Serious Game Leverages Productive Negativity to Facilitate Conceptual Change in Undergraduate Molecular Biology
Bibliographic record
Abstract
We designed a serious game, MolWorlds, to facilitate conceptual change about molecular emergence \nby using game mechanics (resource management, immersed 3rd person character, sequential level \nprogression, and 3-star scoring system) to encourage cycles of productive negativity. We tested the \nvalue-added effect of game design by comparing and correlating pre- and post-test misconceptions, \ninteraction statistics, and engagement in the game with an interactive simulation that used the same \ngraphics and simulation system but lacked gaming elements. We tested first-, second-, and third-year \nbiology students’ misconceptions at the beginning and end of the semester (n = 526), a subset of \nwhom played either the game (n = 20) or control (n = 20) for 30 minutes prior to the post-test. A 3x3 \nmixed model ANOVA revealed that, while educational level (first-, second-, or third-year biology) \ndid not influence misconceptions from pre-test to post-test, the intervention type (no intervention, \nsimulation, or game) did (p<.001). Pairwise comparisons showed that participants exposed to the \ninteractive simulation (p = .007), as well as those exposed to the game (p<.001), lost significantly \nmore misconceptions in comparison to those who did not receive any intervention, while adjusting \nfor educational level. A trending difference was found between the simulation group and the gaming \ngroup (p = .084), with the gaming group resolving more misconceptions. Quantitative analysis of \nclick-stream data revealed the greater exploratory freedom of the control simulation, with greater \naccessibility to individuals who do not play games on a regular basis. However, qualitative analysis of \ngameplay data showed that MolWorlds-players experienced significantly more instances of productive \nnegativity than control-users (p<.001) and that a trending relationship exists between the quality of \nproductively negative events and lower post-test misconceptions (p = .066).
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".