Predictive Student Modeling in Educational Games with Multi-Task Learning

Modeling student knowledge is critical in adaptive learning environments. Predictive student modeling enables formative assessment of student knowledge and skills, and it drives personalized support to create learning experiences that are both effective and engaging. Traditional approaches to predictive student modeling utilize features extracted from VWXGHQWV¶ LQWHUDFWLRQ WUDFH GDWD WR SUHGLFW VWXGHQW WHVW performance, aggregating student test performance as a single output label. We reformulate predictive student modeling as a multi-task learning problem, modeling questions from student WHVW GDWD DV GLVWLQFW ³WDVNV ´: H GHPRQVWUDWH WKH HIIHFWLYHQHVV of this approach by utilizing student data from a series of laboratory-based and classroom-based studies conducted with a game-based learning environment for microbiology education, CRYSTAL ISLAND. Using sequential representations of student gameplay, results show that multi-task stacked LSTMs with residual connections significantly outperform baseline models that do not use the multi-task formulation. Additionally, the accuracy of predictive student models is improved as the number of tasks increases. These findings have significant implications for the design and development of predictive student models in adaptive learning environments.

Authors

• Michael Geden
• Andrew Emerson
• Jonathan Rowe
• Roger Azevedo
• James Lester

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