ICES
3° International Conference on Educational Sciences 2014
TIRANA ALBANIA, April 24-25, 2014
INTRODUCTION
Quality of education improves the quality of human resources
and is directly related to increased individual earnings and
productivity, and economic growth. Educational quality is
typically measured by higher achievement in examinations. In
the learning process significant influence have families,
communities and peers as well as formal institutions; and
social, economic and cultural factors influence the extent to
which family members support children’s classroom learning.
Determinants of student performance have attracted the
attention of academic researchers from many areas. They have
tried to determine which variables impact student performance
in positive and negative direction. Determining the factors that
affect the student performance is important, because, primarily
institutions and lecturers have to find out ways to increase
student performance, and to motivate students for better
performance. In order to do this, first they need to determine
which factors play significant role in student performance.
Socio-demographic factors such as age, income, gender,
psychological factors such as motivation, stress, study
strategies, and other factors like study hours and understanding
the language of instruction are among the factors that could
play substantial roles in students’ academic success.
A number of studies have shown that there is a strong link
between mathematical background and performance in
Economics and Finance units, and hence overall performance in
the degree. Lagerlof and Seltzer (2009) found that, consistent
with previous studies, the level of and performance in
secondary school mathematics has strong predictive power on
students' performance at university level economics. Butler et
al. (1994) studied the effect of Calculus on learning
intermediate microeconomics and macroeconomics and found a
positive and significant association between intermediate
microeconomics, but failed to establish the relationship with
macroeconomics. Mallik and Lodewijks (2010) found that
higher level mathematics (with calculus) and economics in high
school can increase the marks in first year introductory
economics subject significantly.
The purpose of this study was to identify and analyse the
factors that influence academic performance in General
Mathematics course for first-year business students. This study
contributes towards unraveling those significant determinants of
students’ performance that need to be addressed. The findings
of this study may also be applicable to related courses (those
that require the application of Mathematics knowledge) with
high failure rates.
Factors Impacting General Mathematics Success
for first-year Business’ students:
Evidence from Albania
Alma Spaho
University of Tirana, Albania
E-mail: alma.spaho@unitir.edu.al
ABSTRACT
One of the benefits of good education is that it enables individuals to contribute to the
development and improvement in the quality of their life, their communities and the nation
as a whole. Students’ achievements in mathematics in high school are prerequisites for
admission into university and have a significant effect on their academic performance in
university. In addition, mathematical and quantitative skills create possibility for better
chances for employment, higher wages and higher productivity on job. Thus, mathematics
learning and students’ performance in mathematics receive considerable attention from
educators, teachers and parents and is important to identify the factors that could influence
students’ mathematics achievement to help them improve and make substantial academic
progress. This study aims to investigate factors associated with business student’ success in
General Mathematics course. The population of the study consists of business students
attending first-year at the Faculty of Economy, at University of Tirana for 2012/2013
academic year. Using the simple random sampling, 143 business students were randomly
selected and completed a questionnaire during the first week of June 2013. The
questionnaire included questions about student characteristics, family characteristics and
high school characteristics. The binary logistic regression was used to estimate the impact
of lecture attendance, study hours, working status, average grade in mathematics in high
school, father education level, mother education level and monthly family income on final
examination success. The results indicated that lecture attendance, average grade in
mathematics in high school, study hours and working status were statistically significant
variables that influenced their final success in General Mathematics course. These findings
suggest that, primarily, institution and lecturers must find out ways to increase student
performance, and to motivate students for better performance. Enhancing student
participation should be a crucial aspect of administration, in order to improve performance.
At the same time, the lecturer should also create a good learning environment, to motivate
students and engage their interest about the course.
METHODS
LITERATURE REVIEW
Many studies have been carried out to identify and analyse the
numerous factors that affect academic performance in various
centers of learning. Their findings identify students’ effort,
previous schooling, parents’ education, family income, self
motivation, age of student, learning preferences, class
attendance and entry qualifications as factors that have a
significant effect on the students’ academic performance in
various settings (Mlambo, 2011).
According to Uyar and Güngörmüş (2011), grade point average,
high school type, age of the student and attendance were
significant variables which influence student performance.
