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Record W3116169956 · doi:10.21432/cjlt27899

Defining the Visual Complexity of Learning Management Systems Using Image Metrics and Subjective Ratings

2020· article· en· W3116169956 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2020
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLearning ManagementPerceptionVisual learningComputer sciencePsychologyVisual perceptionComplexity managementCognitive psychologyMultimedia

Abstract

fetched live from OpenAlex

Research has demonstrated that students’ learning outcomes and motivation to learn are influenced by the visual design of learning technologies (e.g., learning management systems or LMS). One aspect of LMS design that has not been thoroughly investigated is visual complexity. In two experiments, postsecondary students rated the visual complexity of images of LMS after exposure durations of 50-500 ms. Perceptions of complexity were positively correlated across timed conditions and working memory capacity was associated with complexity ratings. Low-level image metrics were also found to predict perceptions of the LMS complexity. Results demonstrate the importance of the visual complexity of learning technologies and suggest that additional research on the impact of LMS design on learning outcomes is warranted.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.315
Teacher spread0.280 · 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