Journal
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017
Volume 10331, Issue -, Pages 212-223Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-61425-0_18
Keywords
Game-based learning environments; Stealth assessment; Deep learning; Computational thinking; Educational games
Categories
Funding
- National Science Foundation [CNS-1138497, DRL-1640141]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available