DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments
DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments
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DeepStealth：利用深度学习模型在基于游戏的学习环境中进行隐形评估
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
James C. Lester
中科院分区：
文献类型：
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作者：
Wookhee Min;M. Frankosky;Bradford W. Mott;Jonathan P. Rowe;E. Wiebe;K. Boyer;James C. Lester
基于游戏的智能学习环境的一个显著特点是能够进行隐形评估。隐形评估以一种看不见的方式收集有关学生能力的信息，并能够对学生的知识做出有效的推断。我们提出了一个利用深度学习的隐形评估框架，深度学习是一系列利用深度人工神经网络的机器学习方法，用于在基于游戏的学习环境中推断学生的能力，用于中级计算思维。在与Engage的课堂学习中收集的学生互动数据以及先前的知识分数被用来训练深层网络，以预测学生的考试后表现。结果表明，使用堆叠去噪自动编码器进行预训练的深度网络具有很高的预测精度，显著优于支持向量机和朴素贝叶斯等标准分类技术。研究结果表明，深度学习在为基于游戏的智能学习环境自动诱导隐形评估模型方面显示出相当大的前景。
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 naive Bayes. The findings suggest that deep learning shows considerable promise for automatically inducing stealth assessment models for intelligent game-based learning environments.