Grade point average, high school type, attendance had positive
influence on student performance, whereas age of the student
had negative influence. In their study, Hijazi and Naqvi (2006)
found that class attendance; mother education and study hours
had significant impact on student performance. Class
attendance and mother education had positive impact on student
performance, while study hours had negative impact. Olaitan
(2012) in his study found significant association between high
school results and student performance, and between family
income and student performance. Among the factors affecting
students’ grades according to Kara et al (2009) significant
factors were gender, SAT score, number of hours worked, and
number of missed classes, while the number of hours per week
spent on studying for the class was not significant. Moreover,
gender and SAT score had positive effect on students’ grades,
while the effect of number of missed classes and number of
hours per week worked at a job was negative. The findings of
Erdem et al., (2007) indicated that, the type of high school,
residence of the family, number of sisters/brothers in school,
and weekly study hours were statistically significant and
positively related to the cumulative grade point average. The
variables gender, father education and mother education were
statistically significant and negatively related to student
performance.
The study population consists of business students in the 2012-13 academic year attending
first-year at the Faculty of Economy, at University of Tirana. Using the simple random
sampling, 143 students (N = 450) were randomly selected and completed a survey at the last
week of the second semester. The questionnaire included personal questions about age,
gender, number of hours per week using computer, current working status; questions about
general mathematics course such as lecture attendance, study hours; questions about high
school such as average grade in mathematics, residence, and type; and family characteristics
such as monthly income and parents’ education level. The outcome variable was the grade
in General Mathematics course, which was obtained from the lecturers of the course.
STATA was used to perform logistic regression analysis.
A logistic regression model with a dichotomous response of success or failure was modeled.
Success was defined as earning a final grade of 5 or higher in the mathematics course.
Students who received a final grade of 4 were considered to have been unsuccessful. For
the analysis, the response was coded as 1 or 0, respectively. The independent variables of
the logistic regression model were dummy variables that indicated the characteristics of the
students: lecture attendance coded 1 if the student attended more than ten lectures (weeks)
and 0 otherwise; study hours coded 1 if a student spent averagely more than 5 hours
learning mathematics during the weeks of the semester and 0 otherwise, working during the
semester coded 1 if the student worked and 0 otherwise, and average grade in mathematics
in high school coded 1 if the average grade was more than 8 and 0 otherwise. Other
independent variables were dummy variables that indicated family characteristics such as
monthly income coded as 1 if the monthly income of the family was more than 60,000 leks
and 0 otherwise; father education coded 1 if the father education level was university or
more and 0 otherwise; and mother education coded 1 if the mother education level was
university or master and 0 otherwise.
Logistic regression is recommended over linear regression when modeling dichotomous
responses and allows the researcher to estimate probabilities of the response occurring
(Hosmer and Lemeshow, 2004). The logistic regression equation takes the following form
ln(p/1-p) = β0 + β1x1 + β2x2 +… + βkxk
where p is the estimated probability of success, and x1, x2, …, xk are independent variables.
The estimated probability of the response occurring or success (p) divided by the probability
of it not occurring or not success (1-p) is called the odds ratio. Maximum likelihood method
is used to estimate the odds ratios of the model. Values of odds ratios higher than 1 indicate
positive association between the variables, odds ratios equal to 1 indicate no association,
while odds ratios lower than 1 indicate negative association between each independent
variable and the dependent variable of the model.
RESULTS
Majority of the students in the sample were females (89%), 19
years old (83%), have attended an urban high school (91%)
and a public high school (89%). Around 38% of students
spend more than 5 hours every week using computer, mostly
for projects, home-works, and social networks. Only 14% of
them spend time working during the week and 78% of them
had average grade in mathematics in high school more than 8.
Around 46% of the students had family monthly income more
than 90,000 leks; mother education level was high school for
43% of the sample, whereas father education level
professional high school for 35% of the students. About 60%
of the students did not take a passing grade in General
Mathematics course.
Logistic regression model was estimated to identify the
variables associated to success in the General Mathematics
course in the sample. Pearson correlation coeficients between
independent variables were lower that 0.57.
