Bibliographic record
Abstract
Background: The emergence of a participatory culture, brought about mainly by the use of Web2.0 technology, is challenging us to reconsider aspects of teaching and learning. Adapting the learning-as-digital-game-building approach, this paper explores how new educational practices can help students build skills for the 21st century. Purpose: This paper examines elementary students' learning experiences through digital game building and playing. The following research questions guided the study: (1) What emotions do students experience during the process of building digital games for others to use?; (2) What traits do students display when they learn through digital game-building?; and (3) What do students learn from the digital game-building experience? Sample: The participants were 21 elementary students (19 boys and two girls), aged between seven and 11, who were on a summer camp at a university in Canada. Design and methods: This small-scale study made use of enactivism (Li, Clark, and Winchester, Instructional design and technology grounded in enactivism: A paradigm shift?, British Journal of Educational Technology 41, no. 3: 403–419, 2010), a new theoretical framework, as a basis for analysing the students' experiences and responses as they created computer games to teach others the concept of Issac Newton's Three Laws of Motion. Quantitative and qualitative data collected included student and parent surveys, teacher and student interviews, field observations and the digital games created by the students. Data were subjected to quantitative and thematic analyses. Results: The results indicated that only a small minority of students reported never feeling the positive emotions excited/happy or smart/proud during the process of building digital games. In addition, analysis suggested that creativity, engagement and new identity were the three salient traits displayed by the students when learning by digital game-building. There was also evidence that students increased their understanding of the subject matter in question (mathematics, science and technology) and enhanced their general problem-solving abilities through the process. Conclusions: This small-scale study suggests that student engagement in the game-building experience can enhance not just the learning of the game design process but also subject matter and generic skills. Thus, the learning-as-building approach can empower students to ‘take over the technology’ and become creators rather than passive consumers.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".