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Examining International Telecollaboration in Language Teacher Education

2018· book-chapter· en· W2803016192 on OpenAlexaffabout
Geoff Lawrence, Elana Spector‐Cohen

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2018
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsYork University
Fundersnot available
KeywordsPedagogySituatedTeacher educationRelevance (law)Action researchLanguage teacherPsychologyLanguage educationMathematics educationPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This chapter presents findings of case study action research examining the impact of technology-mediated collaboration between teacher-learners in two graduate-level Applied Linguistics Master's programs in Canada and Israel. To date, little research has been conducted on international telecollaborative exchanges in language teacher education programs. This chapter will discuss teacher-learners' perceived benefits and challenges of this international telecollaborative exchange, its impact on beliefs towards the use of technology-mediated tools, and the relevance of these types of collaborations in language teacher education. The authors will highlight individual teacher-learner voices in this study that illustrate how teacher assumptions about authority, experience, and teacher identity evolve on individual pathways and are situated in complex, historically embedded paradigms of teaching and learning experience. The chapter will conclude with insights gained regarding strategies for implementing effective international telecollaborative exchanges in language teacher education programs.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.008
Scholarly communication0.0090.007
Open science0.0020.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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 designQualitative
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

Citations16
Published2018
Admission routes2
Has abstractyes

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