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Record W4381485768 · doi:10.55982/openpraxis.15.1.516

Reusing Distance Courseware to Enable Blended Delivery: A New Zealand Case Study

2023· article· en· W4381485768 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

VenueOpen Praxis · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsYork University
Fundersnot available
KeywordsDistance educationAsynchronous communicationReuseComputer scienceBlended learningInterpersonal communicationPopulationMultimediaQuality (philosophy)Educational technologyMathematics educationEngineeringSociologyPsychologyTelecommunications

Abstract

fetched live from OpenAlex

Digital distance course materials can be used across different forms of education delivery. In particular, courseware designed for asynchronous digital distance education can serve as the basis for blended learning, which features a different teaching role and fuller interpersonal experience. Blended learning can be used to extend programme opportunities across population regions where a full, lecture-based model might not be viable. This case study explores the experiences of three regional polytechnics in New Zealand that adopted and modified courseware created for digital distance learners studying asynchronously. The courseware was used to provide local students with more flexible study options, drawing on high quality courseware that had been centrally created by a team of experienced courseware designers and Subject Matter Experts (SMEs).

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.051
GPT teacher head0.389
Teacher spread0.339 · 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