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Record W2212807578 · doi:10.6000/2371-1655.2015.01.10

Accounting for the Response-Shift: Pre-Service Teacher-Efficacy Development in Immersive Learning

2015· article· en· W2212807578 on OpenAlexvenueno aff
Winnie Mucherah, Kendra J. Thomas

Bibliographic record

VenueInternational Journal of Humanities and Social Science Research · 2015
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPracticumRestructuringPsychologyTeacher educationControl (management)Service (business)Self-efficacyTreatment and control groupsMathematics educationMedical educationMedicineComputer scienceSocial psychologyInternal medicineBusiness

Abstract

fetched live from OpenAlex

This article compares the changes in efficacy of pre-service teachers in an immersive learning practicum and in a traditional practicum. It does not assume that pre-service teachers take the same pattern of development as inservice teachers because of the restructuring that must occur in an educational setting. The first study followed the development of 159 preservice teachers and found a significant interaction between the two groups across time in terms of teacher efficacy. The immersive group's efficacy increased significantly more than the control group. The second study assessed the response-shift of pre-service teachers between their pretests and their retrospective pretests. As hypothesized, the difference between the pretest and the retrospective pretest was greater for the immersive group compared to the control group, indicating the power that immersive experiences has to recalibrate the concept of teacher-efficacy. Implications for pre-service teacher education are discussed.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.407
GPT teacher head0.560
Teacher spread0.153 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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