A study of self-efficacy in the use of interactive whiteboards across educational settings: a European perspective from the iTILT project
Bibliographic record
Abstract
This paper reports on the preliminary findings of an EU-funded project called Interactive Technologies in Language Teaching (iTILT). The project aims to produce a range of training materials and resources to support teachers using the interactive whiteboard (IWB) in foreign language (FL) teaching. The project involves 7 European countries (Belgium, Netherlands, Germany, France, Spain, Wales and Turkey) with teachers at differing levels of IWB implementation and proficiency, and encompasses a wide range of educational sectors from primary through to higher education. During the initial stages of data collection, teachers involved in the project completed a likert-scale questionnaire relating to their self-efficacy with both general ICT skills and using a range of IWB features/tools. Despite the differing educational sectors and IWB experience amongst the teachers within the project, there was very little variation in responses between the different countries. Overall, teachers reported high levels of general ICT self-efficacy but low levels of self-efficacy with particular features and tools of the IWB. Nevertheless teachers stated that they allowed pupils to use the IWB and remained positive about the potential benefit of using IWBs to increase pupil participation, engagement and motivation. The findings are considered in the context of existing IWB transitional frameworks and implications for teaching in a variety of classroom contexts are discussed.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".