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Students in MOOCs: A Latent Profile Analysis on Participants in Massive Open Online Courses

Mon, April 15, 8:00 to 9:30am, Hyatt Regency, Floor: Bay (Level 1), Bayview B

Proposal

Many studies have indicated that online education, especially MOOCs, disproportionately attracts and helps learners with greater access to social and educational resources (Hansen & Reich, 2015; Christensen et al., 2013). However, from the first MOOC (Andrew Ng’s course on Machine Learning) to the majority of the MOOCs today, courses are adapted from and designed around college-level curriculum. While some educators celebrate the “emancipatory potential of this type of access to higher education”(Bennett & Kent, 2017), the fundamental idea of MOOCs requires students to have some levels of secondary or post-secondary education. It is unrealistic to find many underprivileged students in this e-learning community since they do not even possess the “prerequisite” of MOOCs (Liyanagunawardena, 2013). So the questions are: who are we trying to provide free higher education to? Are there underprivileged students in the world of MOOCs? Who are the underprivileged students in this case?
To answer these questions, I will be using latent profile analysis technique to understand different types of students in the MOOCs community. From the results, I will be looking for (i) demographic differences among different student profiles, (ii) potential motivation differences behind profiles, and (iii) if any profile shows significant disadvantage compared to others. By learning the different profiles of students enrolled in MOOCs, we will no longer be constrained in variables like dropout rates and average student scores in online learning, the most widely used metric today. We can better understand the MOOCs student composition and what different types of students want to learn from MOOCs, especially those at a disadvantage in the society.

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