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Record W2132083665 · doi:10.19173/irrodl.v8i3.400

Identifying Effective Pedagogical Approaches for Online Workplace Training: A case study of the South African wood products manufacturing sector

2007· article· en· W2132083665 on OpenAlexaffvenue
Iain Macdonald, Mark Bullen, Robert Kozak

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsBritish Columbia Institute of TechnologyUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Asynchronous communicationInclusion (mineral)PedagogyConstructivist teaching methodsTask (project management)Mathematics educationFace (sociological concept)Computer sciencePsychologyTeaching methodSociologyEngineeringGeography

Abstract

fetched live from OpenAlex

This study investigated appropriate pedagogical techniques for workplace e-learning programs in the South African wood products (furniture) manufacturing sector. The study found that learners responded favourably to constructivist teaching approaches, such as asynchronous discussions, open-ended task-based activities, and assignments incorporating authentic, real-world examples. Learners viewed constructivist activities to be more useful than quizzes and traditional essay-based assignments, as they allowed new concepts to be learned in context and were perceived to promote deeper understanding of the subject matter. Nevertheless, they valued the inclusion of a blend of pedagogical approaches in the course design, as this enabled them to approach and analyze new concepts in an assortment of ways. Learners placed importance on regular interaction with other learners and course instructors, and were strongly in favour of including face-to-face components in Web-based courses.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.378
GPT teacher head0.508
Teacher spread0.130 · 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 designQualitative
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

Citations6
Published2007
Admission routes2
Has abstractyes

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