Web-Based Analysis of Student Activity for Predicting Dropout

Anat Cohen
Tel Aviv University, Israel


Persistence in learning processes is perceived as a central value in education (Horowitz, 1992); therefore, dropout from studies is a prime concern for educators (Barefoot, 2004). Since the increase in student usage of online learning materials on course websites, as well as online courses (Allen & Seaman, 2014; Parker, Lenhart, & Moore, 2011), it is essential to address the dropout issue in a wide array of configurations from web-supported learning to fully online courses. Additional tools and strategies must be developed to allow instructors or other educational decision makers to quickly identify at-risk students and find ways to support their learning in the early stages, before they actually drop out. The ability to detect these students during the semester, and not at the end of the course, can also serve as a basis for the development of appropriate assistance mechanisms which will enable those students to complete the course and even to fulfil the curriculum for their degree.

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