Classy trash monster: An educational game for teaching machine learning to non-major students

In one sentence

Educational game teaching machine learning and data dependency to non-CS students; gameplay follows the ML pipeline (collect data → train → classify).

Abstract

As machine learning (ML) became more relevant to our lives, ML education for college students without technical background arose important. However, not many educational games designed to suit challenges they experience exist. We introduce an educational game Classy Trash Monster (CTM), designed to better educate ML and data dependency to non-major students who learn ML for the first time. The player can easily learn to train a classification model and solve tasks by engaging in simple game activities designed according to an ML pipeline. Simple controls, positive rewards, and clear audiovisual feedback makes game easy to play even for novice players. The playtest result showed that players were able to learn basic ML concepts and how data can impact model results, and that the game made ML feel less difficult and more relevant. However, proper debriefing session seems crucial to prevent misinterpretations that may occur in the learning process.

Source: CHI 2022 Extended Abstracts (via OpenAlex)

Key facts

• Target: non-major undergraduates learning ML for the first time
• Playtest: players learned basic ML concepts and how data affects model results
• Debriefing found crucial to prevent misconceptions
• CHI 2022 Extended Abstracts

When this paper is relevant

• You design AI / machine-learning literacy tools for non-technical learners
• You need prior serious games that teach ML concepts
• You want learners to experience how training data shapes model output (data dependency, bias)
• You study the role of debriefing in game-based learning

Related

• Artwork: Classy Trash Monster

Cite

Joonhyung Bae, K. Eum, H. Kwon, S. Lee, J. Nam, Y. Y. Doh (2022). Classy trash monster: An educational game for teaching machine learning to non-major students. Extended Abstracts 2022 CHI Conf. on Human Factors in Computing Systems, New Orleans, LA, USA, Apr. 30–May 5, 2022. https://doi.org/10.1145/3491101.3516487

@inproceedings{Bae2022classy,
  title = {Classy trash monster: An educational game for teaching machine learning to non-major students},
  author = {Joonhyung Bae and K. Eum and H. Kwon and S. Lee and J. Nam and Y. Y. Doh},
  booktitle = {Extended Abstracts 2022 CHI Conf. on Human Factors in Computing Systems, New Orleans, LA, USA, Apr. 30–May 5, 2022},
  year = {2022},
  doi = {10.1145/3491101.3516487},
  url = {https://doi.org/10.1145/3491101.3516487}
}

Machine-readable copy: /papers/classy-trash-monster-2022.md