CHAPTER 4: EMPIRICAL RESULTS AND DISCUSSIONS OF QUANTITATIVE ANALYSIS
4.5. Summary
140 It is worth mentioning that there is a relationship between the students’ academic performance and other modules taught concurrently at university. In the OLS and Logistic regression analyses, the student success in the ECON101 module has been found to have causal effects (38 percent in relative frequency) on their success in the ACCT101 and ECON102 modules. The student success in the ACCT101 (25 percent in relative frequency), and MATHS134 and STAT181 modules (both have 13 percent in relative frequency) has also been found to have causal effects on the student success in the ACCT102 module. The students’
final examination marks in ACCT101, ISTN101 (13 percent in relative frequency), and the quantitative method MATHS134 modules have been found to have causal effects on the student success in the ECON101 module. This possibly indicates that first-year students who pass do better in both the accountancy and economics modules.
In the Logistic regression analysis the student success in the MATHS134 module has been found to have causal effects on the odds ratio of success in the ECON101 module. The students’ final examination marks in the ECON101 and ISTN101 modules have been found to have causal effects on the odds ratio of success in the ECON102 module. For the other first-year modules at the university level taught concurrently with economics, this possibly means that knowledge of Information Systems and Technology 101 helped students to understand Economics 101 and Economics 102 better. The results indicate that there is a fairly consistent relationship between students’ academic performance in ECON101 and ECON102 modules.
Of interest to this study is that, evidence emanating from the empirical analysis reveals that, although the ECON101 module is not a prerequisite for ECON102, students who do better in ECON101 are more likely to also do better in the ECON102 module. Knowledge of Information Systems and Technology for business and having well-rounded quantitative MATHS134 skills helped improve final examination marks in the ECON101 and ECON102 modules. These relationships appear to be fairly consistent across the semesters measured. These findings have a variety of education policy implications that are discussed in Chapter 6.
141 indicated that total matric points and selected matric subject scores that include Accounting, Economics, English I, and Maths have correlations with the student success in undergraduate accounting and economics modules.
A deduction from these results is that screening and admitting students with better total matric points (also referred to as APS) in the admission process should go a long way to help increase pass rates in first-year accounting and economics modules, and improve the retention of substantial numbers of students on the graduation path in the CLMS at large. These results confirm that students are generally more likely to do better in first-year accountancy and economics modules if they have been previously exposed to these subjects at high school level. It seems reasonable for the FMS to expect future student success in ACCT101 and ACCT102 modules as well as in ECON101 and ECON102 modules to have positive relationships with these listed determinants of student success.
Correlations sweep also indicated some pointer to settle the whole question on the predictors of student performance at the university intake level are wearing off as students progress to second- and third-year accounting and economics modules in the FMS. A perusal of results indicates that correlations factorized above are not wearing off as the student progresses in the FMS. Therefore, to some extent, the total matric points, matric Maths, and matric English I are predictors of student success after the intake level.
A perusal of the results illustrating correlations sweep between the student performance in first-year accounting and economics modules, on the one hand, and their performance in second- and third-year modules at university level reveals that, the student performance in first-year accounting modules are correlated with ACCT200, ACCT2ISR, and ACCT300. The student performance in ACCT101 and ACCT102 was correlated with their performance in ACCT2A0. Performance in ISTN101 and ISTN102 was also correlated with ACCT2ISR. The student performance in ACCT101 was correlated with ACCT102 in 2008. The student performance in first-year economics modules are correlated with their performance in second-year modules but were never correlated with the third-year module. All the pair-wise correlation coefficients between the student performance in first-year economics modules and second-year modules are statistically significant. Information systems and technology modules at the first-year level (ISTN101 and ISTN102) are good predictors of success in second-year accounting information systems (ACCT2ISR) modules and the student performance in first-year accountancy modules was a good predictor of success in ACCT200 module. First-year performance is not correlated with the third-year module. Correlations between second-year and third-year modules were never statistically significant.
142 A further deduction that can emanate from the empirical results is that passing students are more likely to do extremely well when they progress to second-year modules. Alternatively, struggling students are more likely to do extremely badly. Therefore, student performance in first-year ECON101 and ECON102 modules, as well as in the quantitative method course are good predictors of whether the student will perform well in the second-year economics modules, but they are inconclusive in predicting third-year modules. An important finding is also that the correlation between undergraduate modules and matric subject scores (or their aggregate total matric points) is not wearing off as the student progresses in the FMS, except that there is a relatively weak positive correlation between the student performance in ECON202S modules and matric English II, suggesting that students whose the home first language is not English and who wrote matric English II, are more likely to perform less well even at the second-year level.
However, as these correlations have significant coefficients with low magnitude and they were sporadic and not persistent (have low probabilities), this study cannot jump to the conclusion that they are salient and straightforward predictors of student success, demanding further analysis.
