Interactive Whiteboard Use: Changes in Teacher Pedagogy in Reading Instruction in the Primary Grades
Bibliographic record
Abstract
Interactive Whiteboard (IWB) use is increasing in Canadian classrooms accompanied by numerous claims of benefits for pedagogy and learning. The purpose of this study was to examine how IWBs are integrated into reading instruction in the primary grades (K-3), how their use enhances or alters teacher pedagogy and practices, and supports curricular technology integration mandates. Four teachers who taught in mainstream primary classrooms and were frequent IWB users participated in this four-month study. Eight English Language Arts lessons were observed per teacher. Data sources included interviews, observational data, logs, reflective journal responses, and training materials. Quantitative data on duration and frequency of activities with and without IWB use were analyzed to compare teacher and student use, the content of reading instruction, and the interactivity of activities. IWBs were in active use for approximately 50% of instructional time. The most frequent uses were guided practice, information provision, and questioning. Students engaged in paper-based literacy practices such as worksheet completion and shared and independent reading. The type and duration of students’ IWB use varied between and among classrooms. Paper-based texts and not digital texts predominated. Overall, the primary use of the IWB was to display information and interactive affordances were used infrequently. The teachers perceived IWB use made lessons more engaging and motivating, but support for their perceptions was inconclusive and mixed. Teachers concluded the IWB was a tool that improved the efficiency and effectiveness of teaching, however the nature of their pedagogy had not changed. My results contribute comprehensive, empirical support to the growing debate over pedagogical benefits and changes with IWB use, particularly for interactivity. The appeal of the IWB is such that use of the interactive and multimedia functions may overshadow the development of effective pedagogies and materials. Administrators are cautioned to consider carefully reports of benefits to determine under which circumstances use would be beneficial for their teachers and students. Additionally, teacher training must provide support for pedagogical decision-making in subject areas. Further research to determine the optimal conditions for training and use would assist educators and administrators to use the IWB to best benefit in teaching reading.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".