Educational data mining for discovering hidden browsing patterns using non-negative matrix factorization

Kousuke Mouri, Fumiya Suzuki, Atsushi Shimada, Noriko Uosaki, Chengjiu Yin, Keiichi Kaneko, Hiroaki Ogata

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1176-1188
Number of pages13
JournalInteractive Learning Environments
Issue number7
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications


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