CREATIVE SPACES TO DEVELOP DIGITAL COMPETENCE: CHALLENGES IN A UNIVERSITY COURSE
Bibliographic record
Abstract
In the province of Quebec, Canada, the government has published a Digital Action Plan (MEES, 2018) aimed at integrating and leveraging digital technology for the success of all students and citizens.The Plan identifies creative labs as one of the global trends in education.Inspired by third places (Oldenburg, 1999 ;Tremblay et Krauss, 2019) and makerspaces (Hatch, 2014), creative spaces allow people to make, transform, and equip themselves, as well as participate, share, and learn.These actions support the democratizing effect of the maker movement (Hatch, 2014) as well as the development of people's agency (Blikstein, 2013).In the wake of the Plan, the government released a Digital Competency Framework (MEES, 2019), a local way of interpreting 21st century skills.The Framework identifies dimensions deemed essential to learning and growing in the 21st century for students and faculty members (MEES, 2019).This competency has quickly found its place in the "Competency Referential for the Teaching Profession."In order to train future teachers, a course was developed in the bachelor's degree in primary education in Quebec, allowing students to address dimensions of the competency that were previously absent from their training.Thus, the course "Creative Technologies and Networked Learning in Education" is in line with the Plan, which emphasizes that the educational system must ensure the development of the competencies essential to tomorrow's citizens.The focus of the course is the purpose and possibilities of creative spaces.One of the issues that quickly became apparent was the challenge of fitting the creative space and its informal learning into the formal context of an educational program.In its reflective aspect, the course addressed pedagogical innovation.The presentation will relate how twenty students negotiated a collective definition of pedagogical innovation.On a practical level, networked learning was at the heart of the actions and projects.Particular attention was paid to the production of pedagogical objects or the improvement of educational processes.Creative spaces, their tools or ways of doing things, were at the heart of the course activity.Thus, activities such as visits of creative spaces and the exploration of virtual reality supported an ambitious collaborative production project with sixth-grade students.The paper will provide an opportunity to recount, in an autopraxeological way (St-Arnaud, 2003), the experience of the first iteration of a course on pedagogical innovation that focused on the integration of creative spaces.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".