What It Takes to Teach in a Fully Online Learning Environment: Provisional Views from a Developing Country
Bibliographic record
Abstract
Teachers and teaching play a critical role in the success of the online learning space. However, information about the specific strategies they employ in navigating this alternative learning space and the challenges they face remains limited. Thus, the present study was undertaken to obtain a clearer picture of teachers’ online instructional delivery and dig deeper into their difficulties for possible intervention. This study involved 17 teachers from nine higher education institutions in the Philippines. Using a descriptive case approach, overall data indicated that they promoted flexibility and interaction, facilitated learning processes, and fostered an affective learning climate as much as they could. However, these teachers faced several challenges related to technological sufficiency, learner-related factors, teaching delivery and assessment, technological complexity, and self-regulation, among others. Their varying experience was linked to their unique context brought about by several factors, namely available tools, institutional policies, pedagogical goals, and learner-related factors. Implications for classroom practices, policy-making, teacher training, and future research are discussed.
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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 | 0.002 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".