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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
Appears in Collections:ISLS Annual Meeting 2022

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