Exploring the Adoption and Usage of Learning Management System as Alternative for Curriculum Delivery in South African Higher Education Institutions during Covid-19 Lockdown
Bibliographic record
Abstract
This study adopted a discursive approach to review the use of the Learning Management System (LMS) popularly known as 'Moodle’ in most South African universities. Moodle as fondly called is one of the online tools that can be effectively used to deliver learning activities as well as online learning assessments to implement curriculum delivery without borders or disruptions during the COVID-19 lockdown across the globe. The author highlights various extant studies on the usage and adoption of LMS into teaching and learning for effective implementation in higher institutions of learning. LMS is a global online tool that has been in use for decades in various higher institutions of learning across the developed countries. It has proved to be an efficient learning platform that has impacted significantly the education sector in these developed countries, as learning experiences are provided to diverse students at their comfort zones. With the break of the COVID-19 pandemic that affects the global world, teaching and learning in most parts of the developed countries continue to be facilitated through various online approaches that include LMS in the developed countries. Unfortunately, the pandemic lockdown in many developing countries like South Africa grossly impacted the delivery of curriculum and educational activities in various learning institutions for several months. The continuous presence of COVID-19 led the South African Department of Higher Education and Training to review approaches to curriculum delivery to salvage academic activities. Thus, declared online learning as an alternative to face-to-face curriculum delivery in higher institutions. The adoption of LMS results as the best approach to engage students in full teaching and learning activities.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".