Understanding Motivation for Enrolling in Postdoc Academy: Succeeding as a Postdoc Using Natural Language Processing (Poster 36)

• In Event: AERA Poster Session Two
  In Poster Session: Motivational Beliefs in Context

Abstract

It is important to examine learners’ motivation for enrolling in Massive Open Online Courses (MOOCs) since it is correlated with course completion and engagement. However, the data on motivation in some MOOCs is qualitative and poses challenges for analysis. Natural language processing (NLP) provides an opportunity to analyze large-scale qualitative data. This study analyzes learners’ motivation to enroll in a MOOC using NLP techniques. Data were collected from learners taking Postdoc Academy: Succeeding as a Postdoc. After text pre-processing, k-mean clustering algorithms and Latent Dirichlet allocation based on Term Frequency-Inverse Document Frequency matrix were applied, to identify potential clusters and top terms. Nine categories of motivations were identified, and learners were more extrinsically motivated than intrinsically motivated for enrollment.

Authors

• Ting Sun, University of Utah
  Presenting Author

• Denise L. Drane, Northwestern University
  Presenting Author

• Sarah Chobot Hokanson, Boston University
  Presenting Author

• Bennett Goldberg, Northwestern University
  Presenting Author