Bibliographic record
Abstract
Teaching in higher education is to give knowledge to students to understand the principles and apply them in given situations. Method of teaching is to be amalgamated with learning environment where students attend work with interest. By considering this phenomenon, coursework was designed and conducted for Quantity Surveying module from 2014 as a study because of the poor performance shown by the students for both attendance and yearend written paper in 2012. Necessary data was collected from 2012 to 2016 to analyze the impact of this exercise and found that this exercise has supported for promoting teaching and learning. It has improved students’ attendance, built good relationship with the teacher and gained knowledge to achieve high performance at yearend paper. It was found by this study that encouragement made only for improving attendance is not sufficient to obtain high performance in teaching without proper system for learning. With developed coursework in 2016, students have obtained high marks for yearend paper reaching to grades A and B by 35% and 46% respectively. In addition, students have expressed comments by rating 88% and 77% saying this method was at satisfactory and good level respectively. High impact shown in 2015 and 2016 may be due to the experience gathered by the teacher implementing this learning process from 2014. Therefore application of this coursework for teaching and learning process benefited not only to the students but also to the teachers as both parties are able to achieve their objectives with high performances.Key wards: teaching, learning, coursework, attendance, performance
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".