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Record W2937752884 · doi:10.21432/cjlt27795

Massive Open Online Course Instructor Motivations, Innovations, and Designs: Surveys, Interviews, and Course Reviews | Motivations, innovations et conceptions des instructeurs de cours en ligne ouverts à tous : sondages, entrevues et évaluations de cours

2019· article· en· W2937752884 on OpenAlex
Meina Zhu, Curtis J. Bonk, Annisa Ratna Sari

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.

fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2019
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
FundersRoyal Roads University
KeywordsLignePsychologyHumanitiesArt

Abstract

fetched live from OpenAlex

This mixed methods study explores instructor motivations for offering massive open online courses (MOOCs) as well as the instructional innovations used to enhance the MOOC design. The researchers surveyed 143 MOOC instructors worldwide and then interviewed 12 of these instructors via Zoom. They also extensively reviewed the MOOCs of the interviewees. The primary motivations for offering MOOCs included “growth” needs such as curiosity about MOOCs and the exploration of new ways of teaching. In addition, “relatedness” needs of instructors included reaching more people, showcasing research and teaching, marketing their university, integrating interactive technology, and obtaining peer reviews. The perceived instructional innovations of these MOOC instructors included using problem-based learning, service learning in MOOCs, and shortening the length of videos. Overall, these MOOC instructors were satisfied with their MOOC designs.Cette étude faisant appel à des méthodes mixtes explore les motivations des instructeurs de cours en ligne ouverts à tous ainsi que les innovations pédagogiques utilisées pour améliorer la conception de ces cours. Les chercheurs ont procédé au sondage de 143 instructeurs de cours en ligne ouverts à tous à travers le monde et ont ensuite interviewé 12 de ces instructeurs par l’entremise de Zoom. Ils ont également réalisé un examen approfondi des cours en ligne ouverts à tous des instructeurs interviewés. Les motivations principales pour l’offre de cours en ligne ouverts à tous comprenaient des besoins relatifs à la « croissance », comme la curiosité au sujet de ces cours et l’exploration de nouvelles façons d’enseigner. De plus, les désirs relationnels des instructeurs comprenaient joindre plus de gens, mettre en lumière la recherche et l’enseignement, publiciser leur université, intégrer la technologie interactive et obtenir des évaluations par les pairs. Les innovations pédagogiques perçues par ces instructeurs de cours en ligne ouverts à tous comprenaient l’utilisation de l’apprentissage par résolution de problèmes, de l’apprentissage par le service dans les cours en ligne ouverts à tous et la durée écourtée des vidéos. Dans l’ensemble, les instructeurs de cours en ligne ouverts à tous étaient satisfaits de leur conception de cours.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.074
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.331
Teacher spread0.294 · 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