Clustering instructional design models based on student learning behaviors in blended learning: Big data analytics in higher education

• In Event: RTD - Big Data Analytics and the Effects of Signaling and Elaboration on Achievement

Short Description

Although the adoption of blended learning becomes the single-greatest trend in higher education today, very few cases have strategically adopted and evaluated the effectiveness of blended learning at institution-level. The clustering methods were employed to extract common instructional features among 3,112 blended learning courses in a large higher educational institution, by using real-time behavior data produced from learning management system. Finally, we discuss the implications for strategic institutional initiative for blended learning based on the data-driven decisions.

Authors

• Ji Hyun Yu, Virginia Tech

• Yeonjeong Park, Ewha Womans University

• Il-Hyun Jo, Ewha Womans University