Developing Awareness of Connections Between Science, Technology and the Environment through Participation in a Game-Like Approach to Curriculum
Bibliographic record
Abstract
If we are ever to achieve the goal of redirecting technological development along more environmentally and socially responsible lines we need to provide students with opportunities to examine the processes of science and technology, the possible costs and consequences of this work, and the choices available to us. The purpose of this study is to examine students’ developing environmental literacy in the Heat Game. The Heat Game is a game-like approach to curriculum designed to support students developing their environmental literacy while addressing curriculum requirements for a grade 7 unit, Heat in the Environment, in Ontario, Canada. Based on principles of learning in video-games, the Heat Game recreates a simulation of a science and technology setting wherein student-participants role-play junior professional scientists communicating online within a community of scientists. In their roles they work to solve a virtual challenge to design energy-efficient housing, and reflect on possible environmental and societal consequences of their designs. This study, which is part of a larger design-based research study of The Heat Game, uses discourse analysis to examine online role-playing conversations generated within the game as well as online correspondence between students and their teacher after the game. The study demonstrates that through actions and online conversations in the Heat Game students developed their environmental literacy, including understandings of the relationships between science, technology and the environment and the consequences of choices we make. In addition the study provides support for the ideas of Gee (2007) regarding how we can use the principles of learning in video games to create opportunities for students to develop a literacy; in this case environmental literacy.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".