Implementing a Teaching and Learning Enhancement Workshop at Aga Khan University: Reflections onthe implementation and outcomes of an Instructional Skills Workshop in the context of Pakistan
Bibliographic record
Abstract
The Teaching and Learning Enhancement Workshop (TLEW) is an indigenous name for the Canadian-based Instructional Skills Workshop (ISW). TLEW is a teaching development workshop aimed at enhancing faculty members’ stances towards student-centred teaching and reflective practice at the higher education level. This short paper discusses the initiation, implementation and institutionalisation of the TLEW at Aga Khan University (AKU) across entities in Asia and Africa. In total, 77 faculty members drawn from different entities of AKU participated in the workshop in 2016-2017. Empirical evidence collected from TLEW graduates through a survey and interviews suggests that the intense episode of planning, teaching and receiving peer feedback during TLEW helped participants in sensitising them to effective planning for teaching in order to engage and enrich students’ learning. Furthermore, the repertoire of pedagogical strategies has permeated graduates’ classrooms. Nevertheless, for sustainability a mechanism needs to be in place for providing faculty with institutional support and recognition for their contribution in teaching and learning. A need is advocated for TLEW to evolve as a mandatory component for all teaching staff at the university to help serve as a fundamental base for initiating and sustaining change through ongoing professional development opportunities and establishing a community of practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".