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

Title
Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute
Authors
Park, YeonjeongYu, Ji HyunJo, Il-Hyun
Ewha Authors
조일현박연정
SCOPUS Author ID
조일현scopus; 박연정scopus
Issue Date
2016
Journal Title
INTERNET AND HIGHER EDUCATION
ISSN
1096-7516JCR Link1873-5525JCR Link
Citation
vol. 29, pp. 1 - 11
Keywords
Blended learningHigher educationAcademic analyticsEducational data miningLatent class analysis
Publisher
ELSEVIER SCIENCE INC
Indexed
SSCI; SCOPUS WOS scopus
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 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. (C) 2015 Elsevier Inc. All rights reserved.
DOI
10.1016/j.iheduc.2015.11.001
Appears in Collections:
사범대학 > 교육공학과 > Journal papers
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