The Level of Critical and Analytical Thinking Skills among Electrical and Electronics Engineering Students, UKM
Bibliographic record
Abstract
The high demand by the industry for graduates capable to critically analyze the causes, content and quality of information and using them effectively to identify and solve engineering problems, has been constantly and incessantly discussed. The inability to think analytically and critically contributes to the increased percentage of unemployed graduates. Additionally, the Malaysian students resort to memorizing and rote learning to find an easy way to get a degree and then find a job. This paper investigates the level of critical and analytical thinking skills among the students in the Department of Electrical, Electronics and Systems Engineering (EESE), Faculty of Engineering and Built Environment (FEBE), UKM. This study was conducted on a group of third year students in Semester 1 2010/2011 using three instruments; the analytical component of MTest model questions by the Malaysian Ministry of Education (MOE) in the selection of prospective students for Teachers College throughout the country, Marbach-Ad and Sokolove's taxonomy (MST) for student questions on a topic discussed in the lecture and the open-ended question posed in the final examination for the microprocessor and microcontroller course. Analysis based on these three techniques provide a rough estimation on the level of analytical and critical thinking skills among students and in this study, it was learned that the critical and analytical thinking skills among these students are at a very moderate level despite their high academic achievement.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".