Educational data mining for discovering hidden browsing patterns using non-negative matrix factorization
Educational data mining for discovering hidden browsing patterns using non-negative matrix factorization
复制标题
使用非负矩阵分解发现隐藏浏览模式的教育数据挖掘
DOI：
10.1080/10494820.2019.1619594
复制
发表时间：
2019
影响因子：
5.4
通讯作者：
Ogata Hiroaki
中科院分区：
文献类型：
--
作者：
Mouri Kousuke;Suzuki Fumiya;Shimada Atsushi;Uosaki Noriko;Yin Chengjiu;Kaneko Keiichi;Ogata Hiroaki
本文描述了一种在没有眼动追踪技术的情况下收集学习者在数字教科书中浏览的页面部分数据的方法。在之前对数字教科书系统的研究中，如果不使用眼动仪，很难收集此类数据。然而，眼动仪花费了大量预算。我们提出的系统在学习者浏览数字教科书中的文本之前，通过掩码处理自动隐藏数字教科书中的文本。如果他们单击隐藏的文本，系统就会摆脱遮罩，文本会逐个字母地显示。我们使用 NMF 从收集的日志中发现学习者的浏览模式。我们进行了评估实验，以检验我们的系统在吸引力、可理解性和增强思维方面的有效性，并发现学习者的浏览模式。结果发现我们的方法可以增强思维能力。还发现了学习成绩高的勤奋学习者的浏览模式。
This paper describes a method to collect data of which section of pages learners were browsing in digital textbooks without eye-tracking technologies. In previous researches on digital textbook systems, it was difficult to collect such data without using eye-tackers. However, eye-trackers cost a massive budget. Our proposed system automatically hides the texts in the digital textbooks with mask processing before the learners browse the texts in the digital textbooks. If they click the hidden texts, the system gets rid of the masks and the texts appear letter by letter. We used NMF to discover learners’ browsing patterns from the collected logs. Evaluation experiments were conducted to examine the effectiveness of our system in terms of fascination, understandableness and enhancement of thinking and to discover learners’ browsing patterns. It was found that our method could enhance thinking skills. A browsing pattern of diligent learners with high learning achievements was also found.