CHAPTER 3: THEORETICAL FRAMEWORK, METHODOLOGY, AND DATA
3.1. Theoretical framework
3.1.1. Modelling consideration
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66 First, this study examines the FMS, CLMS at UKZN as an educational enterprise that uses educational inputs (funding, policy, resources, and technologies, etc.) to educate students to achieve educational outputs (educational outcomes such as mitigating the attrition of students, improvement of the retention, achievement or attainment of students, graduation and throughput rates, etc.) analogously to how firms produce outputs.
Second, for the functional form, this study uses linear and logistic educational production function approaches and embeds a number of determinants (independent variables or educational inputs) that are likely to determine student performance (dependent variable or educational output or outcome) within the FMS. Economic methods and principles sourced from existing economics of education studies are then applied to assess the efficiency of these embedded educational inputs to enhance the effects of educational outcomes or outputs (Horn et al., 2011; Tewari et al., 2008; Park, 2006; Berg and Hofman, 2005; Dolton et al., 2001, 2003; Edward, 1999, 2001; Belfield, 2000; Edwards, 2000; Hanushek, 1995; Chizmar et al., 1983; McGuckin et al., 1979).
Third, variables that are used to define or signify students’ academic performance in existing studies are varied and controversial. The most commonly used variables are (Kontolaimou et al., 2006): the grade achieved in one or more taught courses (Borg and Stranahan, 2002); the total or average grade in courses taken within a year of study (Gammie et al., 2003; Gracia and Jenkins, 2002; Koh and Koh, 1999); and the cumulative number of credits in a number of years (Hakkinen, 2004). Some studies consider other measures such as the proportion of passed exams to exams taken in a year; or progression rates calculated for a group or for individuals (Baxter and Hutt, 2000; Cantwell et al., 2001).
Existing studies stress the relevance of the need for early intervention and enhancement of first-year student success because of the overwhelming influence of schooling and the challenges that the transition from school to university presents (Leibowitz et al., 2009; Tinto, 2003 and 1999; Yorke and Thomas, 2003). In addition, HEIs internationally are presently focusing on the intricacies of coping with the first-year experience at university (Yathavan, 2008). The student’s final examination mark earned at the end of the first-year of university is the single best predictor of student persistence after controlling for students’
entering characteristics (Pascarella and Terenzine, 2005). Borg and Stranahan (2002) suggest that a student’s course grade is usually the student’s first (and perhaps only) indicator of how successful the student is in a subject, and that the grade received usually determines whether the student chooses to continue in the study of the subject.
67 As the final examination marks have been extensively used as dependent variable for student success in various existing studies that applied the educational production function approach (Horn et al., 2011;
Cappellari et al., 2010; Tewari et al., 2008; Yathavan, 2008; Millar, 2006; Parker, 2006; Johnson and Kuennen, 2006; Krieg and Uyar, 2001; Edwards, 2000; Park and Kerr, 1990), this thesis follows a similar process and proceeds to use the students’ final examination marks achieved in first-year undergraduate accounting and economics modules in the FMS, as dependant variables.
Fourth, the grade or mark used is also measured on various scales, e.g. percentage grades (Woodfield et al., 2005); the ABCDF scale (Borg and Stranahan, 2002); the degree classification scale (first class, upper second class, lower second class, etc (Gammie et al., 2003); the GPA scale, which is a 7-point scale (Cantwell et al., 2001, Kahn et al., 2002), or the probability of getting a pass or fail grade (Park and Kerr, 1990).
The percentage scores of the students’ final examination marks on modules are available in this thesis’ data- bases. The grading scale at UKZN is from 0 to 100, with 50 and above considered a pass. If a student fails an examination in any course or module with a score between 40 and 49, UKZN grants him/her a supplementary examination. For progression purposes, UKZN overwrites the first-sitting marks with the one from the second sitting if the latter is higher than the former. Pass marks determine whether students will progress and major in these courses or modules in the second- or third-year of the university programme. Final examination marks at UKZN is, therefore, a continuous variable – in the range, say, 0 to 100 and it can take any value depending on the precision of measurement (the actual final examination marks achieved by a student). However, Park and Kerr (1990: 102) argue that final examination marks or course grades when used as the dependent variable have to be treated as a discrete variable (also referred to as categorical variable). This has implications for this thesis. The percentage final examination marks are used as the dependent variable in some models when they are continuous (ratio data) and take on any value in some interval of percentage value. In other models, final examination marks are converted into the probability of getting a pass (a final mark of at least 50) or a fail (a final mark of 49 and below) i.e. are converted to discrete variables (dichotomous or rank order) and take on only a finite number of values or countably infinite. Consolidating both continuous and discrete measurements is valuable for a holistic examination of student performance and for comparison purpose as is explained later in the regression analysis in Sub-section 3.1.5.
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