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Abstract Factors that may affect or predict a student’s performance in his degree program were investigated using a sample of 236 students taking up Management degree programs in various areas of specialization. Statistical tests and mathematical tools such as frequency count, descriptive statistics, correlation, chi square analysis, t test, and regression analysis were used to identify existing relationships and possible predictors of overall performance…
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Essay 2
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Download file to see previous pages Independent variables were region, age, and gender. Region referred to the student’s place of study, and took the values of EU, OS, and UK. Age was divided into two values: regular (below 21 years old as of 1st September at the year of intake) and mature (below 21 years old as of 1st September at the year of intake). Gender took the values of Male and Female. Frequency count was used to find the number of respondents per category. In finding the relationship between continuous variables (e. g. scores), Pearson’s correlation techniques were used. In finding the relationship between categorical variables, cross tabulation and chi square tests were performed on the data. To investigate the effects of prior maths education on unit scores and overall performance, t test were conducted. Finally, to find out which variables predicted overall performance, regression analysis was performed. Presentation and Discussion of Results Demographic profile of respondents There were a total of 236 respondents surveyed for this study. Table 1 shows the country of origin of the respondents. Figures indicate that majority if the respondents are from the UK (36.4%), China (25.4%), and India (8.5%). In terms of the Program that the respondents are taking up, 36.4% are majoring in MSc (Hons) Management (n = 88), 26.3% in Marketing (n = 62), 16.1% in Accounting (n = 38), 12.3% in IBE (n = 29), 3.8% in Human Resources (n = 9), and 3.4% in IS (n = 8). There are 2 respondents each majoring in Decision Making and in Operations. Of the respondents surveyed, 53.8% are from the OS region (n = 127), 36.4% are from the UK region (n = 86), and 9.7% are from the EU region (n = 23). There were 87.3% of respondents who were considered regular students (n = 206), while 12.7% are mature students (n = 30). There were more female respondents in this study at 53.4% (n = 126) compared with male respondents at 46.6% (n = 110). Descriptive Statistics The primary interest of this study is the overall performance of students and the underlying factors that may predict overall performance. As such, it would be helpful to look into the descriptive characteristics of overall performance scores and the individual unit course scores (Anderson, Sweeney, & Williams, 2009). Figure 1 shows that the overall performance scores are skewed to the left, with higher concentration on the 50 to 70 range (Mean = 56.88, SD = 11.7). Table 2 reflects the mean scores of respondents in the different units. The figures indicate that students have the highest mean score in BMAN10001 (10) – Economic Principles: Microeconomics (Mean = 70.02, SD = 14.77) and the lowest mean scores in BMAN10621 (10) – Fundamentals of Financial Reporting. Table 1. Histogram of overall performance scores. Relationship between unit courses and overall performance Overall performance was measured by taking the average of a student’s unit courses. It is helpful to find out in this investigation which unit course affects overall performance most. Table 3 shows the correlation coefficients, Pearson’s r, between the unit courses and overall performance. The figures show that overall performan ...Download file to see next pagesRead More
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