The Interactive Whiteboard: Uses, Benefits, and Challenges. A survey of 11,683 Students and 1,131 Teachers | Le tableau blanc interactif : usages, avantages et défis. Une enquête auprès de 11 683 élèves et 1131 enseignants
Bibliographic record
Abstract
Over the past five years, the interactive whiteboard (IWB) has been massively introduced into schools across the province of Quebec, Canada. This study explores how the IWB is being used, and the associated benefits and challenges. Data on 11,683 students (from 4th year elementary to grade 12) and 1,131 teachers were collected with five instruments: 1) a survey questionnaire for all students, 2) a survey questionnaire for all teachers, 3) individual interviews with teachers, 4) group interviews with teachers, and 5) group interviews with students. Far from calling into question the need to integrate technology into education, the results reveal that certain tools, such as the IWB, may be more complicated and time-consuming to integrate than others. However, despite teachers’ reports of technical problems, the IWB appears to have real educational potential.Au cours des dernières années les tableaux blancs interactifs (TBI) ont été massivement introduits dans les écoles du Québec (Canada). Cette étude explore comment cet outil numérique est utilisé, de même que les avantages et les défis qui y sont associés. Les données ont été recueillies auprès de 11 683 élèves (de la 4e à la 12e année) et 1 131 enseignants avec cinq outils de collecte de données: 1) un questionnaire d’enquête pour tous les élèves; 2) un questionnaire d’enquête pour tous les enseignants; 3) des entrevues individuelles avec des enseignants; 4) des entrevues de groupe avec des enseignants; 5) des entrevues de groupe avec des élèves. Loin de remettre en question l’intégration des technologies en éducation, les résultats de cette étude révèlent plutôt que certains outils, comme le TBI, peuvent parfois être plus complexes et chronophages à utiliser que d’autres. Néanmoins, même si plusieurs enseignants ont souligné les nombreux problèmes techniques, le TBI comporte un grand potentiel éducatif.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".