A Generalizable Framework for Classifying Massive Open Online Course Students

• In Event: Roundtable Session 51
  In Roundtable Session: Online Teaching and Learning SIG Roundtable Session 4

Abstract

Critics often pointed to high dropout rate of MOOCs as one of its major shortcomings. Advocates of MOOCs, however, argued that the often-cited high dropout number failed to take the context of MOOCs into account. Specifically, they argued that MOOC students often registered for courses with more diverse intentions than students enrolling in traditional classes. Therefore, estimating dropout rates without taking students’ intentions into account will not only overestimate the number, but it also fails to inform practitioners how one may better support the needs of the dropped out students. The current work proposes a generalizable framework that may be used to study the students’ intentions through their engagement behaviors. Results of applying the framework and its implications are presented.

Authors

• Wik Hung Pun, The Pennsylvania State University

• Yao Xiong, The Pennsylvania State University

• Xiuyan Guo, The Pennsylvania State University - University Park

• Deborah Goins, The Pennsylvania State University - University Park

• Jeremy Ray Gevara, The Pennsylvania State University - University Park

• Hoi K. Suen, The Pennsylvania State University