Mining learning behavioral patterns of students by sequence analysis in cloud classroom

Sanya Liu, Zhenfan Hu, Xian Peng, Zhi Liu, H. N.H. Cheng, Jianwen Sun

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In a MOOC environment, each student's interaction with the course content is a crucial clue for learning analytics, which offers an opportunity to record learner activity of unprecedented scale. In online learning, the educators and the administrators need to get informed with students' learning states since the performance of unsupervised learning style is difficult to control. Learning analytics considered as a key process is to provide students and educators with evidence-based, analytical and contextual outcomes in a way of making sense of their learning engagements. In this conceptual framework, this manuscript per the authors intends to adopt sequential analysis method to exploit students' learning behavior patterns in Cloud classroom (an online course platform based on MOOC). Moreover, this research also compares the behavioral patterns of four grade levels in a university, with the purpose of finding the most key behavioral patterns of each grade group.

Original languageEnglish
Pages (from-to)15-27
Number of pages13
JournalInternational Journal of Distance Education Technologies
Issue number1
Publication statusPublished - Jan 1 2017
Externally publishedYes


  • Behavioral Pattern
  • Cloud Classroom
  • Learning Analytics
  • Massive Open Online Courses
  • Sequential Analysis

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Computer Networks and Communications


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