The Effectiveness of Conflict Maps and the V-Shape Teaching Method in Science Conceptual Change among Eighth-Grade Students in Jordan
Bibliographic record
Abstract
The purpose of the present study is to investigate the effectiveness of Conflict Maps and the V-Shape method as teaching methods in bringing about conceptual change in science among primary eighth-grade students in Jordan. A randomly selected sample (N=63) from the Bani Kenana region North of Jordan was randomly assigned to the two teaching methods (Conflict Maps, N=31; V-Shape, N=32). A conceptual multiple-choice test was developed to measure misconceptions regarding electric energy commonly held by eighth-grade students. The data gathered were statistically processed using SPSS software. The results indicated that both methods, Conflict Maps and the V-Shape method, were successful in bringing about conceptual changes in the students. At the same time, the results did not prove either method to be superior in facilitating conceptual shifts regarding electric energy in eighth-grade students. In light of earlier results, this study recommends that curriculum developers take into account teaching methods designed according to conceptual change models and organize training workshops for teachers to learn how to integrate Conflict Maps and the V-Shape method into their teaching. Furthermore, this study recommends that similar studies be conducted with various subjects.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".