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Title: Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics
A key affordance of game-based learning environments is their potential to unobtrusively assess student learning without interfering with gameplay. In this paper, we introduce a temporal analytics framework for stealth assessment that analyzes students' problem-solving strategies. The strategy-based temporal analytic framework uses long short-term memory network-based evidence models and clusters sequences of students' problem-solving behaviors across consecutive tasks. We investigate this strategy based temporal analytics framework on a dataset of problem solving behaviors collected from student interactions with a game-based learning environment for middle school computational thinking. The results of an evaluation indicate that the strategy-based temporal analytics framework significantly outperforms competitive baseline models with respect to stealth assessment predictive accuracy.
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Award ID(s):
1640141 1138497
Publication Date:
Journal Name:
International Conference on Educational Data Mining
Page Range or eLocation-ID:
208 - 218
Sponsoring Org:
National Science Foundation
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