Benchmarking the habits and behaviours of successful students: A case study of academic-business collaboration
Bibliographic record
Abstract
<p>Student success and retention is a primary goal of higher education institutions across the world. The cost of student failure and dropout in higher education is multifaceted including, amongst other things, the loss of revenue, prestige, and stakeholder trust for both institutions and students. Interventions to address this are complex and varied. While the dominant thrust has been to investigate academic and non-academic risk factors thus applying a “risk” lens, equal attention should be given to exploring the characteristics of successful students which expands the focus to include “requirements for success”.</p><p>Based on a socio-critical model for understanding of student success and retention, the University of South Africa (Unisa) initiated a pilot project to benchmark successful students’ habits and behaviours using a tool employed in business settings, namely Shadowmatch®.</p><p>The original focus was on finding a theoretically valid measured for habits and behaviours to examine the critical aspect of student agency in the social critical model. Although this was not the focus of the pilot, concerns regarding using a commercial tool in an academic setting overshadowed the process. This paper provides insights into how academic-business collaboration could allow an institution to be more dynamic and flexible in supporting its student population.</p>
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".