An Empirical Investigation Of Student S Poor Performance In Quantitative Subjects A Case Study Of Management Students From Pakistan

Sci.Int.(Lahore), 24(4),487‐494, 2012 ISSN 1013‐5316,CODEN: SINTE 8 487
AN EMPIRICAL INVESTIGATION OF STUDENT’S POOR
PERFORMANCE IN QUANTITATIVE SUBJECTS:
A CASE STUDY OF MANAGEMENT STUDENTS FROM PAKISTAN
1
S. M. Irfan, 1
M. Awan, 1
Saman Shahbaz,2
Hakeem–Ur–Rehman
irfansyed36@yahoo.com
1
COMSATS Institute of Information Technology, Lahore, Pakistan
2
Institute of Quality & Technology Management, University of the Punjab, Pakistan
ABSTRACT: In today’s competitive working environment a successful manger needs to be well equipped with latest
quantitative techniques for effective decision making and has the skills to optimize the available resources for
organizational success. This paper aims to investigate the factors that affect the poor performance of management
students in the quantitative subjects; especially in business mathematics & statistics, quantitative techniques and
research methods are taught at bachelor of business administration (BBA) and master of business administration
(MBA) level. The major reason for conducting this study was the high failure rates and poor grades in these
subjects as compared to other subjects. This study has been conducted in a public and private sector universities of
Pakistan at local level. Data has been collected after designing a questionnaire after detailed interview with the
Instructors involved in teaching these subjects and the students scoring low grades in quantitative subjects. Using
the inputs from interviews, 26 major variables were identified that were divided in to reflect student’s aptitude,
attitude and behavior, teacher’s competency, behavior and administrative issues were included in the questionnaire
and the analysis shows positive impact on poor learning and performance.
Key words: Learning, quantitative subjects, management students
INTRODUCTION:
Rapidly changing technologies, complex business
operations, huge investments on research and development
for producing innovative and quality products, and the
competitive pressures, organizations needs to make effective
decision making to compete in the local as well as global
market. A successful and aspiring manger needs to be well
equipped with leadership skills and the latest quantitative
techniques for effective decision making and has the ability
to optimize the available resources strategically for
organizational success. The quantitative techniques involve
statistical analysis, developing mathematical models that
help students for effective utilization of raw material,
transportation costs, effective and efficient utilization of
human resources, and providing quality products to their
customers. The quantitative techniques are essentially
valuable in management decision making like planning,
forecasting, control and evaluations. The strong
understanding of quantitative subjects is, therefore, of
fundamental importance to any of the management graduate.
In this study factor affecting student poor learning and
performance in the quantitative subjects including, business
mathematics & statistics, quantitative techniques and
research methods offered at bachelor of business
administration (BBA) and master of Business administration
(MBA) has been analyzed. A large proportion of curriculum
of these subjects is based on statistics and the remaining part
is based upon mathematical modeling and linear
programming. Therefore, a good knowledge of statistics is a
key to success in these subjects. Students must have to pass
these quantitative subjects to fulfill the requirements of their
degree program.
Due to the growing importance of quantitative subjects,
statistics and research methods are included in almost all the
curricula of management and these subjects are useful in
their jobs as well [1.2] also accentuated the importance of
statistics and pointed out in his studies that in most of the
universities; where social sciences degree programs are
offered; include at least one subject with statistical content.
He further stated that this subject is helpful to the students
for empirical findings and conclusions for their research
articles/thesis, but most of the students in this discipline
have strong aversion to these subjects. The students having
poor aptitude and difficulties faced in learning mathematics
is also causes poor learning and performance in the
quantitative subjects, so mathematics is one factor that
contributes in facing difficulties in quantitative subjects [3].
Earlier studies proved that the quantitative subjects are
problematic and difficult to the students of social sciences,
in general as compare to other subjects in their disciplines
[4-9]. In another study by [10] also identified that students of
social sciences are facing problems in the quantitative
subjects. Similarly [1] explored that in USA, the students
who have undergone 12 years of their schooling without
taking statistics as a subject find it difficult to understand at
university level and causes of anxiety towards these subjects.
