Clustering blended learning courses by online behavior data case study in a Korean higher education institute

Yeonjeong Park, Ji Hyun Yu, Il Hyun Jo

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

Abstract

Blended learning (BL) is recognized as one of the major trends in higher education today. To identify how BL has been actually adopted, this study employed a data-driven approach instead of model-driven methods. Latent Class Analysis method as a clustering approach of educational data model-driven methods. Latent Class Analysis method as a clustering approach of educational data mining was employed to extract common activity features of 612 courses in a large private university located in South Korea by using online behavior data tracked from Learning Management System and institution's course database. Four unique subtypes were identified. Approximately 50% of the courses manifested inactive utilization of LMS or immature stage of blended learning implementation, which is labeled as Type I. Other subtypes included Type C - Communication or Collaboration (24.3%), Type D - Delivery or Discussion (18.0%), and Type S - Sharing or Submission (7.2%). We discussed the implications of BL based on data-driven decisions to provide strategic institutional initiatives.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalInternet and Higher Education
Volume29
DOIs
StatePublished - 1 Apr 2016

Keywords

  • Academic analytics
  • Blended learning
  • Educational data mining
  • Higher education
  • Latent class analysis

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