In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in t...In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and easy.This study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.展开更多
文摘In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and easy.This study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.
