Knowledge discovery of student sentiments in MOOCs and their impact on course performance

Conrad S. Tucker, Anna Divinsky, Bryan Dickens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publication16th International Conference on Advanced Vehicle Technologies; 11th International Conference on Design Education; 7th Frontiers in Biomedical Devices
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791846346
DOIs
StatePublished - Jan 1 2014
EventASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014 - Buffalo, United States
Duration: Aug 17 2014Aug 20 2014

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume3

Other

OtherASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014
CountryUnited States
CityBuffalo
Period8/17/148/20/14

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Knowledge discovery of student sentiments in MOOCs and their impact on course performance'. Together they form a unique fingerprint.

Cite this