Can I MOOC to Catch up? The Effects of Using an LMOOC as a Remedial Tool for EFL Students in Thailand
Bibliographic record
Abstract
This study investigated the effects of supplementing a traditional EFL class with a grammar-focused LMOOC. It also investigated students’ attitudes to the LMOOC. Students taking a compulsory English course at a nursing college in Thailand were divided into two groups, a LMOOC group (n=33) and a non-LMOOC group (n=26). The LMOOC group engaged in a 4-week LMOOC as a supplement to their usual English classes. The non-LMOOC group continued with their usual English classes with no additional interventions. Final examination scores and gains since the midterm for the two groups were compared. Attitudes to the LMOOC were assessed using a questionnaire and interviews. Students in the LMOOC group experienced statistically significantly larger gains in grammar scores than the non-LMOOC group (M = 5.45, SD = 4.31, p < .001). Students reported very positive attitudes towards the LMOOC, in terms of enjoyment and perceived effectiveness. The estimated gains found in this small study were relatively modest, but our findings suggest that LMOOCs as a way to supplement in-class teaching may improve attainment and foster positive attitudes. Further controlled experiments to assess the wider applicability of our findings are needed.  
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".