Proposed flipped classroom model for high schools in developing countries
Bibliographic record
Abstract
Flipped classroom is an approach that uses technology-support instruction to deliver content pre-class in order to maximise student-centered learning and problem-solving skills during class time. The concept is emerging as a feasible approach and is having a positive impact on students learning outcomes and improves information retention. Some developed countries such as United States of America, China, Australia and Canada have implemented this instructional approach to reform their educational system. Despite the positive impact of the flipped classroom instruction, the challenge remains for many high school teachers in developing countries to embrace this new paradigm. This situation raises legitimate concerns that need to be addressed. Therefore, this paper examines the existing literature that offer evidence-based of flipped classroom implementation challenges and proposes a practical alternative model for high schools in the developing countries. The proposed model provides teachers and students who face difficulties concerning internet access, video production, and equipment costs with an easy strategy to adopt flipped classroom instructional method. This study contributes to the high school curriculum development in developing countries to integrate flipped classroom approach and enhance students’ learning experiences. Keywords: Flipped classroom model, high school, student-centered, developing countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".