The Four Scaffolding Modules for Collaborative Problem-Based Learning through the Computer Network on Moodle LMS for the Computer Programming Course
Bibliographic record
Abstract
Scaffolding is a learning approach designed to promote a deeper understanding, it is the support given during the learning process which is tailored to the needs of the student with the intention of helping the student achieve the learning goals, including resources, a compelling task, templates and guides, and guidance on the development of cognitive and social skills. Meanwhile, problem-based learning (PBL) situate learning in complex tasks. Such task require scaffolding to help students engage in sense making, managing their investigations, problem-solving processes, and encouraging students to articulate their thinking and reflect on their learning. This study aimed to develop four scaffolding modules for collaborative problem-based learning through the computer network on Moodle LMS for the computer programming course of undergraduate students, and to analyze the satisfaction of the experts and students after using the developed scaffolding modules. The four scaffolding modules consisted of metacognitive scaffolding, conceptual scaffolding, strategic scaffolding, and procedural scaffolding, each of which represented by a 3-D animation expert cartoon to attract students. The sample group were twenty-two students of small group pilot and six experts. The findings indicated that the degree of satisfaction towards the scaffolding from the experts was high and the degree of the satisfaction towards the scaffolding from the students was also high. This can be used the four scaffolding modules to complete PBL task successfully.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".