THE ROLE OF GAME JAMS IN DEVELOPING INFORMAL LEARNING OF COMPUTATIONAL THINKING: A CROSS-EUROPEAN CASE STUDY
Bibliographic record
Abstract
This paper will present a cross-European experience of game jams as part of a Horizon 2020 funded project: No-one Left Behind (NOLB).The NOLB project was created to unlock inclusive gaming creation and experiences in formal learning situations from primary to secondary level, particularly for children at risk of social exclusion.The project has engendered the concept of game jams, events organised with the aim of designing and creating small games in a short time-frame around a central theme.Game jams can support engagement with informal learning beyond schools across a range of disciplines, resulting in an exciting experience associated with strong, positive emotions which can significantly support learning goals.This paper will disseminate experience of two cross-European game jams; the first a pilot and the second having over 95 submissions from countries across Europe, America, Canada, Egypt, the Philippians and India.Data collected through these games jams supports that coding, designing, reflection, analysing, creating, debugging, persevering and application, as well as developing computational thinking concepts such as decomposition, using patterns, abstraction and evaluation.The notion of game jams provides a paradigm for creating both formal and informal learning experiences such as directed learning experience, problem-solving, hands-on projects, working collaboratively, and creative invention, within a learner-centred learning environment where children are creators of their own knowledge and learning material.This paper explores the use of a mobile app, Pocket Code, in schools across Europe in two game jams during the academic year 2015-16 with children aged 11-18.Pocket Code provides an environment which supports learners in easily creating apps directly on their smart-phones and tablets through a visual Lego®-style programming language where users can put code bricks together to form scripts.We draw on a range of data to support how game jams can be used as a design research method to observe the creation of knowledge in fast-paced, collaborative environments across a range of disciplines.Our data evidences that learners can be more motivated through game jams and that learners who are less likely to create games are nevertheless more engaged in a game jam setting.We will also present the frameworks for 3 games from different disciplines: Chemistry, Languages, and Mathematics.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".