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Record W2741988790 · doi:10.1080/02680513.2017.1357465

Facilitating attitudinal learning in an animal behaviour and welfare MOOC

2017· article· en· W2741988790 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.

fundA Canadian funder is recorded on the work.
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

VenueOpen Learning The Journal of Open Distance and e-Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsFacilitationPsychologyPerceptionMassive open online courseOnline learningMedical educationKnowledge managementPedagogyApplied psychologyMultimediaComputer scienceMedicine

Abstract

fetched live from OpenAlex

This case study examines the design and facilitation of a Massive Open Online Course (MOOC) that focused on attitudinal learning about the topic of animal behaviour and welfare. Findings showed that a team of instructors worked together collaboratively towards realising learning goals and found the experience rewarding. While learners had mixed perceptions of gains in cognitive, affective and behavioural learning, they reported high satisfaction with lecture videos and instructor course participation. Implications for the instructional design of MOOCs and attitudinal learning are discussed based on these findings, including a discussion of MOOCs as a unique platform for attitudinal learning, and recommendations for their successful use. The recommendations include the importance of creating a collaborative instructor team, establishing high instructor presence, using interactive and collaborative learning activities, and receiving support from platform providers and institutions.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0060.005
Open science0.0040.003
Research integrity0.0000.003
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 head0.353
Teacher spread0.317 · 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