Enhancing Pre-Service Teachers’ Perspectives on Teaching and Learning Through the Development of E-Learning
Bibliographic record
Abstract
This study aimed to develop e-learning for pre-service science teachers and investigate the effect of this e-learning on their attitudes toward learning and teaching. A mixed method was employed in this research. Participants were 22 pre-service science teachers at one public university in Thailand who enrolled in the Educational Psychology and Guidance for Teacher class in the first semester of 2020. The instruments used in this study were a questionnaire with 20 items to investigate participants’ knowledge, understanding, and opinions toward the e-learning lessons. The data analysis by mean, standard deviation, relative gain score, and content analysis. The results indicated that the developed e-learning effectively increases pre-service science teachers’ attitudes toward teaching and learning. The relative gain score of attitudes toward teaching and learning was 89.47, and the high satisfaction level towards e-learning (mean = 3.87). The findings show that participants reflected that e-learning lessons helped learners manage learning independently, learn from any place, and repeat learning to learn anytime. However, feedback highlighted areas for improvement, including the need for more engaging audio narration and detailed explanations in exercises of this e-learning because it was monotonous.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".