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Record W4366609374 · doi:10.21432/cjlt28154

Student Perceptions of the Visual Design of Learning Management Systems

2023· article· en· W4366609374 on OpenAlexaffvenue
Brenda M. Stoesz, Mehdi Niknam

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLearning ManagementPerceptionPsychologyVisual learningMathematics educationHigher educationEducational technologyE learningInstructional designLikert scaleMultimediaComputer science

Abstract

fetched live from OpenAlex

Research on the impact of the visual design of the user interface of learning management systems (LMS) on learning experience is sparse. The purpose of this study was to conduct a preliminary examination of students’ perceptions of the visual design of their postsecondary institutions’ LMS and their learning experiences using survey methodology (N= 46). Students generally agreed that the course homepages were well organized and that the LMS colours, while deemed moderately to very important, did not enhance learning or increase the ability to remember course content. However, more positive perceptions of the visual appearance of the LMS were associated with greater satisfaction with grades. Expected end of term grade point average was negatively correlated with the degree to which students perceived that colour enhanced their learning. Students reported a greater satisfaction with the contribution of the LMS to learning correlated to the number of school terms they had used an LMS, their LMS proficiency, and their perceptions about the visual appeal of the LMS design. Together, these results suggest that exploring the impact of LMS colour and other dimensions of visual design on student engagement and learning are important and have practical value for LMS developers, instructional designers, and instructors.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.322
Teacher spread0.303 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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