DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments

A distinctive feature of intelligent game-based learning environments is their capacity for enabling stealth assessment. Stealth assessments gather information about student competencies in a manner that is invisible, and enable drawing valid inferences about student knowledge. We present a framework for stealth assessment that leverages deep learning, a family of machine learning methods that utilize deep artificial neural networks, to infer student competencies in a game-based learning environment for middle grade computational thinking, ENGAGE. Students’ interaction data, collected during a classroom study with ENGAGE, as well as prior knowledge scores, are utilized to train deep networks for predicting students’ post-test performance. Results indicate deep networks that are pre-trained using stacked denoising autoencoders achieve high predictive accuracy, significantly outperforming standard classification techniques such as support vector machines and naïve Bayes. The finding...

• Type: Journal
• Year: 2015
• Where: AIED
• Authors: Wookhee Min, Megan Hardy Frankosky, Bradford W. Mott, Jonathan P. Rowe, Eric N. Wiebe, Kristy Elizabeth Boyer, James C. Lester
• Keywords: AIED 2015, Artificial Intelligence