Guidelines towards the facilitation of interactive online learning programmes in higher education
Bibliographic record
Abstract
The creation of online platforms that establish new learning environments has led to the proliferation of institutions offering online learning programmes. However, the use of technologies for teaching and learning requires sound content specialization, as well as grounding in pedagogy. While gains made by constructivism and observational learning are well documented, research addressing online practices that best encourage constructivist and observational learning in Open and Distance Learning (ODL) contexts is limited. Using a phenomenological methodological approach, this research explored the lived experiences of online learning programme facilitators at an Open and Distance Learning higher education institution. The findings of this research study revealed that facilitators did not use constructivist and observational learning pedagogies to a large extent in their interaction with students. It is concluded that during the curriculum planning phase, facilitators should decide on methods and media to arouse the students’ attention and stimulating constructivist and observational learning amongst students during online courses. This also implies a more reasonable facilitator-student ratio because large numbers of students per facilitator proves not feasible in online learning. The paper concludes by providing guidelines for the facilitation of interactive online learning programmes.
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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 | 0.104 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".