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Knowledge Discovery of Student Sentiments in MOOCs and Their Impact on Course Performance

[+] Author Affiliations
Conrad S. Tucker, Bryan Dickens, Anna Divinsky

The Pennsylvania State University, University Park, PA

Paper No. DETC2014-34797, pp. V003T04A028; 9 pages
doi:10.1115/DETC2014-34797
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 16th International Conference on Advanced Vehicle Technologies; 11th International Conference on Design Education; 7th Frontiers in Biomedical Devices
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4634-6
  • Copyright © 2014 by ASME

abstract

The objective of this research is to mine textual data (e.g., online discussion forums) generated by students enrolled in Massive Open Online Courses (MOOCs) in order to quantify students’ sentiment, in relation to their course performance. Massive Open Online Courses (MOOCs) are free to anyone with a computing device and a means of connecting to the internet and serve as a new paradigm for distance based education. While student interactions in traditional based brick and mortar classes are readily observable by students and instructors, quantifying the sentiments expressed by students in MOOCs remains challenging. This is in part due to the quantity of textual data being generated by students enrolled in MOOCs, in addition to a lack of quantitative methodologies that discover latent, previously unknown knowledge pertaining to student interactions and sentiments in the digital world.

The authors of this work introduce a data mining driven methodology that employs natural language processing techniques and text mining algorithms to quantify students’ sentiments, based on their textual data provided during course assignment discussions. The researchers of this work aim to help educators understand the factors that may impact student performance, team interactions and overall learning outcomes in digital environments such as MOOCs.

Copyright © 2014 by ASME
Topics: Students

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