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Record W4412740510 · doi:10.22329/jtl.v19i2.8909

Fostering Teachers’ Competencies for Integrated Language Arts, Science, and Technology Instruction: An Exploratory Study

2025· article· en· W4412740510 on OpenAlexvenueno aff
Miriam J. Rhodes, Hanno van Keulen, Martine Gijsel, Adrie J. Visscher

Bibliographic record

VenueJournal of Teaching and Learning · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsnot available
FundersDirectorate for STEM EducationUniversiteit MaastrichtAmerican Educational Research Association
KeywordsThe artsMathematics educationExploratory researchComputer sciencePsychologyPedagogySociologyVisual artsArt

Abstract

fetched live from OpenAlex

This study examined the impact of a teacher professional development (TPD) program on primary school teachers’ skills and self-efficacy in integrated language arts, science, and technology (ILS&T) instruction. The program’s design is based on the four-component instructional-design (4C/ID) model, validated in many areas for developing complex professional skills. Nine teachers from one primary school participated in five TPD group meetings during one school year. The TPD program focused on developing prerequisite skills for ILS&T instruction. Data were collected through lesson observations and interviews to assess teachers’ skills in ILS&T instruction, as well as through a self-efficacy questionnaire. Findings demonstrated varying proficiency levels in the required skills for ILS&T instruction among teachers after the program. This suggests that the program may have provided insufficient support for the development of some, potentially more complex, skills. Although teachers showed an overall increase in self-efficacy, the pre-post-test difference was not statistically significant. There were statistically significant increases in student engagement and instructional strategies subscales. This study discusses the exploratory impact of the TPD program for equipping teachers for ILS&T instruction. A discussion of the findings provides indications for optimizing the program for future large-scale 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 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)0.004
metaresearch head score (Gemma)0.002
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 score0.905
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.381
Teacher spread0.328 · 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
Published2025
Admission routes1
Has abstractyes

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