Curriculum Review Process at the School of Mining Engineering at the University of the Witwatersrand
Bibliographic record
Abstract
The School of Mining Engineering (Wits Mining) at the University of the Witwatersrand (Wits) has a long history of Mining Engineering education, being the oldest and largest on the African continent. In 2016, the School celebrated 120 years in existence and according to the recent QS University Rankings, it is recognized as one of the world’s top mining engineering schools, hosting an expansive program. It also has one of the highest growth rates of any of the engineering schools or departments, having seen a consistent increase in students to its program. (1) Need for re-curriculation: With mines in South Africa going deeper as shallow Mineral Resources are depleted, the challenges facing the industry today are substantial. However, best-practice innovations and technology offer the opportunity for the design and management of high-tech mines that are not only safer, but also more productive and environmentally and socially responsible, while still being economically successful. Feedback from industry experts and alumni continuously alluded to revising the existing BSc (Mining) curriculum in order to cater for the needs of an innovative and technology driven mining industry. The School hence decided to go through a comprehensive 2 day curriculum review workshop which hosted academic staff and industry experts from several engineering streams. (2) Finding: The future mining engineer should encompass skills and knowledge in 4 broad streams namely: Basics of Science and Mathematics, relevant core technical skills, operational management and a socio-economic understanding. (3) Aim: The School’s new Strategic Plan and new technology driven curriculum will ensure s that the Wits Mining Team can deliver Excellence in Teaching, Research and Service – in line with the Wits Vision 2022 of being “a leading research-intensive university firmly embedded in the Top 100 world universities by 2022”. This paper reflects on the process that was undertaken for this review and comment on the final outcome that was attained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".