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Predicting Students Performance Based on Their Reading Behaviors

Chau, Hung and Li, Ang and Lin, Yu-Ru (2017) Predicting Students Performance Based on Their Reading Behaviors. In: 2017 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, 5-8 Jul 2017, Washington DC, USA.

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Abstract

E-learning systems can support students in the on-line classroom environment by providing different learning materials. However, recent studies find that students may misuse such systems with a variety of strategies. One particular misused strategy, gaming the system, has repeatedly been found to negatively affect the students’ learning results. Unfortunately, methods to quantitatively capture such behavior are poorly developed, making it difficult to predict students learning outcomes. In this work, we tackle this problem based on a study of the 567,193 records of the 71 students’ reading behaviors from two classes in the academic year 2016. We first quantify the extent to which students misused the system and then predict their class performance based on the quantified results. Our results demonstrated that such misbehavior in the E-learning system can be quantified as a probability and then further used as a significant factor to predict students class learning outcomes with high accuracy.

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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Chau, Hung
Li, Ang
Lin, Yu-Ruyurulin@pitt.eduYURULIN
Date: July 2017
Event Title: 2017 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation
Event Dates: 5-8 Jul 2017
Event Type: Conference
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Date Deposited: 06 Jul 2018 15:28
Last Modified: 06 Jul 2018 15:28
URI: http://d-scholarship.pitt.edu/id/eprint/34715

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