Making in the Middle Years: A Scoping Review Exploring Outcomes of Maker Activities in Educational Contexts for Students and Teachers
Bibliographic record
Abstract
As generative AI reshapes the educational landscape, schools face pressing questions about how to foster more agentive, collaborative, and interdisciplinary learning. This scoping review synthesizes insights from 68 research articles exploring maker-centered projects conducted with students from grades four to eight (age 9-13) in educational contexts. Analyses synthesize key design elements that shape effective making activities for students of this age group that follow what we identify as a three as a three-phase structure of implementation: 1) inspiration and preparation, 2) implementation and creation, and 3) presentation and recontextualization. This paper also reports on the outcomes of making for both students and teachers. Our findings suggest that making, as an intervention for students, supports a range of important disciplinary, social, affective, and metacognitive outcomes. For teachers, engaging in maker-centered learning enables pedagogical decisions that move them away from traditional, teacher-centered practices and toward experimental, self-directed and collaborative pedagogies tailored to student needs. Additionally, findings of both affective and social outcomes were also reported for teachers. In an era of rapid technological change, evidence from this study suggests that making creates meaningful and impactful learning opportunities for students and teachers that can be tailored to students’ social, developmental, and cultural strengths and needs.
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 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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".