Video games as a tool for ecological learning: the case of Animal Crossing
Bibliographic record
Abstract
Abstract Amidst lockdown policies in response to the COVID‐19 pandemic, many used video games as a method to maintain a connection with others while ensuring social distancing. A new edition of the Animal Crossing series of games had been released in March 2020 and beat sales and downloads records. The game focuses on living in a natural environment, building a house and a village, as well as capturing, exhibiting, and selling species to progress. Here we examine whether players gain species identification skills and whether it is transferred to real‐life models. We used the results from a survey conducted from the end of March to early April 2020 on 200 people (72 players and 128 nonplayers of Animal Crossing). Participants were first asked to rank their personal interest in nature and then to identify species from photos. The photos displayed both organisms present in the game and organisms that were not. We expected players to obtain a slightly higher score than nonplayers for questions related to the species present in the game and a similar score in both groups for questions related to species not present in the game. Multivariate analyses (multiple linear regression and principal components analysis [PCA]) showed that players were better than nonplayers at identifying real‐life species that were present in the game. The role of the species in the game design impacts the ability to identify the species in real life, such as plants having mainly a role of ornamentation. Additionally, this study suggests that survey participants could correctly assess their naturalistic knowledge in general. This article shows that video games can help enhance ecological learning, improve organisms identification, and might be used as a tool for education in conservation biology.
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How this classification was reachedexpand
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) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".