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Record W4412870701 · doi:10.24908/pceea.2025.19652

Active Learning Strategies in a First-Year Engineering Materials Course

2025· article· en· W4412870701 on OpenAlex
Bronwyn Chorlton

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCourse (navigation)Active learning (machine learning)Mathematics educationEngineeringComputer sciencePsychologyArtificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

Active learning approaches in engineering education have been gaining popularity in recent years. This method of engaging students in the learning process has been shown to improve student outcomes during assessment and narrow the achievement gap for racialized students. However, active learning changes the typical classroom structure from the traditional lecturing approach and so there is a need to consider how these relatively contemporary approaches are affecting different groups of students. This research aims to understand the effects of active learning on the diverse populations of first-year engineering students. The first phase of this study will be presented herein, which will include a reflection on the active learning methodologies used in a first-year common-core materials engineering course, both during lectures and longer active learning sessions. A variety of active learning strategies used will be examined for their accessibility benefits and challenges.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.804
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.287
Teacher spread0.279 · 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