The Role of Conceptual Change Texts in Concept Teaching: Active Citizenship Learning Space
Bibliographic record
Abstract
The aim of this study is to examine the impact of conceptual change texts in teaching the concepts that are challenging and often misunderstood in the field of active citizenship learning contained in the 6th grade social studies course. A total of 67 sixth graders studying in two different classrooms at the same secondary school participated in this study, in which a quasi-experimental design was used as one of the quantitative research approaches. The experimental group was instructed through conceptual change texts during the implementation, while the control group was taught with the existing curriculum. Data were collected with a concept comprehension test and a concept-related academic achievement test. Descriptive and predictive analytics were used for analysing the data. The students in the experimental and control groups were found to have limited understanding to the extent of not understanding the related concepts. After the activities, it was observed that the final knowledge level of the students in the control group as regards concept comprehension did not show a significant change when compared to their prior level of knowledge, while that of the experimental group showed a considerable increase. A statistical significance was found between the scores of the experimental and control groups in favour of the experimental group in the concept-related academic achievement. In this sense, it can be argued that the use of conceptual change texts to teach the concepts determined in the field of active citizenship learning enables students to learn such concepts more easily and is effective in eliminating students' misconceptions.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".