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.
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea grant funded by the Ministry of Education, Science, and Technology (NRF no. 2013S1A5A2A0304410 ).
© 2015 Elsevier Inc. All rights reserved.
- Academic analytics
- Blended learning
- Educational data mining
- Higher education
- Latent class analysis