The Effect of Teaching Supported by Interactive Whiteboard on Students’ Mathematical Achievements in Lower Secondary Education
Bibliographic record
Abstract
The aim of this study is to investigate the effect of using the interactive whiteboard in mathematics teaching process on the 7th-grade students’ achievement. This study was conducted as experimental design. Experimental and control groups were composed of 58 7th-grade students from one school in the 2015-2016 educational year in Ankara. As a measurement tool, an achievement test developed by the researchers was used as the pre-test and post-test. An education program which included the activities with the interactive whiteboard was developed by researchers. And, this program was implemented to the experimental group 12 hours over 3 weeks. On the other hand, activities for the control group were limited to the blackboard usage. In the analysis of the data, “analysis of covariance (ANCOVA)” was used by defining the pre-test scores as “covariate” variable.According to the findings, it was observed that there was a significant difference between experimental and control groups pre-test average scores. When the difference of pre-test scores under control, it was observed the significant difference between the average post-test scores in favor of the experimental group. These findings show that using the interactive whiteboard in mathematics teaching process has positive effects on the students’ mathematical achievement. These results are supported by some other researchers’ findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".