Critical Thinking Disposition: The Effects of Infusion Approach in Engineering Drawing
Bibliographic record
Abstract
Critical Thinking Disposition is known as an important factor that drives a student to use Higher Order Thinking Skills (HOTS) in order to solve engineering drawing problems. Infusing them while teaching the subject may enhance students’ disposition and higher order thinking skills. However, no research has been done in critical thinking disposition while teaching engineering drawing. The current study is to evaluate how critical thinking disposition infused in Engineering Drawing affected students’ thinking disposition. Quasi-experimental with non-equivalent control group design was conducted on the groups from two different Technical Matriculation College for 8 weeks. Two teaching methods, which are Conventional approach and Infusion Approach, were used while teaching Engineering Drawing for control group and treatment group. Control group with 29 students and treatment group consist of 31 students were selected as samples. Pre-test shows that there is no significant different in critical thinking dispositions between control group and treatment group. However, the result in post-test shows that treatment group was significantly higher in critical thinking dispositions compared to control group.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".