Infographic applications in cooperative groups in physics teaching
Bibliographic record
Abstract
Contrary to the results of some studies, is online education expected to be as effective as face-to-face education? If this question is answered in the affirmative, how can a design be made for Physics education? This research aims to determine the effectiveness of infographic-assisted physics teaching with collaborative groups in teaching physics subjects online, face-to-face, and coeducational settings. In this research, action research method was used and both qualitative and quantitative findings were analyzed. The sample of the study consists of 168 students studying in one of the high schools in Turkey, where one of the researchers teaches physics. Four different data collection tools were used in the study. These tools are: a five-point Likert-type questionnaire, one consisting of 17 questions and the other 20 questions, an interview form consisting of 4 questions, and rubrics consisting of 6 items. Quantitative findings were evaluated with SPSS and qualitative findings were evaluated with the help of content analysis. According to the results obtained from the research findings, infographic applications in collaborative groups offered in different learning environments such as online, face-to-face, and hybrid learning contribute positively to the development of students’ self-efficacy and social skills for learning physics lessons. Applications carried out with infographic-supported collaborative groups (ISCGs); these applications contributed to the development of physics learning, attitude toward physics lessons, and social skills of students studying in online, face-to-face, and hybrid learning environments. It has been determined that these practices contribute positively to the elimination of academic and social differences among students. On the other hand, when ISCG applications in Physics education are carried out together with online education, which is perceived as disadvantageous, it increases the group responsibilities of the students and enables them to have equal opportunities with the environments where face-to-face education is provided. The technological content of ISCG applications affects the attitudes of high school students positively and ensures their active participation in the activities throughout the process.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".