Whither the LMS: Is the LMS Still Fit for Purpose?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".