The Effect of Smart Boards on the Cognition and Motivation of Students
Bibliographic record
Abstract
In recent years the research literature has explored technological developments in varied areas that measure change. The current study focuses on the smart board, and its purpose is to examine its effect on the school system. The study was conducted via a questionnaire completed by 130 respondents (boys and girls) in the fifth and sixth grades of two elementary schools in Jerusalem—Efrata and Tali Gilo. Smart boards were introduced in these two schools in recent years.We hypothesized that smart boards improve teaching, based on the teaching measures developed by Nira Hativa of Tel Aviv University: order and organization, level of clarity, interest, and general level of satisfaction. The study’s significant finding is that the greatest improvement since the introduction of smart boards is in the variable of clarity, and a significant difference was found in the favor of sixth grade students. Additionally, a significant difference was found in the variable of interest, in favor of the girls. All four variables appear to be interrelated, and each contributes to the student’s success and to improving the student’s learning process.The research findings illuminate the contribution of technology to teaching, through a case study of smart boards, in the dimension of clarity, found by the study to be a significant criterion of good teaching. Examination of the various technological tools in light of their contribution to the research-proven dimensions of outstanding teaching might enhance the pedagogical contribution of technological developments to teaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".