Learner Engagement and Satisfaction in the Online Mathematics Course: The Experience of a Private Philippine University
Bibliographic record
Abstract
Institutions of higher learning had to adopt a flexible learning delivery due to the threat of the global health crisis in 2020. Taking advantage of the technological affordances, many universities and colleges implemented the online learning modality. However, teachers and students found themselves overwhelmed with issues of quality assurance and outcomes of online teaching and learning. In this descriptive research study, the university students’ feedback on their engagement and satisfaction in the online mathematics courses was analyzed to get a perspective on successful online learning implementation. The mediation analysis on the responses of 512 university students on a 35-item researcher-made questionnaire showed that the university students were engaged and satisfied with their online mathematics courses. The design factor had a significant effect on learner engagement and satisfaction. The human factor has a significant impact on learner engagement but no significant effect on learner satisfaction. The structural equation model further revealed that learner engagement fully mediates the relationship between human factor and learner satisfaction while partially mediating the relationship between design and learner satisfaction. The results strongly assert the need for efficient and effective instructor knowledge and facilitation, more significant class interaction, and engaging use of technology in online mathematics courses to increase learner satisfaction.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".