Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom
Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom
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DOI：
10.1145/3458380.3458404
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发表时间：
2021-02
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通讯作者：
Meishu Song;Kun Qian;Bin Chen;Keiju Okabayashi;Emilia Parada-Cabaleiro;Zijiang Yang;Shuo Liu;Kazumasa Togami;I. Hidaka;Yueheng Wang;Björn Schuller;Yoshiharu Yamamoto
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作者：
Meishu Song;Kun Qian;Bin Chen;Keiju Okabayashi;Emilia Parada-Cabaleiro;Zijiang Yang;Shuo Liu;Kazumasa Togami;I. Hidaka;Yueheng Wang;Björn Schuller;Yoshiharu Yamamoto
认知理论认为，学生在学习环境中的思维体现在身体活动中。在这方面，最近的研究表明，信号水平的手写动态可以区分学习性能。虽然机器学习被认为可以检测多模态模态与特定学习过程的相关性，但深度学习的使用尚未得到足够的关注。考虑到这一点，我们通过分析智能英语教室中学生的3D（包括笔画频率）手写信号，利用基于深度卷积神经网络（CNN）的回归模型构建了一个小组工作表现预测系统。在评定他们的熟练程度时，采用了他们的口语表现。学生们分组一起学习。3D（2D书写坐标加频率）手写数据集（3D-Writing-DB）是通过一个名为“创意数字空间”的协作平台收集的。我们在英语讨论会期间从平板电脑上提取了3D手写信号。之后，专业英语教师对英语演讲进行注释（值从0 - 5不等）。我们的实验结果表明，通过使用深度学习，可以成功地从物理手写特征预测小组工作绩效，如我们的最佳结果所示，即。例如，0.32在回归评估中，应用RMSE进行评估。
Embodied cognition theory states that students thinking in a learning environment is embodied in physical activity. In this regard, recent research has shown that signal-level handwriting dynamics can distinguish learning performance. Although machine learning has been considered to detect how multimodal modalities correlate to specific learning processes, the use of deep learning has received insufficient attention. With this in mind, we build a Group Work Performance Prediction system from analysis of 3D (including strokes frequency) handwriting signals of students in a smart English classroom, with deep convolutional neuronal network (CNN) based regression models. For labelling of their proficiency level, their spoken language performance is being used. The students were working together in groups. A 3D (2D writing coordinates plus frequency) handwriting dataset (3D-Writing-DB) was collected through a collaboration platform known as ‘creative digital space’. We extracted the 3D handwriting signal from a table tablet during English discussion sessions. Afterwards, professional English teachers annotated the English speech (values vary from 0 - 5). Our experimental results indicate that group work performance can be successfully predicted from physical handwriting features by using deep learning, as shown by our best result, i. e., 0.32 in regression assessment by applying RMSE for evaluation.