The impact of student unrest on freshmen engineering students in South Africa
Bibliographic record
Abstract
In 2016 alone, Google Scholar listed 301 results for “student unrest”. These results listed countries such as South Africa, Nigeria, India, Canada and the USA. The devastating effects of student unrest on student well-being, academic performance and career paths have been documented. Added to this is the disastrous financial consequences that institutions of higher education must endure. The purpose of this study is to draw together the consequences of student unrest, highlighting those that specifically impacted negatively on freshmen engineering students in South Africa during 2016. The academic year in South Africa is divided into two semesters, with approximately 260 students registering for Electronics 1 in the first semester and 130 registering in the second semester. The same course is offered in both semesters. During the first semester of 2016, no student unrest was experienced. However, in the second semester, just after 8 weeks of instruction had passed, student unrest flared up. This occurred at a critical time in the semester, as students were preparing to complete their main test which contributes significantly to their final grade at the end of the module. A descriptive case study is used with descriptive statistics of the quantitative and qualitative data. Quantitative data contrasts the final academic grades of Electronic 1 students in 2016, where the second semester students were affected by student unrest. Students from this second semester were also asked to complete an online questionnaire which sought to obtain their perspectives of the unrest. Results indicate that students from Semester 1 (NO student unrest) enjoyed a 10% higher median grade and 26% higher pass rate for their exam as compared to students from Semester 2 (YES student unrest). It must be emphasized that all these students were exposed to the same course content, academic and assessments. Furthermore, student perspectives primarily highlighted that their study routine was negatively affected by the student unrest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".