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https://repository.isls.org//handle/1/8747| Title: | Predicting Learning Gains in an Educational Game Using Feature Engineering and Machine Learning |
| Authors: | Rahimi, Seyedahmad Fulwider, Curt Jiang, Shiyan Shute, Valerie J. |
| Keywords: | Learning Sciences |
| Issue Date: | 2022 |
| Publisher: | International Society of the Learning Sciences |
| Citation: | Rahimi, S., Fulwider, C., Jiang, S., & Shute, V. J. (2022). Predicting learning gains in an educational game using feature engineering and machine learning. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 2124-2125). International Society of the Learning Sciences. |
| Abstract: | In this study, we use feature engineering and machine learning (ML) to develop a regression model to predict students’ learning gain while playing an educational game. Specifically, this study used the log data from 199 students’ gameplay to build a logistic regression model. The model we created includes 14 features with an accuracy of .61. |
| Description: | Poster |
| URI: | https://dx.doi.org/10.22318/icls2022.2124 https://repository.isls.org//handle/1/8747 |
| Appears in Collections: | ISLS Annual Meeting 2022 |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ICLS2022_2124-2125.pdf | 287.48 kB | Adobe PDF | View/Open |
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