MétaCan
Menu
Back to cohort
Record W2749934332 · doi:10.1117/12.2266663

Engaging colleagues in active learning pedagogies through mentoring and co-design

2017· article· en· W2749934332 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue14th Conference on Education and Training in Optics and Photonics: ETOP 2017 · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsVanier College
Fundersnot available
KeywordsActive learning (machine learning)Student engagementPedagogyPsychologyExperiential learningMathematics educationComputer science

Abstract

fetched live from OpenAlex

When implemented correctly, active learning pedagogies increase student engagement with discipline content. In addition, there is accumulating evidence that they also positively impact the learning of this content. This is particularly relevant for teaching science disciplines because many students perceive science as being difficult to fully understand. However, an ongoing problem is that instructors have difficulty implementing active learning pedagogies effectively and therefore see no benefit to it. Without persistence or guidance, instructors can become discouraged and return to a more traditional style of teaching. We report on how the Faculty of Science at Vanier College is getting more instructors to engage in active learning pedagogies through mentoring and activity co-design.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.286
GPT teacher head0.491
Teacher spread0.205 · 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