Anxiety level towards quantitative subjects is high among
those students came from non-mathematics oriented
disciplines [1, 12] and high level of anxiety among statistics
and quantitative based subjects weaken student’s
performance in these subjects [13]. In another study
conducted by [14] estimated that approximately 80% of the
graduate students in social sciences experiences statistical
anxiety while studying statistical courses. The students
having difficulties in learning quantitative subjects may
also have to face problems in completing their degrees[15,
16] and sometimes even reflected in their views in
professional career [13].
In learning process the student and teachers are in the main
role. Student’s objective is to gain knowledge and skill
where teacher’s responsibility is effective knowledge
transfer to the students. In learning of a particular subject,
student attitude and behavior matters a lot, e.g. some
students who are not ‘good with numbers’ assumed that
these courses will be difficult to understand for them [17,
18]. Therefore, student aptitude, attitude and behavior are

ISSN 1013‐5316,CODEN: SINTE 8 Sci.Int.(Lahore), 24(4),487‐494, 2012
488
one dimension that effect their performance and poor
learning in a particular subject. Student’s motivation and
passion to learn a particular subject will be a one way that
contributes in effective learning.
In effective learning process the other role is performed by
teacher and he/she is one who makes and creates interest,
enhance student motivation in the subject through his/her
competency, skill and knowledge. It is commonly, observed
that during the university/college studies some courses are
labeled with teacher name due their capability, methodology
and the knowledge delivery to students.[19] stated that a
teacher is a person, who interact and engage with the student
to create change among them and this change may be in the
form of knowledge, skills and effectiveness [20]. The
effective learning of any subject may also depend upon how
the knowledge is effectively delivered to the students.
According to [21], it is the teacher who creates a learning
environment in the class, so that the learning process will be
effective and continuously enriched. Teacher and the
knowledge delivered to students assume to be the important
factor that contributes in quality of an educational program
and student’s competency [22]. It is also admired by [23]
that teacher is the main source during a learning process that
can contribute in enhancing student’s knowledge, skill and
capability. Another important factor that contributes in
learning process is the teacher behavior, which varies from
one situation to another and from one course to another.
According to Wiki Educator, the most concerning factor in
the learning process is teacher behavior which is divided in
to four categories: personality, methodology, expectation
and competence.
The personality of a teacher is the most significant factor
during the learning process which influences student during
the learning environment [19] and its personality is said to
be desirable teaching personality if he/she is successful to
create and maintain a learning environment in the class room
where students are motivated and feel comfortable to learn
[24]. In another study by [25] concluded that from theory of
interpersonal perception it can be suggested that student’s
attitude towards the teacher will also contribute in his/her
interest in the course taught and also towards Institute.
Teaching methodology, expectation and competencies are
the important ingredients during the learning process.
According to [26] cited in [27] that professional competence
and professional characteristics are the two distinct qualities
that differentiate among successful professional teacher. He
further explained these qualities as: Professional
characteristics include professional values, personal and
professional development, communication and relationships
as well as synthesis and application; whereas professional
competences include knowledge and understanding of
children and their learning, subject knowledge, curriculum,
the education system and the teacher’s role. Whereas, [28]
believes in three dimensions of a teacher quality:
effectiveness, competence and performance. Effectiveness
means to what extent a teacher accomplish desired effects
upon student, teacher competence is stated in terms of his
knowledge, expertise and skills and teacher performance is
actually how he/she behaves during lecture delivery.
In Pakistan educational Institutes are making every possible
effort to increase the quality of education and to develop the
effective human capital that meets the industry requirements.
For this purpose, a huge investment is made by Higher
Education Commission of Pakistan (HEC) in the training
and development of its faculty. Teachers are also putting
their maximum efforts to enrich the curricula with practical
examples, case studies and current research articles and
making contribution in research and development. Effective
methodologies to create interest in the lecture and computer
based exercises are the common practices in most of the
cases to maximize the student learning. [29] pointed out that
in traditional teaching and learning environment students
were usually taught material given in the specific course text
book means it was a way of spoon feeding to students. Now
the traditional role of a teacher has transformed to a
‘facilitator’, who is responsible to deliver knowledge in the
class room but is also responsible for effective learning to
students. Traditional teaching and learning process was
explained by [30] as …
“… content-driven, emphasizing abstract concepts over
concrete examples and application rarely challenge students
to perform at higher cognitive levels of understanding. This
didactic instruction reinforces in students a naïve view of
learning in which the teacher is responsible for delivering
content and the students are the passive receivers of
knowledge.”
