Inducing Stealth Assessors from Game Interaction Data

• Published in: Artificial Intelligence in Education Vol. 10331; pp. 212 - 223
• Main Authors: Min, Wookhee, Frankosky, Megan H., Mott, Bradford W., Wiebe, Eric N., Boyer, Kristy Elizabeth, Lester, James C.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Computational thinking; Deep learning; Educational games; Game-based learning environments; Stealth assessment
• ISBN: 331961424X; 9783319614243
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-61425-0_18

A key untapped feature of game-based learning environments is their capacity to generate a rich stream of fine-grained learning interaction data. The learning behaviors captured in these data provide a wealth of information on student learning, which stealth assessment can utilize to unobtrusively draw inferences about student knowledge to provide tailored problem-solving support. In this paper, we present a long short-term memory network (LSTM)-based stealth assessment framework that takes as input an observed sequence of raw game-based learning environment interaction data along with external pre-learning measures to infer students’ post-competencies. The framework is evaluated using data collected from 191 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors induced from student game-based learning interaction data outperform comparable models that required labor-intensive hand-engineering of input features. The findings suggest that the LSTM-based approach holds significant promise for evidence modeling in stealth assessment.