Automated Evaluation of Student Comments on Their Learning Behavior
Automated Evaluation of Student Comments on Their Learning Behavior
复制标题
自动评估学生对其学习行为的评论
DOI：
10.1007/978-3-642-41175-5_14
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Tsunenori Mine
中科院分区：
文献类型：
--
作者：
Kazumasa Goda;Sachio Hirokawa;Tsunenori Mine
学习评语是解读学生理解状况的重要资源。[Gouda 2011]中介绍的PCN方法从时间序列的角度分析了学生的态度。评论的每个句子被手动分类为P，C，N或O句子之一。P（previous）表示上课前的学习活动，C（current）表示上课期间的理解或成就，N（next）表示下一堂课之前的学习活动计划或目标。本文应用支持向量机（SVM）来预测句子所属的类别。使用4，086个句子进行了实证评估。通过对各类特征词的选择，预测性能令人满意，P、C、N和O的F-测度分别为0.8203、0.7352、0.8416和0.8612。
Learning comments are valuable sources of interpreting student status of understanding. The PCN method introduced in [Gouda2011] analyzes the attitudes of a student from a view point of time series. Each sentence of a comment is manually classified as one of P,C,N or O sentence. P(previous) indicates learning activities before the classtime, C(current) represents understanding or achievements during the classtime, and N(next) means a learning activity plan or goal until next class. The present paper applies SVM(Support Vecotor Machine) to predict the category to which a given sentence belongs. Empirical evaluation using 4,086 sentences was conducted. By selecting feature words of each category, the prediction performance was satisfactory with F-measures 0.8203, 0.7352, 0.8416 and 0.8612 for P,C,N and O respectively.