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Record W4411372231 · doi:10.1007/s42330-025-00362-4

Exploring Instructional Design, Occupational Interest, and Orientation Through Integrated STEM: A Systematic Literature Review

2025· article· en· W4411372231 on OpenAlexvenueno aff
Hui-Hui Wang, Muhammad Uzair Awan

Bibliographic record

VenueCanadian Journal of Science Mathematics and Technology Education · 2025
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsScience educationOrientation (vector space)Systematic reviewPsychologyEngineering ethicsMathematics educationSociologyEngineeringMEDLINEPolitical science

Abstract

fetched live from OpenAlex

Abstract The science, technology, engineering, and mathematics (STEM) education reform movement has swept across the USA due to an urgent need for more workplace-ready STEM employees. As a result of the STEM education reform movement, there has been an increase in research studies concerned with STEM education and STEM career development in the past decade. The purpose of the study is to analyze the past 10 years’ worth of empirical research to explore the key instructional designs (e.g., inquiry-based, project-based, and problem-based teaching) that are used in STEM integration, as well as the impact on students’ occupational interests and development. The results of the study (1) outline the included studies’ characteristics, (2) summarize and synthesize themes across included studies, and (3) provide implications for future research in pursuit of advancing students’ STEM career development through instructional design. Findings show that substantial progress has been made in designing integrated, student-centred learning experiences that are rooted in real-world contexts. However, findings also illuminate areas of potential growth, such as research design, where employing diverse methods could further enhance our understanding of how using different instructional designs and principles of integrated STEM can positively impact student STEM career aspirations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.263
Teacher spread0.223 · 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 designTheoretical or conceptual
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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