Videogames and Complexity Theory: Learning through Game Play
Bibliographic record
Abstract
The rich virtual worlds of videogames create powerful contexts for learning. In game worlds, as discussed by Shaffer, Halverson, Squire, and Gee (2004), “learners can understand complex concepts without losing the connection between abstract ideas and the real problems they can be used to solve” (p.5). Games are most powerful – and most complex – when they are “personally meaningful, experiential, social, and epistemological all at the same time” (Shaffer et al, 2004, p.3). In this paper we will suggest how complexity theory (Davis & Sumara, 2006; Waldrop, 1992) provides a framework that enabling us to understand learning as a complex and emergent process, an ongoing fluid relationship between personal knowing and collective knowledge as a learner/player observes and acts in the observed world. Learning skills in games becomes a process of ‘perception-action coupling’ (Chow et al., 2007; W. E. Davis & Broadhead, 2007; Renshaw, Davids, Shuttleworth, & Chow, 2008), where players’ capacity to understand game play and to act effectively is enabled through interaction in the game, discussion with other players, and prior understandings. As learners adapt to the perceived world in a self-organizing process, they develop a better relational connection to the perceived world, their task goals, and the actions and goals of others.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".