Online English Teaching with Interactive Slides: Reflection from Thai Pre-Service English Teachers’ Self-Directed Learning
Bibliographic record
Abstract
This study explores the self-directed learning (SDL) experiences of Thai pre-service English teachers as they navigate interactive slide platforms for online English teaching. Aimed at understanding how these teachers engage with SDL and reflecting on their experiences, the research employs a mixed-method approach. It begins with qualitative data collection to uncover the SDL strategies used by participants, followed by a quantitative phase to strengthen the findings. Engaging with 29 second-year English Education major students at a university in Thailand, the study focuses on those most relevant to the research objectives. The findings reveal that these pre-service teachers effectively utilized various SDL strategies, including utilizing YouTube tutorials and collaborating with peers. Interestingly, they highlighted gamification features, such as rewards for correct answers, as powerful tools for keeping students engaged and motivated. While the interactive slides were appreciated for their user-friendly design and real-time feedback capabilities, participants also faced challenges like technical issues and the costs associated with premium features. This study underscores the significance of interactive platforms in equipping pre-service teachers with the skills needed for effective online teaching, a growing necessity in today’s educational landscape.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".