MétaCan
Menu
Back to cohort
Record W91654530 · doi:10.21061/jcte.v17i1.589

Competency-Based Versus Traditional Cohort-Based Technical Education: A Comparison of Students' Perceptions

2000· article· en· W91654530 on OpenAlexaffabout
Jill Sinclair Bell, Robin Mitchell

Bibliographic record

VenueJournal of Career and Technical Education · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsYork University
Fundersnot available
KeywordsCurriculumPsychologyCohortMathematics educationMedical educationVocational educationPerceptionApprenticeshipPatiencePedagogyMedicineSocial psychology

Abstract

fetched live from OpenAlex

As part of a participant-observation study investigating technical education in Canada, students enrolled in pre-apprenticeship refrigeration mechanics courses at the community college level were interviewed. The responses of students enrolled in a 1-year, competency-based program were compared with the responses of students enrolled in a 36-week, traditionally-delivered, cohort-based program. The results suggest that the different curricula lead to different student experiences of the content. Most notable was a distinctly perceived split between the "theory" and the "practical" aspects of refrigeration mechanics by students in the traditional cohort-based program, whereas students in the competency-based program did not seem to perceive theory and practice as 2 distinct entities. Additionally, although students in both samples described histories of language and literacy difficulties, the competency-based program participants seemed less adversely affected by these weaknesses. However, students in both types of programs viewed patience and supportiveness as crucial aspects of good teachers.

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 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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.379
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations23
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Career and Technical EducationSame topicAdult and Continuing Education TopicsFrench-language works237,207