Predictors of Online Learning Readiness and Their Consequences on Learning Engagement and Perceived Teaching Quality during Covid19
Bibliographic record
Abstract
This empirical study attempts to investigate the causal relationships between the predictors of online learning readiness and its effects on learning engagement and perceived teaching quality. In other words, the study aims to explore the direct relationship between goal orientation and perceived teaching quality, on the one hand, and the students’ learning engagement and perceived teaching quality and their indirect relationships via online learning readiness. A total of 703 students from Malaysian and Omani higher institutions voluntarily participated in this study following the quota sampling technique. Structural Equation Modeling (SEM) was used to analyze the data gathered. The results of the analysis suggested that the goal orientation and perceived self-efficacy were statistically and directly related to learning engagement and perceived teaching quality and indirectly via online learning readiness.Furthermore, the analysis showed that goal orientation has a direct positive and significant relationship with learning engagement and perceived teaching quality and positive indirect relationships with them via online learning readiness. However, while perceived self-efficacy had a direct positive correlation with learning engagement, it had a negative and direct relation with perceived teaching quality but a positive indirect relationship via online learning readiness. Hence, due to the ongoing covid19 global pandemic, this study implicates that highlighting the roles of goal and efficacy in an online context is essential because they would affect students’ learning engagement and their evaluation of teaching quality.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".