Videogames and Complexity Theory: Learning through Game Play
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it