Continuous-Grouped-Self-Learning: In the Perspective of Lecturers, Tutors and Laboratory Instructors
Bibliographic record
Abstract
This paper presents the perception of lecturers, tutors and lab instructors towards the implemented Continuous-Group-Self-Learning (CGSL) in the Department of Computer and Communication System Engineering (CCSE), Universiti Putra Malaysia. This innovative system introduces mock teaching and student-lecturer role as a technique of delivery. The system ensures a continuous group work and the students are learning with class-oriented problem-based learning (CO-PBL) instead of seasonal project oriented problem-based learning (PO-PBL). The radical change in the assessment by adopting mock teaching oriented assessment (MTOA) has given a new definition to assess the student thoroughly. 49 respondents have taken part in this study, in which 30 of them are lecturers, 8 are tutors and 11 are laboratory instructors who currently active serving in the department. In general, 56% of the respondent do not agree this learning system shifted the teaching job to the students and 56.55% of them disagree this approach is a burdensome to the students who are undergoing this learning style. This system in fact a catalyst that urges the lecturers, tutors and lab instructors to enhance themselves in order to cope up with the ‘knowledge demand’ from the student when 82.1% of the respondents agree to be more knowledgeable as compared to conventional teaching method.
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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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".