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Record W4399727839 · doi:10.4000/11ub6

Collaboration Between Two Contexts: Acceptability of a Pedagogical Innovation with Digital Technology

2024· article· en· W4399727839 on OpenAlexaff
Alain Stockless, Thomas Forissier, Isabelle Lepage, Lamprini Chartofylaka, Valéry Psyché, Claire Anjou, Jacqueline Bourdeau

Bibliographic record

VenueContextes et didactiques · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsContext (archaeology)Perspective (graphical)Relevance (law)Theme (computing)PerceptionPedagogyPsychologyQualitative researchMathematics educationKnowledge managementSociologyComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

As part of the Educational Technologies for Teaching in Context research project, we have conceived a pedagogical innovation with digital technologies by considering teaching from two different contexts. In this case, the context is characterized by environments that differ between two groups of learners and this is what we call context effects-based teaching. Thus, we consider context to develop a pedagogical innovation with digital technologies involving students from two different countries. They collaborate online and investigate an identical theme while facing strongly contrasting contextual realities. This pedagogical innovation with digital technologies involves a significant change for teachers and its acceptance can foster its use in the classroom. In this perspective, this article examines the acceptability of pedagogical innovation (context effects-based teaching) and digital use by teachers. Inspired by the Technology Acceptance Model by Davis, a qualitative design research was used and 7 semi-directed interviews were conducted. The finding showed the relevance of Context Effects-Based Teaching with digital and its acceptability is characterized by a positive perception of usefulness and intention of use, and teachers noticed in-depth learning in their students.

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 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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.291
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.149
GPT teacher head0.482
Teacher spread0.333 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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