Learning to innovate: Students and teachers constructing collective innovation practices in a primary school’s makerspace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it