Game and Simulation Stimulate Conceptual Change about Molecular Emergence in Different Ways, with Potential Cultural Implications
Bibliographic record
Abstract
Many undergraduate students hold robust misconceptions about the emergent nature of molecular processes, believing them to be directed rather than random. Interactive simulations might help transform such misconceptions by visualizing stochastic processes in a time-independent medium and enabling students to manipulate the environment to test their naïve hypotheses. Furthermore, game-based learning (GBL) might enhance the effectiveness of such simulations by promoting productive negativity (PN), i.e., learning from failure. In a randomized controlled trial with pre-test, post-test and delayed one-year follow-up, undergraduates (n = 84) engaged with either a GBL or interactive simulation (SIM) environment for 20–45 min and were compared to a baseline group (n = 138). GBL (p = 0.035) and SIM (p = 0.069) resolved more misconceptions than baseline but did not differ from each other (p = 0.992). GBL group also trended toward more positive long-term conceptual change. In-game interactions generated in response to PN were predictive of conceptual change in the GBL group alone, suggesting that PN may only be effective when supported by game design. Participants’ native English-speaking status had a moderating effect, with native-speakers performing well in GBL and poorly in SIM environment, while the opposite was true for non-native-speakers, which, as discussed herein, may be aligned with cultural differences in acceptability of GBL. The GBL intervention generated longer voluntary use (p = 0.005), especially amongst frequent game-players. The results inform how GBL/SIM approaches can implement PN as a mechanism for conceptual change about molecular emergence.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".