To Flip or Not to Flip: The Challenges and Benefits of Using Flipped Classroom in Geography Lessons in Brunei Darussalam
Bibliographic record
Abstract
This study examined the use of flipped classroom in geography lessons in one of the pre-university colleges in Brunei Darussalam. The benefits and challenges of using the flipped classroom as a pedagogical tool in geography were also investigated. Data were collected through action research adopting the use of a flipped classroom approach. This meant that learning geography as subject content was done outside the classroom. The findings of this study revealed that it was not necessary to apply flipped classroom for every lessons. Yet, this study found that flipped classroom was most beneficial when students worked on the application of geographical concepts where they learned to analyse and evaluate given scenarios. A significant improvement in the students’ academic achievement was also observed where through the interactive classroom activities, students developed a deeper understanding of the subject concepts. On the other hand, there were challenges in conducting a flipped classroom, for instance, some students had problems in accessing the lessons outside the classroom. This was one of the crucial elements conveyed in order to successfully implement a flipped classroom and to create an active learning environment during the class time. Without learning the concepts before the class time, the students reported the feeling of being lost, and thus could not fully participate in the classroom activities. Furthermore, a significant amount of time was wasted during the class time in teaching the students the concepts since they were supposed to have learned them prior to the lesson itself. Finally, the flipped classroom was also found to be a challenge to implement in a classroom known to have a passive learning environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".