MétaCan
Menu
Back to cohort
Record W4385952944 · doi:10.53761/1.20.6.13

Whither the LMS: Is the LMS Still Fit for Purpose?

2023· article· en· W4385952944 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueJournal of University Teaching and Learning Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
FundersMonash UniversityDeakin University
KeywordsThematic analysisDelphi methodLearning ManagementHigher educationFocus groupQualitative researchPedagogyTeaching methodPsychologyMedical educationSociologyMathematics educationComputer sciencePolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Learning management systems (LMSs) have long been adopted by tertiary education providers to be the conduit through which courses are delivered. However, debates about the capacity of the LMS to meet all the required current and future needs of both students and educators have become more pronounced over the past few years, particularly given the rapid shift to online learning during Covid-19. This qualitative study aimed to examine practitioners’ current experiences in using the LMS for formal teaching and learning in tertiary environments. To discern the possibilities and issues, a focus group was held with fourteen practitioners from Australasia (Australia and Singapore), Canada, and the UK (England and Scotland) attending virtually. Adopting a novel and recognised approach to thematic analysis, a Delphi process was adopted on the de-identified webinar and chat transcripts. Analysis revealed several key themes ranging across pedagogical, technological, and managerial issues with the LMS. The findings in this paper have become even more pertinent as a result of Covid-19 with institutions urgently reviewing standards for teaching in the LMS whilst also reviewing their overall technology ecosystems to ensure a suite of complementary teaching and learning tools to enable best teaching and learning practices. It appears the LMS still has a key role to play in contemporary learning ecosystems.

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)0.011
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.362
Teacher spread0.299 · 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