Sciweavers

Share
AIED
2015
Springer

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

3 years 10 days ago
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...
Wookhee Min, Megan Hardy Frankosky, Bradford W. Mo
Added 14 Apr 2016
Updated 14 Apr 2016
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
Comments (0)
books