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Preserve Teachers’ Experiential Leading and Learning of Inclusion Strategies

2023· article· en· W4390759845 on OpenAlex
Erin Keith

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

VenueInternational Journal of Technology and Inclusive Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsExperiential learningInclusion (mineral)PsychologyPedagogyMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

The purpose of the research was to investigate the relationship between preservice teachers' attitudes, funds of knowledge, and self-efficacy of using inclusive high-leverage instructional strategies while collaborating with others to curate an experiential collection of hands-on tools and artifacts.This investigation employed a mixed-method approach including pre-and post-surveys (n=18) as well as semi structured interviews of Year 2 BEd students (n=2).The findings of this study contribute to the existing research on inclusion self-efficacy that new teachers benefit from peer-led experiential learning opportunities.The results also support a pre-service program dialogue in reimagining and strengthening the design of BEd inclusion courses and address balancing students' experiential with theoretical knowledge, promote positive pre-service teacher attitudes, and enhance self-efficacy about inclusive teaching practices.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.264
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.012
GPT teacher head0.374
Teacher spread0.362 · 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