Student teachers’ satisfaction for blended learning via Edmodo learning management system
Bibliographic record
Abstract
In a digitally driven world, behaviours of future teachers for blended learning (both face-to-face and on-line classes) need to be examined. This study serves three purposes. The first is to examine student teachers’ preferences for Community-of-Inquiry model-driven blended learning via Edmodo. Second, predicting student satisfaction on b-learning from a combination of four variables (gender, having internet access, using the internet for information access, and previous experience in on-line learning) was questioned. And third, b-learning orientations of participants were investigated. One of the mixed methods, the concurrent triangulation design was employed in which both qualitative and quantitative methods were applied. The study group included 135 freshmen and junior students (29 males and 106 females) from a western Turkish educational faculty. The findings for the first question indicated that 70.4% of student teachers prefer b-learning. For the second, 15% of the variance in satisfaction on b-learning was explained by the proposed model with a medium effect. And for the third, the qualitative findings were discussed under Perceived Usefulness (PU) and Perceived Uselessness and Unease-of-Use (PU-UU) themes. Although less than a quarter of participants found b-learning useless, most held positive notions for b-learning practices via Edmodo.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".