Developing maker activities to enhance adolescents’ self-directed learning: A systematic review
Bibliographic record
Abstract
With a greater emphasis on technology and innovation, developing self-directed learners has become a predominant goal in 21 st century education. Maker and design-centric activities, including 3D printing, may provide opportunities to foster self-directed learning (SDL) skills. To identify the elements that enhance SDL in adolescents during maker tool use, a systematic review of studies that targeted SDL was conducted. The review identified three main theories used to support SDL: a) self-regulated learning (SRL), b) inquiry-based learning, and c) problem-based learning. Each framework was evaluated on the applicability to 3D printing and making activities. Further, six key characteristics of SDL environments were identified as there were commonalities amongst frameworks. These include: a) guiding supports, b) SRL components, c) inquiry and choice, d) collaboration, e) differentiation: balancing goals with abilities, and f) hypothesis testing and inquiry. Based on these results, a set of practices is proposed that teachers can implement when using making-activities in their high school classrooms. It further provides a foundation for future research on the effective integration of 3D printing as an educational tool that extends beyond behavioural engagement.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".