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Record W1985306945 · doi:10.1080/0305764x.2014.998624

The physical placement of classroom technology and its influences on educational practices

2015· article· en· W1985306945 on OpenAlexaff
Jo Tondeur, Elke De Bruyne, Maarten Van Den Driessche, Susan McKenney, David B. Zandvliet

Bibliographic record

VenueCambridge Journal of Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTypologySchematicPerceptionEducational technologyRepresentation (politics)Mathematics educationComputer scienceMultimediaPsychologySociologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study was to gain deeper insights into how technology restructures the classroom as a spatial setting and how the positioning of these technologies can be associated with educational practices. The research includes a photographic and schematic representation of 115 classrooms in 12 primary schools in Belgium, resulting in a typology based on structural features of the classrooms. Based on the typology derived and the specific positioning of technology, nine teachers were purposefully selected and interviewed regarding their perceptions concerning the link between the use of technology and the classroom layout. The results indicate that (1) the positioning of technology can be related to specific types of technology use; (2) the classroom layout is in transition from one central display to multiple screens; and (3) because of physical access to technology, the educational practice of individual classes is spatially dispersed over different locations within the school.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.401
Teacher spread0.358 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2015
Admission routes1
Has abstractyes

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