Student Clickstreams Activity Based Performance of Online Course

被引:2
|
作者
Singh, Anshu [1 ]
Sachan, Anuj [2 ]
机构
[1] Univ Lucknow, Lucknow, Uttar Pradesh, India
[2] IIT Roorkee, Roorkee, Uttar Pradesh, India
关键词
MOOC; Moodle; Support vector machine; K-nearest neighbors;
D O I
10.1007/978-3-030-82322-1_18
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
MOOC is an online course targeted at massive-scale interactive involvement and web-based free access. The student prefers online video lecture. Analyzing the impact of student's interaction with different learning modes will improve the quality of an online course. The learning model used in this work is an online video course. In this work the authors have proposed features related to the number of time student watches a video, how much time spent on the course and the authors have taken help from secondary features like intermediate quiz performance and attendance in-class lectures. Using these features, the authors want to predict the performance of students in an online course. In this work, the author analyses students' video watching behaviour with the help of their on click event during the whole online course.
引用
收藏
页码:242 / 253
页数:12
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