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Record W1913110846 · doi:10.24908/pceea.v0i0.5750

I flipped my tutorials: a case study of implementing active learning strategies in engineering

2015· article· en· W1913110846 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) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsMcGill University
FundersMcGill University
KeywordsFlipped classroomPaceVariety (cybernetics)Class (philosophy)Computer scienceMathematics educationActive learning (machine learning)MultimediaBlended learningEducational technologyPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

A variety of active learning strategies havebeen applied to engineering classrooms, includingflipping classrooms by recording lectures and havingstudents watch them outside of class time. In this study, asimilar approach was used for long-answer problemspresented in one-hour tutorial sessions. Problemsolutions were recorded and made available online.Instead of solving long-answer problems, tutorials beganwith a review of relevant material. The review was thenfollowed by independent working time where studentswere free to interact with the teaching assistant anddiscuss concepts with one another while working on anonline quiz.Students generally responded very positively tothe changes and appreciated the ability to go throughproblem solutions at their own pace with the recordings.In tutorials, the quizzes were successful at encouragingdiscussion of course content amongst students. Thetechniques also provided a repository of online videosand quizzes to be used in future course iterations.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.034
GPT teacher head0.343
Teacher spread0.310 · 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