Interactive whiteboards in state school settings: Teacher responses to socio-constructivist hegemonies
Bibliographic record
Abstract
Recent CALL research suggests that the arrival of new technologies in the language classroom has led to an increased dominance of the socio-constructivist paradigm (Felix, 2006). Borg (2006) suggests, however, that the hegemony of this paradigm may not extend beyond well-researched university and private ESL contexts. The present study tests this prediction by examining the integration of interactive whiteboard (IWB) technology by non-native speaking teachers of EFL in state schools in France and Germany. Teachers ’ cognitions were investigated via longitudinal qualitative empirical data, involving classroom observations, video recordings of lessons, in-depth interviews and video-stimulated reflections. Findings suggest that in spite of communicatively oriented, socio-constructivist training, teachers used IWB technology to implement a variety of different approaches. The paper traces teachers ’ use of different models, from traditional grammar-translation to more communicative and constructivist models of task and project-based learning. It shows how individual teachers ’ approaches are shaped by a variety of factors, such as teachers ’ teaching and learning experience, pedagogical beliefs and institutional demands. These findings illustrate the complexities of technology integration in CALL and show how teachers often adapt or ignore hegemonic pedagogies to construct their own representations of the technology which are more in line with their curricular and personal goals.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".