The Use of the Interactive Whiteboard in Mathematics and Mathematics Lessons from the Perspective of Turkish Middle School Students
Bibliographic record
Abstract
It is a great paradox that despite the great importance attached to mathematics education in Turkey, high failure rates are observed among Turkish students in mathematics. For this reason, new applications are implemented in the field of mathematics education in Turkey. One of these applications is the use of technology in mathematics education. Thus, this research aimed to determine the attitudes and opinions of the middle school students towards the use of the interactive whiteboard, which is among the technologies used in mathematics and mathematics lessons. The research is based on a mixed-method research design in which both quantitative and qualitative methods were used. The quantitative part of the study was conducted with 726 students, selected via the convenience sampling method, in 4 different central schools affiliated to the Ministry of Education (MNE) in the Karabük province during the 2015-2016 academic year, while the qualitative part was carried out with 20 participants determined on a voluntary basis. The research data were obtained from the “Attitude Scale towards Mathematics”, the “Interactive Whiteboard Attitude Scale” and the semi-structured interviews. It was found that the participants in the survey had a positive attitude towards the use of the interactive whiteboard in mathematics lessons and that they were positively affected by the interactive whiteboard in learning mathematics. In addition, it was concluded that participants' attitudes towards mathematics and the use of the interactive whiteboard was above average. It was also found that the male students’ attitudes towards the interactive whiteboard were more positive than that of the female students, and the level of positive attitude towards mathematics decreases as the class level increases accordingly. Finally, a low positive correlation was found between students' attitudes towards mathematics and the interactive whiteboard.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".