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Record W7002270970

A Nontraditional Instructional Model: Examining its Efficacy of Professional Development

2023· article· en· W7002270970 on OpenAlexaffabout

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDeliverableProfessional developmentSession (web analytics)Sample (material)Qualitative propertySelf-efficacyField (mathematics)Qualitative research
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to examine the efficacy of a nontraditional instructional model developed as a means of professional development, with multi-faceted theoretical and practical knowledge foundations. As the deliverables of the model, a training session and a web application were produced. In this study, the model was applied to 12 in-service teachers in a Canadian education context, using a quasi-experimental, convergent mixed-methods design. Quantitative data were collected with an evaluation questionnaire and analyzed by one sample t-test. Meanwhile, qualitative data were gathered from field notes and analyzed through the interpretive phenomenological analysis (IPA). The model was evaluated as an effective means to increase participants’ teacher efficacy (t(10) = 1.81, P ≦ 0.001). Methodological limitations and recommendations were addressed. The model’s external validity can be further investigated with its application to the wider audience.

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)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.417
GPT teacher head0.552
Teacher spread0.134 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicFetal and Pediatric Neurological Disorders→French-language works237,207→