Analysis of Positive Emotional Effects on Student Learning Involvement, Social Influence, and Effort Expectations Factors on Academic Achievement and Use of e-Learning Systems
Bibliographic record
Abstract
The Covid-19 pandemic has had a significant impact on student learning. Previous research found that the pandemic had exacerbated student stress and anxiety in several countries, including Malaysia, the United States, and Canada. There has been no previous research that looks at how positive emotion (PE), student learning engagement (SLE), social influence (SI), effort expectancy (EE), and academic motivation (AM) influence academic motivation and actual use (AU) of a learning management system, so this quantitative research was conducted to investigate these factors. Structural Equation Model and Partial Lease Square (SEM PLS) techniques were used in this study. And found five factors that have no influence and four factors that have influence. Some of the findings from this study are different from previous studies. The results of this study are very useful for the development of a learning management system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".