In our education system, the students enrolled in bachelor of
business administration (BBA) a four years degree after 12
years of education and master in business administration
(MBA) a two year degree after 14 years of education. These
students are from different educational background like
engineering, social sciences, pre-medical, commerce and
arts. The students from other than engineering background
do not study any mathematical or statistical subjects during
their previous degrees. Due to the reason most of the
students have poor aptitude in these subjects. Higher
education commission of Pakistan (HEC) is continuously
engaged to maximize the quality of its Institutes of higher
learning and their graduates so that these graduates can be
easily placed and compete in the market.
The objective of this study is to identify the problem faced
by management students while studying quantitative
subjects especially, business mathematics, statistics,
quantitative techniques and research methods during their
undergraduate (BBA) and graduate (MBA) studies. The
problem arises from the poor performance of students in
these subjects and scoring low grades that affects their
overall performance mean their CGPA. Major reason for
conducting this study is to identify and highlights the key
factors that contributes in poor learning and performance.
Secondly, due to poor learning and week concepts in these
subjects, students also face difficulties in understanding the
key concept in other subjects which involve quantitative
techniques like project management, operation management,
and supply chain management. Students also find it difficult
to understand the research finding and statistical results in
research articles and it also create problems while compiling
their research analysis part during their final dissertations. In
this study the key factors that contribute in poor learning due
to student attitude in quantitative subjects, teacher’s role in
terms of its competency, behavior and methodology, and the
management issues related to students that creates problem
in learning process will be investigated. This study was
conducted in the business schools of public and private

universities located in a big city of Pakistan, Lahore which is
also known as the hub of educational Institutions.
RESEARCH QUESTIONS AND HYPOTHESIS
Figure (1) depict the operational model that link the factors
contribute student’s poor learning and performance in
quantitative subjects. The following four hypotheses will be
tested using this model.
Figure 1 Operational Model
H1: There exist a relationship among teacher competency
and poor learning
H2: There exist a relationship among administrative issues
and poor learning
H3: There exist a relationship among student aptitude and
poor learning
H4: There exist a relationship among student attitude and
behavior and poor learning
H5: There exist a relationship among teacher behavior and
poor learning
METHODOLOGY
Sample
This research was conducted in the business schools both in
public and private universities located in Lahore, Pakistan.
The participant of this study was the students registered in
bachelor of business administration (BBA) and master in
business administration (MBA) studying in public and
private institutes of Pakistan located in Lahore. In this study
students with poor aptitude and lower grades in the
quantitative subjects were selected. Due to limited resources
the data was collected the business school located in city,
Lahore of Pakistan. Simple random sampling technique was
used to select the schools for responses. This study was
based on the survey technique and the survey was conducted
in 4 public and 6 private business schools at local level. The
questionnaire was developed on the basis of interviews
conducted from the students facing problems while studying
these subjects and detailed interviews with the Instructors
involved in teaching these subjects. The target population
was the students scoring low grades and facing problems in
studying quantitative subjects. On the basis of interviews, 26
major variables were identified and were included in the
questionnaire that contributes in poor learning and
performance in these quantitative subjects. Out of these 26
variables, ten variables reflects the students issues regarding
poor learning, 13 variable reflects teacher role which effects
student performance in these subjects and 3 variables are
concern with the administrative issues related to the
students. A total 300 questionnaire were distributed among
students and 233 questionnaires were returned. So the
response rate was 78%. The instrument used was a five-
point Likert Scale from strongly disagrees to the strongly
agrees. The coding of the Likert scale was made as [1 =
strongly disagree], [2 = disagree], [3 = neither agree nor
disagree], [4 = agree], [5 = strongly agree]. The gender
distribution of respondents is:
Table.1 and 2 shows the demographic Statistics of sample
comprised of gender and the student at undergraduate and
graduate level in the studies. There were total 233
participants in this study. Out of which 125 participants were
male representing 53.6 % participation and 108 were female
representing 46.4% participation of the total population.
Table 2 indicates the participation of MBA and BBA
students in the studies. There are 106 participants from BBA
with 45.5 % participation and 127 participants from MBA
representing 54.5% participation. The variables used in this
study are represented in table 3.
