Flipped Classroom Research and Trends from Different Fields of Study
Bibliographic record
Abstract
This paper aims to analyse the trends and contents of flipped classroom research based on 20 articles that report on flipped learning classroom initiatives from 2013–2015. The content analysis was used as a methodology to investigate methodologies, area of studies, technology tools or online platforms, the most frequently keywords used and works cited references, impacts for students’ learning, and flipped classroom challenges. The results of the analysis were interpreted using descriptive analysis, percentages, and frequencies. This analysis found that various fields were practiced in the flipped classroom approach, and some technology tools were used as the online platform for its practice. Analysis of the impacts showed that flipped classroom brought positive impacts toward students’ learning activities such as achievement, motivation, engagement, and interaction. Several issues in this discussion become implications that can be taken into consideration for future research. Some challenges found in applying flipped classroom are needed to be addressed by future researchers, such as suitability of the flipped classroom for poor quality of video lectures and untrained instructor. This study also becomes an implication for government or policymakers to determine the flipped classroom as a contemporary model to be implemented in teaching-learning activities for higher education, even K-12 students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".