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Record W7066430710

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

2019· article· en· W7066430710 on OpenAlexaboutno aff

Bibliographic record

VenueeCommons - AKU (Aga Khan University) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)InstitutionalisationIndigenousFaculty developmentProfessional developmentTeaching methodCommunity of practice
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.265
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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