Teachers’ Attitudes toward Using Interactive Whiteboards in English Language Classrooms
Bibliographic record
Abstract
<p class="apa">Educational technology plays an increasingly important role in the teaching and learning process. Successful integration is the goal of any new educational technology. The interactive whiteboard (IWB) can be effectively used by teachers to enhance the effectiveness of their lessons. This study explored the attitudes and insights of Saudi female teachers regarding the use of IWBs when teaching English as a foreign language (EFL). It also investigated possible obstacles they may face during their use of this novel technology.</p><p class="apa">Data was collected by distributing questionnaires to forty three teachers at different girls’ schools in Riyadh. The results indicated that participants in this study demonstrated positive attitudes toward using the IWB in the EFL classrooms. The results also showed that teachers consider IWBs to be useful devices for enhancing the teaching and learning process and for designing new instructional situations. IWB-based lessons were perceived to be more comfortable for teachers in teaching English. However, teachers stated that they faced some technical obstacles in their use of IWBs.</p><p class="apa">The current study recommended that EFL classes should be equipped with all supplicants of the IWBs. It also suggested that training is important for teachers to deal with the technological devices. EFL teachers need more training to learn how to resolve technical and system problems; they also need to understand how to use all the options offered by the IWBs.</p>
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".