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Record W4313049822 · doi:10.24908/pceea.vi0.14844

SITUATED LEARNING PERSPECTIVE FOR ONLINE APPROACHES TO LABORATORY AND PROJECT WORK

2021· article· en· W4313049822 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsCarleton University
Fundersnot available
KeywordsSituatedExperiential learningSituated learningLearning cyclePerspective (graphical)MediationComputer scienceMathematics educationPsychologySociologyArtificial intelligence

Abstract

fetched live from OpenAlex

The engineering laboratory in a university is where most students feel they are entering a place where engineering is done and practiced. With the COVID-19 pandemic many Canadian universities closed theircampuses and teaching moved online. This closed off access to laboratories and their rich range of resourcesand experiences to students. We use a situated learning framework to consider the online lab. To furtherreinforce the examination of the online approach we used the Kolb experiential learning cycle to complement the situated framework. Using examples from our own courses we look at the three areas of situated learning,activities and interactions, mediation, and participation and identity. The examination shows the insight fromusing this framework and learning cycle can be valuable, including insight into the stages of learning and howthose stages can be developed.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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 head0.038
GPT teacher head0.265
Teacher spread0.228 · 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