The empirical results of the OLS and Logistic regression analyses and correlations sweep taken together provide a definite statistically significant support for some predictors, which have shown some evidence of linear relationships. Salient predictors of student success at university include total matric points (or APS), proficiency in English that is having English as home first language (not students who have taken English (I or II) at the school leaving level), and matric Maths performance. Student success in first-year accountancy and economics modules is also influenced by previous matric Accounting and Economics, and the student’s final examination marks in ISTN101, MATHS134, and STAT181 at first-year level at university.
The logistic regression analysis also give support to a marked improvement in performance and pass rates at the upper end of total matric points (or APS)when using a typical total matric points threshold. Total matric points of 36 set as the entrance requirement for the BCom (Accouting) and BCom (General) degree was confirmed as good predictor of student success in the regression analysis. This finding suggests that total matric points (or APS) at the upper end are a relatively good predictor of university success at the intake level.
Age at the point of admission and the race of the students also play some role in predicting student success and can therefore be generalized to have an effect in boosting or impeding student success in the FMS. That is, respectively, a young age is statistically significant at the intake level, English as a first home language, and good quantitative skills help students do well in the FMS. Non-white students (black Africans,
143 Coloureds, and Indians) are likely to perform less well than white students. This later results on race does not imply that all black African, Indian, and Coloured students are not competent good and that it will be impossible for them to cope or do well in BCom (Accounting) and BCom (General) degree modules. There are non-whites students who are outperforming their white peers in the FMS.
A deduction that can emanate from the empirical results of the logistic regression analysis is that students who meet minimum requirements in terms of total matric points, have quantitative skills and English language proficiency, and sorted by other personal and some student demographics such as age and race of the student are more likely to perform better in first-year accounting and economics modules. More specifically, as hypothesized, the BCom (Accounting) and BCom (General) degree are the more mathematical degrees in the FMS, and the importance of mathematical skills to student success in the FMS has been supported in this study.
The results of these OLS and logistic regression analyses are robust across undergraduate accountancy and economics modules and across the two academic years. Thus, the results presented and discussed in this study can be generalized to any multiple linear regression model involving any number of explanatory (regressors) variables. These results are in line with national and international studies. Designated matric subject scores such as Maths scores and English I scores are salient predictors of student success in the College (Mitchell et al., 1997). A positive correlation was found between student success and HG Maths scores, as well as the aggregate matriculation points at the Stellenbosch University (Horn et al., 2011).
Yathavan (2008) notes that the total matric points (a student’s high school aggregate) is the most influencing variable of first-year performance at the University of the Witwatersrand. Matric Maths and English scores are all related to first-year performance (Eeden, Beer and Coetzee, 2001; Yathavan, 2008).
Duff (2004) is of the opinion that student performance in school examinations is a strongest predictor of first-year academic performance and progression at university. Mc Nabb et al. (2002) and Smith and Naylor (2001) found that final examination marks in first-year Maths at university is a good predictor of subsequent academic performance in economics. Horn et al. (2011) reported that academic performance in the first-year is an important determinant of success in the second-year and most matric subjects become statistically insignificant as contributors to academic success for second-year students.
This study, however, cautions that all the predictors identified in the regression analyses though important, play only a minor role since they predict only a proportion of the entire variance in the students’
performance during the two academic years’ cohort of students. This is evident from some of the pseudo R2
144 and R2 which ranged from as low as 2 to as large as 65 percent pointing out large variations in the explanatory power
Thus, this suggests that results from the regression analyses alone are not enough to explain entire variances in the performance of students in the College. Matric scores are only a small part of the overall picture in the characteristics of students (low coefficients not supported by the probabilities). Premised upon the evidence that emanated from the empirical results in the correlations sweep and in the regression analyses, the determinants of student performance are far from being predicted at the time of university entrance. This study suggests the implementation of additional mechanisms to be used in conjunction with the total matric points to select candidates in the College.
Indeed, international studies point out exogenous factors including inter alia hard work and discipline, previous schooling, parents’ education, family income and self-motivation as factors that can explain differences in university student success. Siegfried and Fels (1979), for example, concluded that the student’s aptitude is the most important determinant of his/her university success. Beron (1990) found that there is a link between the perceived usefulness of an additional course in economics and the performance of the students in a current economics course who want to take another economics course. These results have implications for selection and admission policy, curriculum development, module contents, module prerequisites, student support systems, and strategic planning to enhance the characteristics of students that help them become successful in the FMS. Thus, additional mechanisms are needed and should be considered in the selection and admission of candidates and their placement into appropriate curricular routes where they are more likely to be successful in the FMS. This study offers hypotheses, suggestions, and policy implications in the following Chapters 5 and 6.
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