The impact of student unrest on freshmen engineering students in South Africa
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».