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Record W1767735416 · doi:10.24908/pceea.v0i0.4889

“THERMODYNAMICS IS NOT A SPECTATOR SPORT!” [7] AN EXPLORATORY STUDY ON INCORPORATING ACTIVE LEARNING INTO A FIRST YEAR THERMODYNAMICS COURSE

2013· article· en· W1767735416 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsActive learning (machine learning)Construct (python library)Mathematics educationPsychologyProcess (computing)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Student engagement is deemed as one of the fundamental components of consequential learning. Essentially, contextualizing information in authentic events and situations and designing active learning experiences to orchestrate opportunities for students to construct their own knowledge are ways to engage students in their own learning process. This paper reports on a Professor’s efforts to turn a traditional, lecture- based first year Thermodynamics course into an active learning arena. The study uses a qualitative approach to data collection and analysis, with data amassed through participant observations and open-ended interviews with the Professor. A number of categories and themes have already emerged. This paper outlines the category, Instructional Strategies, and discusses how they were modified and/or amplified to incorporate active learning in an attempt to further engage students.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.204
Teacher spread0.200 · 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