research-article

Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses

Authors:

Peter Brusilovsky,

Jordan BarrÃ-a PinedaAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 2068 - 2079

https://doi.org/10.1145/3308558.3313713

Published: 13 May 2019 Publication History

Abstract

The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.

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Cited By

Akhuseyinoglu KBrusilovsky P(2022)Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual DifferencesFrontiers in Artificial Intelligence10.3389/frai.2022.8073205Online publication date: 15-Feb-2022
https://doi.org/10.3389/frai.2022.807320
Mirzaei MSahebi SBrusilovsky P(2020)Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00041(283-290)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00041

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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Cited By

Akhuseyinoglu KBrusilovsky P(2022)Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual DifferencesFrontiers in Artificial Intelligence10.3389/frai.2022.8073205Online publication date: 15-Feb-2022
https://doi.org/10.3389/frai.2022.807320
Mirzaei MSahebi SBrusilovsky P(2020)Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00041(283-290)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00041

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