The Development of Self-Directed Learning in Online English Reading of Thai Students Attending a CALL Learner Training
Bibliographic record
Abstract
This research studied self-directed learning in online English reading of seven secondary school students who attended a ten-week Computer-Assisted Language Learning (CALL) learner training. The CALL learner training was sequenced using Knowles’s (1975) six steps of self-directed learning, which were setting climate, analyzing needs, setting goals, choosing materials, using strategies, and evaluating the outcomes. Each training session covered three components: pedagogical, strategic, and technical training. The training was conducted over ten weeks and included three required sessions and seven optional consultation sessions. During the training, the learners conducted three weeks of self-learning independently. Three sources of qualitative data, including learners’ learning logs, consultation recordings, and interviews, were used to examine the development of learners’ self-directed learning throughout the training. Overall, all participants showed improvement in their self-directed learning in online English reading after the training. However, goal setting and material selection seemed to be the main challenges for most participants. The findings suggested that more research on using CALL learner training should introduce more technology for different online reading tasks since the current study only presented limited tools.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".