Predictors for Minimum Time Completion in BHS Program at Wits University
This study investigates predictors of minimum time completion for Bachelor of Health Sciences students at Wits University, focusing on NBT domains and NSC subjects. A sample of 162 students from five cohorts was analyzed using hierarchical multiple regression to determine the variables influencing grade performance averages across three study years.
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Presentation Transcript
Predictors for minimum time completion for the Bachelor of Health Sciences students at Wits University
1. Background Information The Bachelor of Health Sciences (BHSs) offered for the first time in 2005, was design for students who intends to pursue various careers in the field of health In order to be admitted into the BHSs programme students needed to have passed their examination on the National Senior Certificate (NSC). Students need to have obtained level 5 in Mathematics, English, Physical sciences or Life Sciences on the National Senior Certificate Students will additionally be required to write the National Benchmark Test (NBT)
Cont. A composite index is calculated using five NSC subjects, NBT scores. The matriculation score is calculated using five subject which are; Mathematics, English, Physical sciences or Life sciences and the best two subjects
2. National Benchmark Test (NBT) The NBT was implemented by the University of South Africa in order to measure the academic preparedness of high school leaving learners who wanted university admission.
2.1 Three domains of the NBT The NBT was designed to asses student s cognitive knowledge in three domains: NBT academic literacy (NBTAL) NBT quantitative literacy (NBTQL) and NBT mathematics(NBTMA)
2.2 Students performance on the NBT Student s performance on the NBT test are categorised into three levels of proficiency Proficiency Intermediate Basic
3. Aim of the study The aims of this study was to identify variables which predict minimum time throughput in the first, second and third year of study
4. Methodology Sample size =162 students (completed in minimum time compmletion) Five cohorts selected (2012-2015) A hierarchical multiple regression (HMR) was used to explore the proportion of variance accounted for by the three NBT domains and four NSC subjects (English, Mathematics, Physical and Life Sciences) The outcome variable was the Grade Performance Average (GPA) of students in all three years of study
5. Study Findings Year one results In year one, The NBT domains accounted 14% of the variance with the R2 140, F (3, 158) =8.605, p <.000 The four NSC subject explained 27% of the variance when added to the regression equation with an R2= .267, F (4, 154) =17.101, p <.000 All predicting variables added together account for 40% of the variance with an R2 = .407, F (7, 154) =15.101, p <.000 The table below indicates that the NBT Academic literacy, Mathematics and Physical sciences were statistically significant in predicting success in year one
Table 1 Variables NBT Mathematics B [95% CI] Sig .000*** .140 [.071, .210] .340 NBT Academic Literacy .307 1 .052 [-.049, .153] .087 NBT Quantitative Literacy .972 .001 [-.080, .083] .003 NBT Mathematics .862 -.006 [-.078, .065] -.015 NBT Academic Literacy .012** .119 [.026, .211] .196 NBT Quantitative Literacy .863 -.006 [-.076, .064] -.014 English .231 2 .081 [-.052, .213] .087 Maths .008*** .187 [.050, .324] .267 Life Science .186 .120 [-.058, .298] .107 Physical Science .002*** .206 [.080, .333] .314
Cont. Year two results In year two, The NBT domains accounted 12% of the variance with the R2 119, F (3, 154) =7.087, p <.000 The four NSC subject accounted 13% of the variance when added to the regression equation with an R2= .131, F (4, 154) =6,702 p <.000 All independent variables added together explained 25% of the variance with an R2 = .249, F (7, 154) = 7.305, p <.000 The table indicates that the NBT Academic literacy was the only statistically significant variable in predicting success in year two
Table 2 Variables sig B [95% CI] NBTMA .170 [.082, .258] .328 .000*** NBTAL 1 .104 [-.025, .232] .136 .113 NBTQL -.046 [-.150, .057] -.084 .378 NBTMA .071 [-.031, .172] .136 .171 NBTAL .137 [.006, .268] .180 .041* NBTQL -.059 [-.158, .041] -.106 .246 English 2 .149 [-.039, .336] .128 .119 Maths .094 [-.100, .288] .107 .340 Life Sciences .230 [-.022, .482] .163 .073 Physical Sciences .124 [-.054, .303] .151 .171
Cont. Year three results In year three, The NBT domains accounted 7% of the variance with the R2 .07, F (3, 154) = 3.982, p <.000 The four NSC subject explained 16% of the variance when added to the regression equation with an R2= .163, F (4, 154) =8.200, p <.000 All predicting variables added together account for 23% of the variance with an R2 = .234, F (7, 154) =6.704, p <.000 The table indicates that the NBT Academic literacy, English and Physical sciences were statistically significant in predicting success in year three
Table 3 Model Variables NBT Mathematics Sig B [95% CI] .061 [-.013, .135] .144 .104 NBT Academic Literacy 1 .136 [.028, .244] .219 .014** NBT Quantitative Literacy -.023 [-.109, .064] -.050 .606 NBT Mathematics -.014 [-.098, .069] -.034 .735 NBT Academic Literacy .135 [.026, .243] .217 .015** NBT Quantitative Literacy -.020 [-.102, .062] -.044 .636 English 2 .242 [.088, .396] .257 .002*** Maths -.011 [-.171, .149] -.016 .889 Life Sciences .067 [-.141, .274] .058 .525 Physical Sciences .184 [.037, .331] .274 .014**
Conclusion It is important for universities to not only use the NBT for selection and placement of students. Universities should also make it a priority to provide appropriate and sufficient support, especially to students who are admitted with low entry skills. Most students that enter higher education with low entry skills were affected by the quality of education that they received in high school. The academic performance of such students will highly be affected if the university is not ready for such students.
