A Content Analysis of the Studies on the Use of Flipped Classrooms in Foreign Language Education
Bibliographic record
Abstract
Teaching foreign languages via flipped classrooms, in which the typical elements of a course are reversed, has been apopular pedagogy recently as the modern digital technology is flourishing unprecedentedly. The aim of this study isto review a selected sample of 50 studies on flipped classroom instruction in foreign language education publishedfrom 2014 to 2018 in Turkey and abroad. A content analysis was conducted for each study in terms of study years,study types, study locations, foreign languages taught, language skills taught, research methods, sampling, data tools,data analysis procedure and variables through a ‘Research Classification Form’. Results showed that studies weredone mostly as articles in 2016 in 14 countries mostly in Turkey using quantitative research designs commonly. Inthese studies, flipped classroom instruction was implemented for teaching all skills of English as a foreign language.Samples generally consisted of higher education students with lower than 50 as a sample size. In these studies, asquantitative data collection tools, achievement tests were utilized and as for analysis procedures, mean and standarddeviation were used predominantly. Additionally, the variables of Attitudes towards Foreign Language Lessons,Academic Performance, Perceptions, and Writing Performance were frequently researched. The findings obtainedfrom this study are expected to contribute to future studies conducted on flipped classrooms in foreign languageteaching.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".