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

- Authors: Joonhyung Bae, K. Eum, H. Kwon, S. Lee, J. Nam, Y. Y. Doh
- Venue: Extended Abstracts 2022 CHI Conf. on Human Factors in Computing Systems, New Orleans, LA, USA, Apr. 30–May 5, 2022
- Year: 2022
- Type: Conference Paper
- DOI: https://doi.org/10.1145/3491101.3516487
- Publisher page: https://dl.acm.org/doi/10.1145/3491101.3516487
- PDF: https://drive.google.com/file/d/1qIxLvzgjNvwYG-1GYh5wQsjAB2kZ4Qva/view?usp=drive_link
- Keywords: educational game, machine learning, non-major students, HCI, gamification
- Page: https://joonhyungbae.com/publication/classy-trash-monster-2022

## In one sentence

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

비전공 대학생에게 머신러닝과 '데이터 의존성'을 가르치는 교육용 게임. 게임 활동이 ML 파이프라인(데이터 수집→학습→분류)을 그대로 따른다.

## 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 / 비전공자 대상 AI·머신러닝 교육(AI 리터러시) 도구를 설계할 때
- You need prior serious games that teach ML concepts / 교육용 게임(serious game)으로 ML 개념을 가르친 선행 사례가 필요할 때
- You want learners to experience how training data shapes model output (data dependency, bias) / 학습 데이터가 모델 결과에 미치는 영향(데이터 의존성·편향)을 체험적으로 가르치려 할 때
- You study the role of debriefing in game-based learning / 게임 기반 학습에서 디브리핑의 역할을 다룰 때

## Related

- Artwork: https://joonhyungbae.com/art/classy-trash-monster-2022

## How to 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

```bibtex
@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}
}
```