Problem Based Learning to Enhance Students Critical Thinking Skill via Online Tools
Bibliographic record
Abstract
Critical thinking in 21st century has been recognized as a skill for citizens. Critical thinking is define as the intellectual thinking skills like analyzing, reasoning, problem solving, creative thinking, making judgement and good decision maker. One way to enhance critical thinking skill is by Problem Based Learning (PBL) approach and it is already widely utilized in educational course as problem solving in learning assessment. Meanwhile, an online tool is the effective approach for teaching and learning in worldwide nowadays. Recently, the previous papers more focus on PBL and the outcome of critical thinking, but not the process, tools to support especially in the new era of learning. There is quite a few paper discuss about using online tools in PBL and critical thinking. The purpose of this review is to look into how the online tools were tackled by previous scholars and the latest trends on online tools in PBL to enhance critical thinking skill. The finding is based on past articles from the other researchers before. Hopefully, this study will contribute to the introduction of a new era of understanding the importance of PBL to enhance critical thinking skill via online 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".