Sequence Pattern Mining for the Identification of Reading Behavior based on SQ3R Reading Strategy

Authors

  • Owen H.T. LU College of Computer Science, National Pingtung University, Pingtung City, Taiwan Author
  • Anna Y.Q. HUANG Computer Science & Information Engineering, National Central University, Taoyuan City, Taiwan Author
  • Che-Yu KUO Computer Science & Information Engineering, National Central University, Taoyuan City, Taiwan Author
  • Irene Y.L. CHEN Department of Accounting, National Changhua University of Education, Changhua City, Taiwan Author
  • Stephen J.H. YANG Department of Accounting, National Changhua University of Education, Changhua City, Taiwan Author

Abstract

SQ3R (survey, question, read, recite, and review) is an efficient reading method that has confirmed the benefits of learning performance in numerous studies. To demonstrate that the e-book reading environment can also obtain the benefits of SQ3R, we conducted a course with 60 students, classify students into two groups based on the SQ3R ability. The results show that in the context of e-book reading, students' behavior: (1) create a memo and modify it regarding the reviewing content (CMeP) and (2) create a memo and modify it at the next login (CMeO) is related to recite and review steps in SQ3R. Furthermore, the above two e-book reading behaviors are positively related to students' learning performance and learning engagement.

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Published

2020-11-23

How to Cite

Sequence Pattern Mining for the Identification of Reading Behavior based on SQ3R Reading Strategy. (2020). International Conference on Computers in Education, 307-311. https://library.apsce.net/index.php/ICCE/article/view/3936