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Learning Curve Analysis using Intensive Longitudinal and Cluster-Correlated Data
Procedia Computer Science
  • Xiao Zhong
  • Zeyi Sun, Missouri University of Science and Technology
  • Haoyi Xiong, Missouri University of Science and Technology
  • Neil Heffernan
  • Md Monirul Islam

Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple individuals or study units are observed and measured at tens, hundreds, or thousands of occasions. The spacing of measurements in time for each individual can be regular or irregular, fixed or random, and the number of characteristics measured at each occasion may be few or many. Such data can also arise in situations involving continuous-time measurements of recurrent events. Generalized linear models (GLMs) are usually considered for the analysis of correlated non-normal data, while multivariate analysis of variance (MANOVA) is another option. In the paper, both GLMs and MANOVA are applied to ASSISTments online teaching system via the Wald test in order to estimate and predict the learning effects of the students after taking an Algebra course for three months in the system. Three case studies based on these two methodologies are presented in a mathematical and statistical manner.

Meeting Name
Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS (2017: Oct. 30-Nov. 1, Chicago, IL)
Engineering Management and Systems Engineering
Second Department
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
  • Case Study,
  • GLMs,
  • Learning Curve Analysis,
  • Longitudinal Data Analysis,
  • Wald Test
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
© 2017 The Authors, All rights reserved.
Creative Commons Licensing
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Publication Date
Citation Information
Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, et al.. "Learning Curve Analysis using Intensive Longitudinal and Cluster-Correlated Data" Procedia Computer Science Vol. 114 (2017) p. 250 - 257
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