CHAPTER 2: BRIEF SURVEY OF LITERATURE
2.3. The determinants of student academic performance
2.3.1. Variables attached to the general characteristics of students
STUDENTS
Horn et al. (2011) and others identified the characteristics of students that affect their performance (McGuckin et al., 1979; Chizmar et al., 1983; Beron, 1990; Park and Kerr, 1990; Romer, 1993; Anderson et al., 1994; Tay, 1994; Bailey and Rask, 1996; Koh and Koh, 1999; Belfield, 2000; Edward, 2001and 1999; Krieg and Uyar, 2001; Gracia and Jenkins, 2003 and 2002; Borg and Stranahan, 2002; Schuetze and Slowey, 2002; Dolton et al., 2003 and 2001; Gammie et al., 2003; Duff, 2004; Van Den Berg and Hofman, 2005; Johnson and Kuennen, 2006; Millar, 2006; Parker, 2006; Pseiridis et al., 2006; Tewari et al., 2008;
Guney, 2009; Leibowitz et al., 2009; Cappellari et al., 2010; Ganpath, 2010; and Lubben et al., 2010). A list of these variables (non-exhaustive and not in any particular order) is presented in Table 2-2.
In the US and European countries, Van Den Berg and Hofman (2005) suggested that approximately 95 percent of total variance in student performance is ascribed to student-related factors. Furthermore, they found that the first- and second-year at a university are significantly more challenging for a student than the following years, and in general first- and second-year students have different study success, lifestyles, and study and work behaviour than third-year and upwards. Wößmann (2000) estimated a micro-econometric student-level model based on data from 39 countries and found that international differences in educational institutions explain the large international differences in student success in cognitive achievement tests.
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Table 2-2: Factors/Variables Attached to the General Characteristics of Students
Socioeconomic Background
• Socio economic status/ poverty/ family structure / minority /gender/race/ from own-race (racial match or mixed-race) (Ferber, 1995)
• Investment in children, children’s age in student’s household; student birth order, student’s childhood, student’s birth weight; parental contribution/involvement in education
School Endowment
• Higher/lower quality of School attended. DoE’s quintile ranking of school attended
• Language proficiency (Medium of instruction)
• Twelfth grade of high school average score in courses or grades on specific subjects/ total grade (Matrics) or average achieved in one or more courses (Tewari et al., 2008)
• Cumulative number of credits; proportion of passed exams to exams taken or number of passed exams, GPA (Yathavan, 2008; Walstad et al., 2001)
• Pass rate or failure rate in courses
Study Environment
• Worse home/ neighborhood (creates setbacks and is less conducive to high educational attainment)
• Interaction, identification, socialization with other or across students of different races and like
• Difficult courses offered, study period; conditions, satisfaction from studies’ time table: evening classes, etc (Lubben et al., 2010; Cohen et al., 2009; Walstad et al., 2001; Rutter, 1979).
Personality
• Ability, age, ambition, attendance, attitude, goals, preparedness, envisioning skills (Marburger, 2001)
• Home language (Lubben et al., 2010)
• Systematic differences in self-control and motivation, peer group (Coleman et al., 1996)
Financial Incentives
• Bursaries, financial aid, subsidies, tuition, sponsorship, loans, working status, etc.
• Lost of parent
Others
• Trauma, ill-health, personal circumstances.
Source: Based on survey of literature of existing studies as reviewed in Section 2.3.1.
Pseiridis et al. (2006) used a comprehensive model to explain student performance in terms of the average of high school grades in mathematics and economics, weekly hours of lecture attendance, money spent per month, gender, etc. Romer (1993) found that class attendance is reflected significantly on the students’
Grade Point Average (GPA). Anderson et al. (1994) found that the most important factors that affect
57 student performance in university introductory economics course were the overall achievement level and taking a course in calculus. With regard to gender, they found that male students outperform their female counterparts. Kennedy and Tay (1994) concluded in their survey article that research on the factors affecting student performance in economics points to students’ aptitude as the most important determinant of university success. However, study effort and the age of the student have also positive effect on student performance.
Studies in the US and Europe have also found other sundry characteristics attached to student performance.
These include: memory and note-taking affecting learning in the introductory courses in economics (Cohn et al., 1995), previous GPA, class attendance, motivation and financial status (students who support themselves financially) affecting positively the current student performance (Devados and Foltz, 1996).
Statistics anxiety and attitude, and computer experience are linked to student performance in statistics courses (Zimmer and Fuller, 1996). On the one hand, the likelihood of a student making a grade of A or B in principles of economics significantly decreases as the number of absences increases, when the student is a member of fraternity or sorority, and as the number of credit hours carried by the student during the semester increases. On the other hand, the chance of a student making an A or B in the course significantly increases with having taken a calculus course, a higher GPA, and higher SAT (Matric) scores (Ellis et al., 1998). Age and students’ attitude toward accounting have a significant effect on student performance on an introductory undergraduate financial accounting course (Lane and Porch, 2002). The chilly classroom climate for women and minority students and differences by gender in mathematics ability or preparedness are a possible cause of low pass rates for these groups (Ferber, 1995).
In the United Arab Emirates University, Asia, the most important factors with a positive effect on student performance are a student’s competence in English, having positive attitudes towards the university, and class participation (non-national students outperform national students and female students outperform male students). On the other hand, the most important factors that have a negative effect on student performance are missing too many classes, credit hours achieved (progression of the students in his/her study plan), and the student’s economic background captured by the crowding variable of the number of people who live in the student’s household divided by number of rooms in the house (Harb and El-Shaarawi, 2006).
In South Africa, success of students in an introductory accounting course at the University of South Africa (UNISA) is attributed primarily to proficiency in English language and prior experience in accounting and mathematics (Du Plessis et al., 2005). With regard to background variables, high school performance and
58 school achievement have positive effects on student performance, while there was no statistical evidence of significant association between family income level and students’ academic performance (Karemera, 2003).
Millar (2006) who tracked the progress of two cohorts of students (the cohort of 1999-2003 students and the cohort of 2000-2004) in one of the constituents of UKZN, the former UN, found that at the undergraduate first- and second-year modules in BCom (Accounting) there were positive relationships between students’
final examination marks and both the total matric points (or APS) and matric Maths results. At third- and fourth-year of university studies, matric Maths became a better predictor of student success than the overall total matric points. Success of first-year economics students at South African universities is attributed to lecture and tutorial attendance, age, gender, and matric scores (Horn et al., 2011).
2.3.2. VARIABLES ATTACHED TO THE GENERAL CHARACTERISTICS OF