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Record W4210335776 · doi:10.5539/ies.v15n1p148

Making a Difference Through Sustained In-Service Teacher Training

2022· article· en· W4210335776 on OpenAlexvenueno aff
Abha Gupta, Guang Lea Lee

Bibliographic record

VenueInternational Education Studies · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationLiteracyAcademic achievementMultimethodologyReading (process)Professional developmentQualitative propertyTeacher educationTeaching methodKnowledge levelData collectionFaculty developmentQualitative researchPedagogySociology

Abstract

fetched live from OpenAlex

This study is based on collaboration between a school and a university on professional development training of 4th and 5th grade elementary school teachers in a southeastern state in the USA. The study was three-pronged and focused on teacher knowledge, pedagogy, and student achievement. We examined how the building of teacher capacity affected the performance of underachieving students in math and literacy. Underachieving students were targeted with specific strategies, projects, problems solving stories, self-reflection, and higher-level thinking questions. Student performance was measured for literacy achievement, with quantitative and qualitative measures used for data collection purposes. Students showed progress over previous years in reading scores. Teachers’ growth in knowledge related to the content area was evidenced by the consistently high mean scores. Teachers’ self-ratings on the frequency of using the targeted strategies were indirect evidence of their implementation in the classroom. The results were positive and showed significant progress over previous years concerning student achievement.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.284
GPT teacher head0.408
Teacher spread0.125 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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