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Record W1740003218 · doi:10.6000/2371-1655.2015.01.05

“It Puts us in our Students’ Shoes”: Listening to Voices from Teacher Candidates on Their Test-Taking Experience

2015· article· en· W1740003218 on OpenAlexaffvenue
Liying Cheng, Jia Ma

Bibliographic record

VenueInternational Journal of Humanities and Social Science Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsConstruct (python library)Test (biology)Active listeningPsychologyExperiential learningBachelorRelevance (law)Construct validityMathematics educationPedagogyDevelopmental psychologyPsychometricsComputer science

Abstract

fetched live from OpenAlex

This study explores teacher candidates' experiential learning through their test-taking experiences while attending a Bachelor of Education (B. Ed.) program. Eighty-four written reflections by teacher candidates taking a midterm course examination on classroom assessment practices were analyzed. Major themes emerging from these reflections on the test-taking experience are related to validity concepts of construct representation, construct-irrelevant variance, relevance, and fairness. The study reveals that the test-taking experience could be valuable to teacher candidates in their learning of classroom assessment practices and in their understanding of the issues in test taking that may influence test performance. This, in turn, could potentially provide teacher candidates with a direct framework for their future classroom assessment practices, by which they may support their own future 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.409
GPT teacher head0.546
Teacher spread0.136 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designOther design · Qualitative
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
Published2015
Admission routes2
Has abstractyes

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