in the course is also necessary to find out whether the course content fulfills
what they anticipate from this course as this is necessary to supplement the findings of this current
study.
This study is primarily designed to generally compare the academic performance of Arts and Science
students regardless of any issues. However, research is need to examine the performance of male and
female students in this course, and also to compare the performance of students who have morning
sessions with those in the evening session of this course.
Basing on the results in this study, the researcher would recommend administrators to increase the
admission of Science students in BIT since the results have indicated that they perform better than their
Arts counterparts in the BIT programme, especially for IUIU where the number of Science students is
extremely low (10) compared to that of Arts students (63).
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