Learning to innovate: Students and teachers constructing collective innovation practices in a primary school’s makerspace
Bibliographic record
Abstract
The need to foster citizens’ innovation skills is widely recognized. Although current research acknowledges the potential of makerspaces to promote innovation activities, research still lacks an understanding of underlying mechanisms that can lead the creation of innovations in makerspaces by students. Moreover, research to date has overlooked how innovation practices are formed in K–12 makerspaces. In this sociocultural study, we used ethnographic video data from a Finnish primary school’s makerspace and applied methods of abductive Video Data Analysis to investigate how innovation practices are constructed in first to sixth grade students’ and teachers’ interactions. The results of this study show that the innovations created by the students in the makerspace were an outcome of students’ and teachers’ collective innovation practices. The study provides a typology of these collective innovation practices, namely: taking joint action to innovate, navigating a network of resources, and sustaining innovation activities. Further, our results reveal that the collective actions encouraged students to use skills deemed to be important for innovation creation. Also, adding to existing research knowledge, our results reveal two mechanisms that potentially promote students’ learning to innovate. These mechanisms include the teachers’ orientation to facilitating open-ended STEAM projects and practices that emphasize students’ ownership over their personal projects.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".