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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".