Personalised Blended Learning Experiences for English Communication and Self-Regulated Learning Skills in a Thai Secondary School
Bibliographic record
Abstract
In this study the researcher investigates Thai secondary students’ experiences with a personalised blended learning (PBL) platform using Microsoft Teams (MS Teams) to enhance their English communication skills and self-regulated learning (SRL) skills. Employing an intrinsic case study method, I collect data from 19 students with A1 level Common European Framework of Reference for Languages enrolled in the remedial English course through classroom observation field notes, learners’ reflective journals, semi-structured interviews, and MS Teams assignments integration. Through thematic analysis, the findings reveal that MS Teams features, such as Reading Progress, Immersive Reader, and interactive task-based assignments, support listening, speaking, reading, and writing skills, allowing students to practise asynchronously and receive timely, personalised feedback. Regarding SRL, MS Teams enables students to set goals, track progress, and reflect on their performance while offering flexibility through adjustable deadlines and revisitable materials. However, I note challenges such as technical limitations, variations in digital literacy, and inconsistent engagement levels. Via thematic analysis I identify recurring patterns in qualitative data, while recorded data from MS Teams Insights highlight student performance and engagement trends. In conclusion, MS Teams, when integrated into PBL, significantly enhances English communication skills and SRL behaviours, providing valuable insights for improving technology-supported language instruction in secondary education contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".