Independent variable
Lecture attendance during the semester
More than 10 lectures
Ten or less lectures
Average study hours during the weeks of the semester
More than 5 hours
Five or less hours
Working during the weeks of the semester
Yes
No
Average grade in mathematics in high school
More than 8
Eight or less
Monthly family income
More than 60,000
Less than or 60,000
Odds ratio
3.28*
1.00
2.08**
1.00
2.46**
1.00
2.82*
1.00
0.52**
1.00
Note: * p-value < 5%, ** p-value < 10%.
Results of the logistic regression model indicated that the
variables lecture attendance and average grade in mathematics
in high school were statistically significant at 5% level. The
other variables, study hours and working during the semester
were significant at 10% level. The variable, average monthly
family income was significant at 10% level, that is students
with average family monthly income more than 60,000 leks
had less chances (OR = 0.52) to pass the final exam.
DISCUSSION
The data analysis of the study highlighted significant linear
association between lecture attendance, average grade in
mathematics in high school, study hours during the weeks,
working during the semester and family monthly income and
final success of General Mathematics course for first-year
business students.
The attendance of lecture was positively associated with
student success in the General Mathematics course. A
number of studies have examined the relationship between
students' attendance and academic performance, generally
finding that attendance impact significantly the academic
achievement (Cohen and Johnson, 2006; Kirby and McElroy,
2003).
The number of study hours per week during the semester was
positively and significant associated with the success. This
finding was in support to the study of Erdem et al (2007).
Unlikely previous studies of, Hijazi and Naqvi (2006) that
found that study hours had negative impact on student
performance, and Kara et al (2009) that found that the number
of study hours per week was not significant.
Working during the semester was positively related to final
success, this might be due to the fact that working students
take more responsibilities than other students, so they look at
education as seriously. This finding was consistent with the
studies of McInnes and Hartley (2002) and inconsistent with
the findings of Kara et al., 2009.
High school grade point average is also important as
predictors of performance at other levels of education
(Kuncel et al, 2005). According to the study of Anderson et
al. (1994), students who received better scores in high school
also performed better in college. The average grade in
mathematics in high school was also significant and
positively associated with student success in the General
Mathematics course. This finding was consistent with the
studies of Uyar and Güngörmüş (2011), and Olaitan (2012).
Family monthly income was found negatively significant in
this study. This finding was inconsistent with the study of
Goldhaber and Brewer (1997) that found a positive
relationship between family income and maths achievement.
CONCLUSION
The educational performance of the students is one of the main factors of their success in the future. Thus, focusing on the effective factors in students’ success is one of the vital factors in
higher education system and this is the reason why many research papers have been published about these factors. One of the important factors that have been frequently referred is the
essential differences among the students’ personal characteristics which are effective in their efficiency in learning. The results of logistic regression models indicated significant linear
association between lecture attendance, average grade in mathematics in high school, study hours during the weeks, working during the semester and family monthly income and final
success of General Mathematics course for first-year business students.
At the beginning of the semester, students should be informed of the empirical relationship between class attendance, study hours, working status and performance. Based on these findings
enhancing student participation should be a crucial aspect of administration, in order to improve performance. Classes scheduled between 10 a.m. and 3 p.m. have better attendance, thus
scheduling classes, particularly core or required courses, during these hours is strongly recommended (Devadoss and FoltzSource, 1996). To encourage attendance, instructors may assign
a certain percentage of the total marks to lecture attendance. To encourage the study of the subject during the semester, instructors may assign a certain percentage of the total marks to
home-works, quizzes and projects. Excessive financial stress and working odd jobs affect students' academic performance; therefore, students need to be counseled to attempt to find work
related to their studies or to seek financial aid to alleviate their financial burden (Devadoss and FoltzSource, 1996). These findings have important contribution to the university
administrations, policy makers, students and students’ families in terms of providing them which factors have impact on student success. Also, the lecturers should create a good learning
environment, to motivate students and engage their interest in the course. This study examined the impact of some factors on the student success. Other factors such as student selfmotivation, stress, entry qualifications, study strategies, instructor and understanding the language of instruction was not examined. In future research, these factors can be examined.