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arXiv 2102.07312cs.HCcs.LG
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自信的学习助手

Confidence-Aware Learning Assistant

German Research Center for Artificial Intelligence (DFKI)(德国人工智能研究中心(DFKI));Osaka Prefecture University(大阪府立大学)

Shoya Ishimaru, Takanori Maruichi, Andreas Dengel, Koichi Kise

摘要:

不仅正确性,而且自信心在提高知识质量方面起着重要作用。不理想的情境,如自信的错误知识和不自信的正确知识,阻碍了学习者修改他们的知识,因为他们并不总是容易感知的情况。为了解决这个问题,我们提出了一个系统,估计自信,同时解决多项选择题的眼动追踪,并给出反馈,哪些问题应该仔细审查。我们报告了三项研究的结果,衡量其有效性。(1)在一个有10名参与者的控制良好的数据集上,我们的方法以81%和79%的平均精度检测到了信心和不信心。(2)在20名被试的帮助下,我们观察到,在没有信心的情况下给出正确答案和有信心的情况下给出错误答案的反馈,问题的正确回答率分别提高了14%和17%。(3)我们在一所私立学校进行了大规模的数据记录(72名高中生解决了14,302个问题),以调查有效特征和所需训练样本的数量。

英文摘要:

Not only correctness but also self-confidence play an important role in improving the quality of knowledge. Undesirable situations such as confident incorrect and unconfident correct knowledge prevent learners from revising their knowledge because it is not always easy for them to perceive the situations. To solve this problem, we propose a system that estimates self-confidence while solving multiple-choice questions by eye tracking and gives feedback about which question should be reviewed carefully. We report the results of three studies measuring its effectiveness. (1) On a well-controlled dataset with 10 participants, our approach detected confidence and unconfidence with 81% and 79% average precision. (2) With the help of 20 participants, we observed that correct answer rates of questions were increased by 14% and 17% by giving feedback about correct answers without confidence and incorrect answers with confidence, respectively. (3) We conducted a large-scale data recording in a private school (72 high school students solved 14,302 questions) to investigate effective features and the number of required training samples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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