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Record W4405680022 · doi:10.24908/pceea.2024.18526

Enhancing Undergraduate Engineering Education: Collaborative and Reflective Practices in Professional Learning Programs for Instructors and GTAs

2024· article· en· W4405680022 on OpenAlexaffvenueabout
Gökçe Akçayır, Xiong Wang, Qingna Jin, Kerry Rose, Kristian Basaraba, D Buchanan, Marnie Jamieson, Mijung Kim, P. Janelle McFeetors

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsCape Breton UniversityUniversity of Alberta
Fundersnot available
KeywordsMedical educationEngineering educationEngineering ethicsPsychologyPedagogyEngineering managementComputer scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

This study focuses on a professional learning program designed to enhance pedagogical practices for engineering educators at a Canadian university. Rooted in situated learning theory, the program encompasses key themes such as the philosophy of teaching and learning, fostering learning opportunities, designing course for learning, and the scholarship of teaching and learning. Throughout the program, participants, including professors and graduate teaching assistants (GTAs), engaged in understanding student-centered pedagogies, reflective practices, and collaborative learning within Communities of Practice (CoPs). Employing a case study approach, we investigate participants’ learning experiences with the program. The outcomes of this study reveal participants’ emphasis on student-centered pedagogies, recognition of the value of CoPs and instructional coaches, reflection on pedagogical change, and the challenges and tensions encountered during program engagement.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0070.003
Open science0.0030.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.257
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes3
Has abstractyes

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