MétaCan
Menu
Back to cohort
Record W2992164681 · doi:10.5555/1088791.1088826

Kinesthetic learning in the classroom

2005· article· en· W2992164681 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.

Bibliographic record

VenueCornerstone (Minnesota State University, Mankato) · 2005
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKinesthetic learningSession (web analytics)Computer scienceThrowingMultimediaVariety (cybernetics)Mathematics educationHuman–computer interactionWorld Wide WebPsychologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

We present a tutorial (based on [1]) focusing on kinesthetic learning activities, i.e., physically engaging classroom exercises. These might, for example, involve throwing a frisbee around the classroom to represent transfer of control in a procedure call, or simulating polygon scan conversion with rope for edges and students for pixels. The session begins with a brief kinesthetic learning activity to motivate the value of these activities. We follow with a variety of examples, and discuss how to use these successfully in the classroom. The audience then divides into facilitated groups to design their own activities. Finally, we all mingle to share and discuss the results. These results are posted on a public web forum---the KLA wiki [2]---for continued discussion and generation of new ideas.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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