The Correlation Between the Creative Thinking Tendency of Mathematics Teacher Candidates and Their Attitudes Towards Instructional Technologies and Material Design Lesson
Bibliographic record
Abstract
Instructional Technologies and Material Design (ITMD) lesson is a tool that enables Mathematics teacher candidatesto design materials for contributing to lasting learning of their students, and use them most effectively. This lessonhelps students to activate their creative thinking skills, increase their courage and self-confidence, find differentsolutions in the face of problems, and therefore introduce creative products. In this context, firstly, it is aimed toexamine the level of Mathematics teacher candidates' attitudes towards the Instructional Technologies and MaterialDesign lesson and their effects in terms of different variables in this study and then determine whether there is arelationship between creative thinking tendencies of Mathematics teacher candidates and their attitudes towardsInstructional Technologies and Material Design lesson in this study. The sample of the research constitutes of 315Mathematics teacher candidates who are studying in Mathematics Education Department spring semester of the2016-2017 academic year of at Necmettin Erbakan University. The data were obtained by applying Marmara CreativeThinking Tendency Scale (MCTTS) developed by Özgenel and Çetin (2017) and whose validity and reliability studieswere conducted to teacher candidates, and by applying the Attitude Scale towards Instructional Technologies andMaterial Design Lesson (ASITMD) developed by Çetin, Bağçeci, Kınay, Şimşek (2013) and whose validity andreliability studies were conducted. There was no significant difference between the Attitudes towards InstructionalTechnologies and Material Design Lesson and the Creative Thinking tendencies, in terms of gender variable, but it wasfound significant difference in terms of the high school graduation variable. However, it was obtained that there is apositive and non significant relationship between creative thinking tendencies of Mathematics teacher candidates andtheir attitudes towards Instructional Technologies and Material Design lesson.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".