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Record W2885425310 · doi:10.24908/pceea.v0i0.9738

INTRODUCTION OF AN ACTIVE LEARNING COMPONENT THROUGH RESEARCH REPORTS IN A LABORATORY COURSE

2018· article· en· W2885425310 on OpenAlexaffvenueabout
Juan Abelló, Paul Labossière

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsComponent (thermodynamics)Active learning (machine learning)Course (navigation)TeamworkLifelong learningMathematics educationComputer scienceMedical educationPsychologyEngineeringPedagogyArtificial intelligenceMedicineManagement

Abstract

fetched live from OpenAlex

Abstract –Mechanical Engineering Laboratories is a third-year course at the University of Manitoba that covers the application of mechanical engineering principles to relevant experimental problems. The course spans two terms, counts for two credit hours per term and traditionally required student teams to prepare formal laboratory reports only.
 An active learning component was introduced by assigning students to write research reports on topics that would otherwise have been covered in the lectures. This allowed us to enable additional laboratory groups during former lecture slots and accommodate the program’s enrollment increase from 80 to 120 students. Introducing research reports also allowed students to learn independently, research beyond the level of detail in the course if they wished, and practice their lifelong learning skills along with teamwork skills.
 Most students did well in this component of the course. Student feedback comments on the research reports were mostly positive. This experience suggests that an active learning component through research reports in lieu of some lectures may be a useful active learning tool in an upper-level laboratory course.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2018
Admission routes3
Has abstractyes

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