Correlation of grade prediction performance and validity of self-evaluation comments
Correlation of grade prediction performance and validity of self-evaluation comments
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DOI：
10.1145/2512276.2512294
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Kazumasa Goda;S. Hirokawa;Tsunenori Mine
中科院分区：
文献类型：
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作者：
Kazumasa Goda;S. Hirokawa;Tsunenori Mine
掌握学生的上课态度和学习情况，反馈给每个学生，是教育的基础。Goda等人提出了PCN方法，用于从学生自由撰写的评论中估计学习情况[6，7]。PCN方法将评论分为P（上一个）、C（当前）和N（下一个）三个项目。他们指出，学生的最终结果和项目C的描述性内容的有效性之间的相关性，这与对课程的理解和对课程的学习态度有关。然而，高年级学生的预测成绩较差是他们工作中遗留的一个问题。本文提出了PCN成绩的两种利用方式：评价效度水平的确定和学生期末成绩的预测。为了验证所提出的利用方式，我们进行了两个实验。首先，我们采用多元回归分析来计算PCN分数，以确定每个观点的有效性水平。PCN得分高的学生被认为是那些恰当地描述了他们的学习态度的学生。我们还将机器学习方法SVM（支持向量机）应用于学生的评论，预测他们在S，A，B，C和D五个等级中的最终结果。实验结果表明，学生评论的PCN得分越高，学生成绩的预测性能越高。
To grasp a student's lesson attitude and learning situation and to give a feed back to each student are educational foundations. Goda et al. proposed the PCN method to estimate a learning situation from a comment freely written by students[6, 7]. The PCN method categorizes comments into three items of P (previous), C(current) and N(next). They pointed out a correlation between the student's final results and the validity of a descriptive content of item C, that is something related to understanding of the lesson and learning attitudes to the lesson. However, a problem left in their work is the badness of performance in prediction for upper grade students. This paper proposes two manners of utilization of PCN scores: the validity level determination for assessment, and for prediction performance of students' final grades. In order to validate the proposed manners of utilization, we conducted two experiments. First, we employed multiple regression analysis to calculate PCN scores that determine the validity level with respect to each viewpoint. Students who wrote comments with a high PCN score are considered as those who describe their learning attitude appropriately. We also applied a machine learning method SVM (support vector machine) to students' comments for predicting their final results in five grades of S, A, B, C and D. Experimental results illustrated that as comments of students get higher PCN scores, the prediction performance of the students' grades becomes higher.