Table 2 indicates the participation of MBA and BBA
students in the studies. There are 106 participants from BBA
with 45.5 % participation and 127 participants from MBA
representing 54.5% participation. The variables used in this
study are represented in table 3.
STATISTICAL ANALYSIS
To test the supposed hypothesis of the proposed framework
the methodology, we use is ‘structural equation modeling’
using AMOS 16.0. Structural equation modeling is one of
the effective tools for statistical analysis and specially to test
the models that are path analytic with the mediating
variables and include the latent constructs and further these
constructs are being measured with other items included in
the study [31]. We used Chi-square, normed-chi-square
values, the goodness of fit indices of (GFI) [32] which
should not go lower than 0.70 in case of complex models
[33], adjusted goodness of fit index (AGFI), the comparative
fit index (CFI) and its value close to 1.00 indicate a very
good fit, root mean square residual (RMR) and root mean
squared error of approximation (RMSEA)[34] and for
RMSEA a value of less than 0.08 represents a good model.

490
RESULTS
The estimated path diagram for the proposed student poor
performance in quantitative subjects is generated through
AMOS 16.0 and is given in figure 2. The figure shows
standardized regression coefficients. In this estimated path
diagram the rectangles represent exogenous or endogenous
observed variables and the circles are representing the
related latent variables. The light arrows indicate the
observed variables that constitute the related latent variables
and the bold arrows indicate the structural relationship
between the corresponding variables. The numbers assigned
to each arrow represents the standardized estimated
coefficients. All the coefficients in the estimated model are
positive indicating that the effect of all the variables on
defined latent factor is positive. The direct effect of manifest
variable like TMY on latent factor PP is easily evaluated to
be (0.56 * 0.89 = 0.50). Similarly we can also calculate the
direct effect of other manifest variables. The computed Chi–
square of this model is 446.354 with 294 degrees of
freedom. The normed Chi–square of model is 1.518 which is
reasonably below the critical cut point of 3.000. This
indicates that the proposed model portrays the situation
fairly adequately. The GFI of model is 0.877 and CFI is
0.780. Both the values are close to subjective yardstick for
these two measures. This again indicates that the proposed
model fits the scenario in a good fitting manner. The RMR
for fitted model is 0.085 with RMSEA of 0.045. Both of
these measures are below the critical point of 0.1 indicating
low predictive error by fitted model. According to the above
discussion we can say that overall proposed structural model
is a fair representation of poor learning performance among
Overall we can say that the proposed structural model is a
fair representation of poor learning performance among
management students.
We have also tested all the constructs separately. The
computed value for each of the construct is presented in
table 4.According to the above detail of the individual latent
variables, it is clear that all the values are within the
specified cut off range, so the individual construct in the
proposed structural models is a fair representation. The path
diagram of this study is shown in figure 2.
TCOM
SPY
0.25
TMA
0.27
OFH
0.42
TDP
0.53
CSL
0.52
IMT
0.5
CMC 0.59
TSF
0.62
TET
0.56
TMY
0.56
ADMI
CEM
COT
ADI
NRS
0.47
0.38
0.47
STAP
MIS
DFL
SAP
NST
0.46
0.56
0.64
STAB
QAS
SSM
SRL
TPCH
LPE
CNE
TAC
TPY 0.77
P LEARNING
0.89
0.65
0.31
0.73
0.15
FSP
0.19
0.56
0.37
Path Diagram of the model
Chi-Square =446.354 df = 294 p-value = 0.000 Normed Chi-Square =1.518 RMSEA = 0045 CFI = 0.78 GFI = 0.88
Figure 2. “The estimated model using AMOS ”
0.29
0.25
0.29
0.12
0.33
0.45

Table 3: Variables Used In the Study
ariables Descriptions Mean Standard
Deviation
TCOM Teacher Competency
TMY Teaching methodology 4.0089 1.08146
TET Teacher knowledge and expertise in the subject 3.4800 1.05255
TSF First time teaching the subject 3.3556 1.20556
CMC Course covered through make up classes 3.5778 1.08333
IMT Important topics skipped 3.6222 1.10778
