Should We Flip the Social Studies Classrooms? The Opinions of Social Studies Teacher Candidates on Flipped Classroom
Bibliographic record
Abstract
The technology revolution continues to profoundly influence the educational process. Thus, the traditional teaching process is changing and education which is individualized with technology supported teaching processes comes to the forefront. One of the concrete indicators is the flipped classroom model. The purpose of this study is to determine the opinions of the teacher candidates who continue the undergraduate program of social studies education towards the flipped classroom model and its usability in social studies course. Phenomenology which is one of patterns of the qualitative research approach was used as the individual perceptions of the students were examined in the study. Purposeful sampling method was used in the determining the study group of the research and nine teacher candidates were included in the study with criteria sampling. The data were collected with a semi-structured interview form. Content analysis method was used in the analysis of research data. As a result, teacher candidates have a positive attitude towards technology-supported teaching processes and stated that the use of flipped classroom model in social studies course would solve many problems like time management and absence of in-class practices. Moreover, it has been stated that prospective teachers are not equipped enough to implement this model, and problems may arise due to supervision during the application phase. Finally, teacher candidates stated they wanted to use the flipped classroom model in the teaching process, but that a certain experience has to be provided first.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".