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Record W2739347400 · doi:10.56059/jl4d.v4i2.217

MOOCifying Courses: Delivery of a MOOC to Enhance University Course Activities

2017· article· en· W2739347400 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

VenueJournal of Learning for Development · 2017
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsAthabasca University
Fundersnot available
KeywordsComputer scienceSpeculationContent deliveryOnline learningHigher educationCourse (navigation)Mathematics educationMultimediaEngineeringPsychologyPolitical science

Abstract

fetched live from OpenAlex

Since 2012 MOOCs have been heralded as a new way of learning outside of formal university programs of study and there has been much speculation regarding their impact. While MOOCs have provided millions of global learners with access to courses, they failed to deliver the types of learning experiences and completion requirements that were hoped for. One potential iteration of MOOCs might be to blend them with existing courses offered in universities supporting links and connections between study and the outside world. This MOOCification of full-fee courses may provide another next step in the delivery of real and authentic learning. Using an empirical case study design, this project explored the MOOCification of an undergraduate preservice education course at an Australian university. The study presents evidence that blending MOOCs with classroom-based or online learning does provide higher education learners with personalized active learning opportunities. Further research on scaffolded support enabling learners to capitalize on additional aspects of networked learning in MOOCs is needed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.310
Teacher spread0.289 · 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