CSL Communication skills 3.6622 1.06555
TDP Class participation is discouraged 3.8800 1.22066
OFH Office hours not allocated 3.3244 1.29096
TMA Teaching through visual aid
3.7511 1.18409
SPY Punctuality during semester
3.8667 .95898
ADMI Administrative Issues
NRS Registered students are more than class capacity
3.4933 1.03130
ADI Administrative issues like registration in the course
3.5067 1.06536
COT Course outline, timetable
3.3289 .99477
CEM Class environment
3.2578 1.02000
STAP Students aptitude
NST Non-mathematical background
3.8533 .83495
SAP Poor attitude in quantitative subjects
3.4222 .98853
DFL Difficulties to understand derivation and word
problem
3.7333 .75000
MIS In last degree Medium of education was not English
3.6844 1.08273
FSP Family guidance/support in studies
3.5911 .85142
STAB Student attitude and behavior
SRL Revision of lectures
3.6871 1.11970
SSM Attending class without study material like books
3.5789 1.01220
QAS Quizzes and assignments are not properly submitted
3.7427 1.08490
TPY Punctuality /short attendance
3.4708 1.07927
TPCH Teacher behavior
TAC Teacher personality
3.4971 1.09048
CNE Teacher student relationship
3.9766 .98047
LPE Encourage and facilitate students to resolve study
issues
3.5205 1.11160
Table 4: Estimated values of the Latent Variables
Construct Chi-
square
CMIN GFI AGFI CFI RMR RMSEA
TCOM 68.515 1.958 .95 .92 .90 .09 .06
ADMI 4.097 2.049 .99 .96 .95 .05 .07
STAP 6.975 1.395 .99 .96 .97 .05 .04
STAB 1.502 0.751 1.00 0.98 1.00 .03 .00
TPCH 1.502 0.751 1.00 0.98 1.00 0.03 .00
With respect to teacher competency (TCOM), the teaching
methodology has a direct positive effect on poor learning
(0.56*0.89 =0.50). The factors like communication
(0.59*0.89=0.531), knowledge and expertise
(0.56*0.89=0.50), punctuality (0.25*0.89=0.22) has direct
positive effect on poor learning. Similarly, we can calculate
the other factors as all are positive, so all the factors of
teacher competency have positive impact on poor learning.
The standardized regression weight of teacher’s competency
and poor learning is 0.89. Therefore, these results support

492
the hypothesis H1 that there is a relationship between
teacher competency and the poor learning.
• With respect to administrative issues (ADMI) affect
student’s performance and causes of poor learning. We
discussed Number of registered students more than the class
capacity (0.47*0.65=0.31), registration issues
(0.38*0.65=0.25) and schedule of classes (0.47*0.65=0.31),
and class environment (0.45*0.65=0.29) has positive effects
on the poor learning. The above results indicate that there is
a strong effect of administrative issues on poor learning of
management students. The standardized regression weight of
administrative issues and poor learning is 0.65. Therefore,
we can conclude that these results support our hypothesis H2
that administrative issues effects students learning.
• With respect to student aptitude (STAP) and its impact
on poor learning while studying quantitative subjects during
their studies. The students enrolled in management
discipline with non-mathematical background
(0.64*0.31=0.20), poor aptitude in quantitative subjects
(0.56*0.31=0.17), difficulties to understand the long
derivations or to understand the word problems
(0.46*0.31=0.14), medium of instruction in the last degree
was not English (0.33*0.31=0.10), family guidance and
support to help them in these subjects at home
(0.19*0.31=0.06) has positive impact on poor learning. So,
student aptitude has direct positive impact on poor learning
0.31. Therefore, we can say that student aptitude has a week
impact on poor learning in quantitative subjects.
• With respect to student attitude and behavior (STAB)
that cause of poor learning during the learning process of
quantitative subjects. Attending the classes without revising
the previous lecture (0.12*0.73=0.88), attending classes
without study material mean books, calculators etc.
(0.29*0.73=0.21), participation in quizzes and assignments
(0.56*0.73=0.41), student punctuality in the course
(0.37*0.73=0.27) has positive impact on poor learning.
These results indicate a positive relationship of student
attitude and behavior with student’s poor learning (0.73) in
the quantitative subjects and therefore, it proves our
hypothesis H4.
