Towards better affect detectors: effect of missing skills, class features and common wrong answers
Towards better affect detectors: effect of missing skills, class features and common wrong answers
复制标题
打造更好的影响检测器：缺失技能、职业特征和常见错误答案的影响
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Cristina Heffernan
中科院分区：
文献类型：
--
作者：
Yutao Wang;N. Heffernan;Cristina Heffernan
经过充分研究的Baker等人，研究人员使用ASSISTments数据集的无聊，沮丧，困惑和参与集中的影响检测器来预测州考试成绩，大学入学率，甚至学生是否主修STEM领域。在本文中，我们提出了三种尝试，以改善电流影响检测器。第一次尝试分析了数据集中缺少技能标签对影响检测器准确性的影响。结果显示，在正确标记缺失的技能值后，性能略有改善。第二次尝试添加了四个与学生类相关的功能，用于功能选择。第三次尝试增加了两个特征，描述了学生常见错误答案的信息，用于特征选择。实验结果表明，四个检测器中有两个通过添加新的特征得到了改进。
The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first attempt analyzed the effect of missing skill tags in the dataset to the accuracy of the affect detectors. The results show a small improvement after correctly tagging the missing skill values. The second attempt added four features related to student classes for feature selection. The third attempt added two features that described information about student common wrong answers for feature selection. Result showed that two out of the four detectors were improved by adding the new features.