TY - JOUR
T1 - Video learning analytics
T2 - Investigating behavioral patterns and learner clusters in video-based online learning
AU - Yoon, Meehyun
AU - Lee, Jungeun
AU - Jo, Il Hyun
N1 - Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea ( NRF-2020S1A5C2A04092451 ).
Publisher Copyright:
© 2021
PY - 2021/6
Y1 - 2021/6
N2 - Video-based online learning is becoming commonplace in higher education settings. Prior studies have suggested design principles and instructional strategies to boost video-based learning. However, little research has been done on different learner characteristics, such as how learners behave, what behavioral patterns they exhibit, and how different they are from each other. To fill this research gap in student-video interaction, we employed learning analytics to obtain useful insights into students' learning in the context of video-based online learning. From 11 log behaviors represented by log data from 72 college students, four behavioral patterns were identified while students learned from videos: browsing, social interaction, information seeking, and environment configuration. Based on the behavioral patterns observed, participants were classified into two clusters. Participants in the active learner cluster exhibited frequent use of social interaction, information seeking, and environment configuration, while participants in the passive learner cluster exhibited only frequent browsing. We found that active learners exhibited higher learning achievement than passive learners.
AB - Video-based online learning is becoming commonplace in higher education settings. Prior studies have suggested design principles and instructional strategies to boost video-based learning. However, little research has been done on different learner characteristics, such as how learners behave, what behavioral patterns they exhibit, and how different they are from each other. To fill this research gap in student-video interaction, we employed learning analytics to obtain useful insights into students' learning in the context of video-based online learning. From 11 log behaviors represented by log data from 72 college students, four behavioral patterns were identified while students learned from videos: browsing, social interaction, information seeking, and environment configuration. Based on the behavioral patterns observed, participants were classified into two clusters. Participants in the active learner cluster exhibited frequent use of social interaction, information seeking, and environment configuration, while participants in the passive learner cluster exhibited only frequent browsing. We found that active learners exhibited higher learning achievement than passive learners.
KW - Behavioral patterns
KW - Learner cluster
KW - Learner online behavior
KW - Learning analytics
KW - Video log analytics
UR - http://www.scopus.com/inward/record.url?scp=85104807089&partnerID=8YFLogxK
U2 - 10.1016/j.iheduc.2021.100806
DO - 10.1016/j.iheduc.2021.100806
M3 - Article
AN - SCOPUS:85104807089
SN - 1096-7516
VL - 50
JO - Internet and Higher Education
JF - Internet and Higher Education
M1 - 100806
ER -