Clustering blended learning courses by online behavior data case study in a Korean higher education institute
- 주제(키워드) Academic analytics , Blended learning , Educational data mining , Higher education , Latent class analysis
- 등재 SSCI, SCOPUS
- 발행기관 Elsevier Ltd
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000120573
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.iheduc.2015.11.001
초록/요약
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. © 2015 Elsevier Inc. All rights reserved.
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