Educational data mining for discovering hidden browsing patterns using non-negative matrix factorization. Issue 7 (3rd October 2021)

• Record Type: Journal Article
• Authors: Mouri, Kousuke; Suzuki, Fumiya; Shimada, Atsushi; Uosaki, Noriko; Yin, Chengjiu; Kaneko, Keiichi; Ogata, Hiroaki
• Is Part Of: Interactive learning environments. Volume 29:Issue 7(2021)
• Page Start: 1176
• Page End: 1188
• Publication Date: 2021-10-03
• Subjects: Learning analytics -- digital textbooks -- non-negative matrix factorization -- educational data mining -- data visualization; Educational technology -- United States -- Periodicals; 371.33
• DOI: 10.1080/10494820.2019.1619594
• Languages: English
• ISSNs: 1049-4820

Abstract

ABSTRACT: 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.