Generalized Additive Modeling for Learning Trajectories in E-Learning Environments

Jung Yeon Park, Jin Ho Kim, Dries Debeer, Wim Van den Noortgate

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Adaptive E-learning is growing in popularity as it personalizes recommendations in response to learners’ learning needs. An a priori expectation of the learning environment is that the learners’ performance levels may change in real time as they complete a sequence of items and receive feedback. Also, the learners’ learning (performance) trajectories may be irregularly shaped over time. Therefore, a modeling approach that flexibly explores the learner’s learning change is desirable. In this study, we demonstrate the applicability of a semi-parametric modeling approach that can estimate learners’ unique learning trajectories in the E-learning environment. We use a generalized additive mixed model that integrates properties of generalized linear mixed models with those of additive models, in which the linear predictor is given by a sum of smooth functions of the covariates as well as a parametric component of the linear predictor. The model we consider explores the effect of time that the learners spend inside and outside the learning environment. We demonstrate its applicability to log data generated by a real-life E-learning environment.

Original languageEnglish
Title of host publicationQuantitative Psychology - The 85th Annual Meeting of the Psychometric Society
EditorsMarie Wiberg, Dylan Molenaar, Jorge González, Ulf Böckenholt, Jee-Seon Kim
PublisherSpringer
Pages453-461
Number of pages9
ISBN (Print)9783030747718
DOIs
StatePublished - 2021
Event85th Annual International Meeting of the Psychometric Society, IMPS 2020 - Virtual, Online
Duration: 13 Jul 202017 Jul 2020

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume353
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference85th Annual International Meeting of the Psychometric Society, IMPS 2020
CityVirtual, Online
Period13/07/2017/07/20

Keywords

  • Between-session effect
  • E-learning environments
  • Generalized additive mixed model
  • Learning Trajectory

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