Flipped Classroom and Traditional Classroom: Lecturer and Student Perceptions between Two Learning Cultures, a Case Study at Malaysian Polytechnic
Bibliographic record
Abstract
Malaysian Polytechnic is moving towards the use of Information, Communication and Technology (ICT) to meet the needs of the Outcome Based Education (OBE) system that has been implemented since 2010. However, the lack of resources, internet access and lecturer skills in developing their instruction has caused the “chalk and talk” learning culture to remain unchanged, especially in accounting courses. The purpose of this study is to determine the lecturer and students' perception and their achievement between two learning cultures, the traditional classroom and flipped classroom. This study has been conducted between two classes; 61 final diploma accountancy students and a lecturer. Questionnaires and interview was conducted and analyze using independent sample t test. The findings show that there is a significant difference in perception (t(59) = -3.71, p < .05), mean students in a traditional classroom significantly different (M = 4.42, SD = .38) than in a flipped classroom (M = 4.07, SD = .37). The mean also shows, students from both classes had similar perceptions on their learning culture. The percentage of students pass their assessments for the flipped classroom, quiz=26%, test=52%, higher than traditional classroom, quiz=17%, test=50%. It was found that the lecturer had more time to spend on problem solving in the flipped class compared with the traditional class, and although it suffers from a lack of facilities, the flipped class can still be implemented. Therefore, Malaysian Polytechnic institutions could think more globally by teaching locals to meet students' needs of learning with appropriate learning approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".