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Record W2976819598 · doi:10.18733/cpi29483

Pre-Service Teachers’ Reactions to Education Teacher Performance Assessment

2019· article· en· W2976819598 on OpenAlexvenueno aff
Belete Mebratu, Kelly H. Ahuna

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsPracticumTeacher educationStudent teachingTeacher preparationStudent teacherMathematics educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

The purpose of study was to explore the experiences of teacher candidates about being assessed by the Education Teacher Preparation Assessment (edTPA) requirements during their student teaching practicum. Fifty-six elementary and adolescent majors working for a Master of Science Degree in Education participated in the study by responding to open-ended survey questions. The study aimed at answering two research questions: (1) What are the challenges/concerns that the student teachers report about their experiences of edTPA during their student teaching placements? (2) Do teacher candidates suggest edTPA remains as part of the teacher education program requirement? The findings of the study indicate that the teacher candidates are adamant about their unfavorable experiences of edTPA implementation. They expressed that they found edTPA requirements to be an additional burden, not beneficial, a distraction, and they suggest that edTPA should be discarded from current teacher education programing. While such findings call for considerations to revisit aspects of edTPA for improvement, further studies are suggested to add insight into the nature of edTPA implementation.

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)0.004
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.375
GPT teacher head0.467
Teacher spread0.092 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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