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Record W2093030283 · doi:10.1177/000841740507200303

A Web-Based Tutorial to Enhance Student Learning of Activity Analysis

2005· article· en· W2093030283 on OpenAlexaffvenueabout
Cynthia Perlman, Cynthia Weston, Erika G. Gisel

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsSummative assessmentComputer sciencePsychologyMathematics educationMultimediaFormative assessment

Abstract

fetched live from OpenAlex

BACKGROUND: This paper describes the impact on learning of a web-based tutorial for the application of activity analysis, with occupational therapy students, at McGill University, Montreal, Quebec. This tutorial offers unique, interactive instructional strategies allowing for self-directed higher cognitive and reflective learning, which has not been possible, to date, in current web-based technology. SCOPE: Through repeated practice, students collaboratively (in dyads) apply concepts of activity analysis, and receive immediate feedback by comparing their answers to a standard. METHODS AND RESULTS: Comparisons of performance outcomes on summative exams before and after implementation of the tutorial are made, suggesting a decrease in variance scores indicating fewer students are falling below the class average. These results are attributed to opportunities to practice activity analysis and to receive immediate feedback. PRACTICE IMPLICATIONS: Web-based instruction can impact student learning if the instructional strategies ensure coherence with all other instructional components, match the learning outcomes, facilitate self-directed and collaborative learning, and allow for practice and feedback.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0200.004

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 head0.171
GPT teacher head0.540
Teacher spread0.369 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designOther design · Observational
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

Citations22
Published2005
Admission routes3
Has abstractyes

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