Combining click-stream data with NLP tools to better understand MOOC completion-Reference-Cited by-同舟云学术

Combining click-stream data with NLP tools to better understand MOOC completion

Published:2016-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Sixth International Conference on Learning Analytics & Knowledge
language:
Short-container-title:

Author:

Crossley Scott¹,Paquette Luc²,Dascalu Mihai³,McNamara Danielle S.⁴,Baker Ryan S.⁵

Affiliation:

1. Georgia State University, Atlanta, GA

2. University of Illinois at Urbana-Champaign, Champaign, IL

3. University Politehnica of Bucharest, Bucharest, Romania

4. Arizona State University, Tempe, AZ

5. Teachers College, Columbia University, New York, NY

Funder

National Science Foundation

Institute for Education Sciences

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2883851.2883931

Reference46 articles.

1. Balakrishnan G. & Coetzee D. 2013. Predicting Student Retention in Massive Open Online Courses Using Hidden Markov Models. Electrical Engineering and Computer Sciences University of California at Berkeley. Balakrishnan G. & Coetzee D. 2013. Predicting Student Retention in Massive Open Online Courses Using Hidden Markov Models. Electrical Engineering and Computer Sciences University of California at Berkeley .

2. Bradley M. M. and Lang P. J. 1999. Affective norms for English words (ANEW): Stimuli instruction manual and affective ratings. Technical report. The Center for Research in Psychophysiology University of Florida. Bradley M. M. and Lang P. J. 1999. Affective norms for English words (ANEW): Stimuli instruction manual and affective ratings. Technical report. The Center for Research in Psychophysiology University of Florida .

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