Lecturers’ Perception of Classroom Management: An Empirical Study of Higher Learning Institutions in Malaysia
Bibliographic record
Abstract
The classroom is a learning environment where active interactions and meaningful learning occur between learners and knowledge providers. The teachers and the learners have a unique relationship and this relationship is highly determined by their backgrounds and experiences. Teachers have the responsibility to manage the classroom with the aim of providing quality teaching and enhance the students’ learning experiences. Classroom management can be categorized into three major components namely, 1) content management, 2) conduct management and 3) covenant management. In addition to these three components, time management is another element that is used to evaluate classroom management effectiveness. The objective of this study is to investigate lecturers’ perception of classroom management and the challenges faced. This empirical study compares classroom management practices of two higher learning institutions in Malaysia. One is a government institution and the other is a private university. The data was analyzed based on statistical analysis using mean comparison using the “t” test to identify the regression weight of perceptions in the teaching staff between the private and the government higher learning institutions. The findings of this study revealed that there is no significant difference in the perception of understanding of the four classroom management factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".