MétaCan
Menu
Back to cohort
Record W1596232365 · doi:10.64152/10125/44287

Interactive whiteboards in state school settings: Teacher responses to socio-constructivist hegemonies

2012· article· en· W1596232365 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage learning & technology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsCanadian Linguistic Association
Fundersnot available
KeywordsVariety (cybernetics)Constructivism (international relations)Constructivist teaching methodsMathematics educationInteractive whiteboardPedagogyPsychologyTeacher educationSocial constructivismTechnology integrationEducational technologyHegemonyTeaching methodComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.331
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it