Original Research ARTICLE
Modelling Learner Heterogeneity: A Mixture Learning Model with Responses and Response Times
- 1Columbia University, United States
- 2University of Georgia, United States
The increased popularity of computer-based testing has enabled researchers to collect various types of process data, including test taker’s reaction time to assessment items, also known as response times. In recent studies, the relationship between speed and accuracy in a learning setting was explored to understand students’ fluency changes over time in applying the mastered skills, in addition to skill mastery. This can be achieved by modeling the changes in response accuracy and response times throughout the learning process. We propose a mixture learning model that utilizes the response times and response accuracy. Such a model accounts for the heterogeneities in learning styles among learners and may provide instructors with valuable information, which can be used to design individualized instructions. A Bayesian modeling framework is developed for parameter estimation and the proposed model is evaluated through a simulation study and is fitted to a real data set collected from a computer-based learning system for spatial rotation skills.
Keywords: response times, learning behavior, Diagnostic classification model, Hidden markov model, mixture model
Received: 27 Aug 2018;
Accepted: 08 Nov 2018.
Edited by:Qiwei He, Educational Testing Service, United States
Reviewed by:Daniel Bolt, University of Wisconsin-Madison, United States
Yang Liu, University of Maryland, College Park, United States
Copyright: © 2018 Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Shiyu Wang, University of Georgia, Athens, 30602, Georgia, United States, email@example.com