Abstract:

The first of its kind, this research explores the degree to which personality, divided into facet level traits, predicts academic performance of undergraduate students in the core courses of the Computer Science (CS) discipline at the Punjab University College of Information Technology (PUCIT), Pakistan.

Thus, the objectives of our study are to examine:
1) Whether, and to what extent, the six HEXACO personality dimensions can predict the academic performance of undergraduate students in the CS core courses.
2) Which among the domain level and facet level traits are more significantly correlated to the academic performance of undergraduate students in the CS core courses.

The participants of our study were 100 undergraduate students (75 males and 25 females) in the BS Computer Science program. The range of their age was 19-21 years, with an arithmetic mean of 20.10 years and Standard Deviation (SD) of 0.835 years. The participants were in the fifth semester of their degree program at the time of data collection.

In order to assess the personality traits of our participants, we used statement measures HEXACO-PI-R based on the HEXACO model of personality (Ashton & Lee, 2009). HEXACO-PI-R is a 60-items questionnaire that measures six HEXACO personality dimensions i.e., Honesty-Humility (H), Emotionality (E), eXtraversion (X), Agreeableness (A), Conscientiousness (C), and Openness to experience (O).

Academic performance of the students was obtained from the Examination Department. We collected the final scores of eight core courses i.e. Programming Fundamentals, Object Oriented Programming, Data Structures & Algorithms, Discrete Mathematics, Digital Logic Design, Computer Organization & Assembly Language, Analysis of Algorithms, and Operating Systems. These final scores consist of Midterm Exam, Final Term Exam, and Semester Activities (quizzes, assignments, homework, presentations etc.).

The correlation between the academic performance of the undergrad students in the eight CS core courses and personality traits (both domain level and facet level) was performed on the data. We, then, classified these eight courses into five clusters/groups to extract more meaningful results. Our classification was based on the pre-requisite and co-requisite relationship between courses and the similarity of the content covered in the courses. Correlation between the academic performance in the five course clusters and personality traits (both domain and facet level) were, then, performed on the data.

The results of the study show that Honesty, Emotionality, Extraversion, Conscientiousness, and Openness to Experience are significantly linked to the academic performance of students in the various core courses in Computer Science. The study also discovers relationship between the facet level personality traits and different courses in the Computer Science domain. The findings are discussed with reference to previous research on personality-academic performance relationship. The findings of the study can not only be used for the mentoring of the students but also be used to improve classroom learning of students in the CS core courses by devising teaching strategies based on the personality traits of students. The results can also be used to design intelligent tutoring systems that cater to the needs of students based on their personalities.

Keywords:

Computer Science Education, Personality, Academic Performance, HEXACO Personality Model, Computer Science Core Courses.