Expectations of Student Engaged in Tertiary Education on Engineering Courses from Their Teachers of Choice
Bibliographic record
Abstract
Although several learning theories ranging from behaviourism to cognitive to humanism have been proposed to choose the appropriate effective teaching models, none can be applied across the broad to all learners in all situations, nevertheless some commonalities emerge. A combination of pedagogical and andragogical, “middle of the road” approach meets the need of a larger segment of the audience. Our experiences and many surveys confirmed that the theory of behaviourism may be well suitable to first and second year students and cognitive approach may be well fit to third and forth year students. Only for supervising final year project work, the theory of teachers and students work together, humanism may be adoptable. The survey was conducted among the students enrolled for engineering courses in Curtin University, Sarawak to assess the grade of importance on twelve basic aspects of knowledge, skill and planning expected from the teachers. This paper describes the analysis of the action survey results and summaries of the recommendations for the effective teaching. The survey concludes that each well defined lecture arranged in right sequences should be orally presented in a simple constructive language with the consistent flow speed optimum suitable to majority of the audience for better learning outcomes. Language proficiency and fluency are not the barriers for the successful teaching to multicultural class room in tertiary education. It concludes that “Teachers of student choice” are not born and they are trained by acquiring required relevant knowledge and sincere practice of delivering the lectures in an optimum suitable way to the audience for effective learning.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".