Relationship between Students’ Perception toward the Teaching and Learning Methods of Mathematics’ Lecturer and Their Achievement in Pre-University Studies
Bibliographic record
Abstract
The academic performance of students is affected by many factors, including effectiveness in teaching, the subjects taught and the environment as well as the facilities provided. The purpose of this study is to determine the relationship between students’ perceptions of the teaching and learning towards the lecturers with their achievements in Mathematics at the Centre for Pre University Studies. The study was a descriptive study in which a survey research design was adopted. A total of 841 students from the centre participated in the study. The data were collected through student’s questionnaire. The questionnaires consisted of 26 questions. 5-Likert Scale questionnaires used in this study focused on the five categories of students’ perceptions; teaching, evaluations, subjects, guidance and environment dimensions. The findings revealed that there is no significant correlation between the average scores of students’ perceptions of teaching and learning towards the Mathematics lecturer with the average scores Mathematics achievement of the students. The study also revealed that there are no significant differences between the average scores of male and female students’ perceptions of the effectiveness of teaching and learning of the Mathematics lecturer. The findings of this study show that the lecturer can improve their teaching skills and techniques that are appropriate to the students.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".