Exploring Instructional Design, Occupational Interest, and Orientation Through Integrated STEM: A Systematic Literature Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".