Understanding STEM Outcomes for Autistic Middle Schoolers in an Interest-Based, Afterschool Program: A Qualitative Study
Bibliographic record
Abstract
Abstract Current research underscores that there are only a few evidence-based programs that teach STEM (science, technology, engineering, and mathematics) as part of their curriculum, especially for autistic students. Even fewer programs focus on engineering and design learning. Hence, we developed an informal afterschool maker program to develop autistic and non-autistic students’ interests in engineering to understand their experiences learning STEM concepts and values while applying the engineering mindset to develop projects. This qualitative study aimed to explore and understand students’ experiences participating in STEM activities in the maker club. We interviewed twenty-six students (seventeen autistic and nine non-autistic), nine teachers, and thirteen parents representing diverse cultural and socio-economic backgrounds across three public middle schools in a large urban metropolitan city between 2018 and 2019. Our thematic analysis yielded four themes: (1) active participation in STEM; (2) curiosity about STEM topics, concepts, and practices, (3) capacity-building to engage in STEM learning; and 4) understanding of the importance of STEM education in daily life. The results of this study enabled us to understand that students were deeply engaged with the content and curriculum of our program, expanded their knowledge base about scientific concepts, used engineering-specific scientific terminologies, and engaged with the engineering design process to conceptualize, test, improvise, and problem-solve. Furthermore, this afterschool engineering education program created a safe, nurturing, and stimulating environment for students to build engineering readiness skills.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".