• Lastly, with respect to the teacher’s behavior (TPCH)
and how it causes of poor learning. Teacher personality
(0.77*0.15=0.16), student teacher relationship
(0.25*0.15=0.038) and to student encouragement and
facilitation (0.29*0.15=0.044). The above results indicate
that there is a positive relationship between teacher’s
behavior and poor learning (0.15). It shows that teacher’s
behavior has a week impact on poor learning.
DISCUSSION AND CONCLUSION
The results represented in figure 2 supports the proposed
hypothesis. With respect to teacher competency, analysis
shows that teaching methodology, expertise in the subject,
communication skills, and punctuality plays an important
role in the effective learning process. Therefore, to increase
interest and enhance student learning in these subjects the
above variables plays a significant role. Secondly,
administrative issues also affect the student’s effective
learning process. As in this study the matters related to
students registration in the particular course/with a particular
teacher or students enrolled more than the class capacity
mean sometime students love to enroll with the best teacher
in that particular subject and due to the reason the class is
overcrowded or time table issues like in this study
particularly for those students who are repeating the course
there may occur clashes mean at the same time he/she have
to attend two different course were some of the factors that
causes of poor learning in these subjects. The results in the
figure 2, indicates a positive relationship with the poor
learning.
Another main factor which plays a significant role in
student’s performance in the quantitative subjects is their
aptitude in these subjects. In our study most of the students
enrolled in the management discipline (BBA & MBA) is
lack of mathematical skills mean these students have not
studied mathematics or statistics in their previous degrees as
discussed above. There is a general perception among
students that mathematical subjects are difficult to learn as it
involves formulas and derivations which are difficult to
digest and remember, due to this reason they developed a
poor aptitude in the quantitative subjects. In Pakistan most
of the population is living in rural areas, where educational
opportunities are very rare as compare to the urban areas.
This also causes of poor aptitude in learning process.
Secondly, in most of the cases the parents are uneducated
and due to this reason these students find no support from
their families to get help in the studies. The analysis shows
that there is a positive relationship among student aptitude
and poor learning.
Student’s attitude and behavior is significant factor in
learning of quantitative subjects. The students attending the
classes without the study material like books, calculator etc.
is unable to understand the concepts: especially word
problems and the calculation. Another reason is that students
attended the classes without revising the previous lectures
and it also matters to understand the next lecture. These
students also try to avoid to participate in the quizzes and to
submit the assignments at the mentioned time. There is
another reason that these students do not pay attention to
attend the classes regularly. The analysis in figure 2 proves
that students attitude and behavior contributes in poor
learning of quantitative subjects.
Lastly, the teacher behavior is another factor in our study
that contributes in poor learning. In learning process teacher
act as a role model and a good personality is one attribute.
A strong student- teacher relationship helps to create a
healthy working environment in the class. It creates
motivation among students to increase interest in the subject
and students love to actively participate in discussions which
help to enhance their learning abilities. Figure 2 shows there
is a positive relationship among teacher behavior and
learning.
From the above discussion it is concluded that teacher
competency in a particular subject, administrative issues
related to student, student’s aptitude, student attitude and
behavior and teacher’s behavior are the key factors that
contributes in student’s poor learning. To overcome these
issues it may be possible to increase student interest in these
subjects and better performance. There is another option that
universities should offer some preliminary courses to
overcome this problem. So we can say that the studied factor
has a positive impact on student’s poor learning and
performance in quantitative subjects.

LIMITATIONS AND FUTURE RESEARCH
Despite some contribution to the literature and most
common factors that contributes in student’s poor
performance in quantitative subjects. This study has some
limitations that should be addressed in the future studies on
this topic. The first limitation of this study is that the data
collected for this study is limited to only 4 public
universities and 6 private universities that are located in one
city (Lahore) of Pakistan. Secondly, there is a need to
identify more factors to conduct a comprehensive study on
this topic and it needs more inputs from students and
teachers. As we have 132 public and private universities in
the country, so comprehensive results can be drawn and
generalized. This study is conducted on personal expenses
and to approach all the universities it requires funding. We
hope this study serves as the basis for an effort to sharpen
understanding of some of those factors that contributes in
student’s poor learning and performance in quantitative
subjects. Another limitation of this study is that it is
conducted in only few universities located in one city of
Pakistan so, the results of this study cannot be generalized.
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