MOOC performance prediction via clickstream data and social learning networks
- 1 April 2015
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2299-2307
- https://doi.org/10.1109/infocom.2015.7218617
Abstract
We study student performance prediction in Massive Open Online Courses (MOOCs), where the objective is to predict whether a user will be Correct on First Attempt (CFA) in answering a question. In doing so, we develop novel techniques that leverage behavioral data collected by MOOC platforms. Using video-watching clickstream data from one of our MOOCs, we first extract summary quantities (e.g., fraction played, number of pauses) for each user-video pair, and show how certain intervals/sets of values for these behaviors quantify that a pair is more likely to be CFA or not for the corresponding question. Motivated by these findings, our methods are designed to determine suitable intervals from training data and to use the corresponding success estimates as learning features in prediction algorithms. Tested against a large set of empirical data, we find that our schemes outperform standard algorithms (i.e., without behavioral data) for all datasets and metrics tested. Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the “early detection” capability of such clickstream data. We also discuss how CFA prediction can be used to depict graphs of the Social Learning Network (SLN) of students, which can help instructors manage courses more effectively.Keywords
This publication has 10 references indexed in Scilit:
- Learning about Social Learning in MOOCs: From Statistical Analysis to Generative ModelIEEE Transactions on Learning Technologies, 2014
- Monitoring MOOCsPublished by Association for Computing Machinery (ACM) ,2014
- Understanding in-video dropouts and interaction peaks inonline lecture videosPublished by Association for Computing Machinery (ACM) ,2014
- Social learning networks: A brief surveyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Networked LifePublished by Cambridge University Press (CUP) ,2012
- Factorization Machines with libFMACM Transactions on Intelligent Systems and Technology, 2012
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Multidimensional Item Response TheoryPublished by Springer Science and Business Media LLC ,2009
- Factorization meets the neighborhoodPublished by Association for Computing Machinery (ACM) ,2008
- Handbook of Parametric and Nonparametric Statistical ProceduresPublished by Taylor & Francis Ltd ,2003