Students' perceptions of the positive impact of learning with bogs: an investigation of influencing, moderating, and mediating factors.
Bibliographic record
Abstract
This paper discusses students’ perceptions of the positive impact of learning with blogs in higher educational settings. The research model uses constructs from the social-cognitive theory and motivational model. Relevant hypotheses focusing on direct, moderating, and mediating influences were formulated. A survey was administered to 108 university students taking an undergraduatelevel course in management information systems in a Canadian university. Data analysis was done with the partial least squares structural equation modeling technique. The result shows that students’ perceived usefulness of blogs was not positively associated with their perceptions of the positive impact of using the tool to learn; however, perceived self-efficacy and enjoyment were found to be positively associated with positive impact of learning with blogs. With regard to mediation, the results show that perceived enjoyment was a significant mediator in the relationship between students’ perceived usefulness and their perceptions of positive impact of learning with blogs; the link between perceived selfefficacy was not mediated by perceived enjoyment. The result also indicates that perceived enjoyment moderated the relationship between perceived self-efficacy and usefulness. The implications of the findings for research and practice are noted.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".