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A STUDY ON IDENTIFING FACTORS AFFECTING ON STUDENT’S GPA : A CASE STUDY IN THE
FACULTY OF APPLIED SCIENCES WAYAMBA UNIVERSITY OF SRI LANKA
(2000) behavior of the student has a big market. The questionnaire was distributed
influence to their academic success and during the lectures, practical sessions and
failure. According to Robbins, Lauver, in hostels.
Lee, Davis, Langley, & Carlstrom,
SPSS (IBM SPSS Statistics 22),
(2004), achievement motivation, statistical software was used to analyze
academic goals, institutional the collected data. A descriptive data
commitment, perceived social support, analysis was carried out to understand the
social involvement, academic self- composition of the third-year students’
efficacy, general self-concept, academic-
related skills and contextual influences data. Using bars charts, pie charts and
tables, the variations of the collected data
are some of the psychological factors that are analyzed.
directly affect the performances of the
undergraduates. Then the Mean values, Ranges,
Standard deviations of the collected data
3 METHODOLOGY
were analyzed. After checking the
For this study, 19 factors are correlation and the p-values of the
considered by assuming that they have an analyzed factors the regression model
impact of GPA value of students. A between variables was fitted.
multiple regression model was developed 5 RESULTS AND DISCUSSION
to find out the factors that affect the
GPA. Pearson Correlation analysis revealed
Under the descriptive analysis bar that the factors, Total number of repeats,
charts, pie charts and frequency tables Involvement in Sports, Subject Combination,
were drawn to understand the distribution Having Good Foundation in 1st Year, Having
of each independent variable properly. Good Relationship with Lectures and
Pearson Correlation test was carried out Participation to the informal revision classes
to find out whether there is a linear conducted by the students have significant
relationship between each independent correlation with the GPA value.
and dependent variable. Enter method Females have a higher mean GPA value
was used to fit the multiple linear than the males. Females’ GPA values lie
2
regression model for data set. Higher R between the ranges 1.78 to 3.50. But male’s
value explains a higher variability GPA values lie between the ranges 1.50 to
describe by the model. Variance inflation 3.60. According to collected data mean GPA
factor (VIF) can be used to determine value of the students is 2.57. Maximum
whether a high degree of GPA value of 3 year is 3.60.Considering
rd
multicollinearity between the predictor about students’ attendance, only few
variable that can be affected the estimates students attends 2-5 lectures per week. Most
of standard error for the estimated number of students attends 5-10 lectures per
parameters. week. Comparing the attendance gender
wise female students are more likely to
4 DATA COLLECTION AND
ANALYSIS participate to the lectures than male
students. According to the collected data
Primary data was collected by 46% of students are involved in sports and
distributing a questionnaire among 44% of them are females. 54% of students
students. Third year undergraduate do not involve in sports. 38% of students do
students were the target group of this not have enough time to involve in sports.
study because they are the people who 24% of students allocate 1-2 hours for
have the most experience in University sports.
life and they are about to enter the job
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