A Development of Online Problem-Based Learning Model to Promote Self-Regulated Learning among Undergraduates
Bibliographic record
Abstract
This research aimed to develop and evaluate an online problem-based learning (PBL) model to enhance undergraduates' self-regulated learning (SRL). The Research and Development method was used in two phases: the first was to develop and validate the model. It began with a literature review to identify core features of effective PBL and SRL within an online environment, then set a concept framework and create the model manually. Seven experts were reviewed following the model, and it was piloted for further optimization. Second, the model will be implemented and evaluated with a sample of 52 students at Thailand National Sports University, Chumphon Campus. The main instruments used were the developed model, the model quality questionnaire, SRL assessments (pre- and post-), and after-action review forms. The results showed that (1) a developed model consisting of four sections: orientation of the model, the model of instruction, application, and student outcomes. Six core components: authentic problems, online collaborative learning, self-regulated learning, instructional scaffolding, online learning resources, and authentic assessments, which are organized through three main processes: preparation, implementation of SRL strategies, and summative assessment, and all activities using an iterative learning three stages: meeting the problem and planning, doing and checking, and presenting the result and reflecting. Experts agreed on the quality of this model, which was excellent. (M= 4.61, SD=0.52). (2) Average SRL score was significantly higher after learning with this model compared to prior us (p<0.01, large effect size; d=1.03). The findings of this study support the developed model that can improve the students' SRL effectiveness. Overall, the students agreed that an instructor plays a critical role in developing their SRL and problem-solving skills. The students demonstrated more self-assurance and were apt to use this method to learn other subjects.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".