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Modeling Person-Specific Development of Math Skills in Continuous Time: New Evidence for Mutualism

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Title: Modeling Person-Specific Development of Math Skills in Continuous Time: New Evidence for Mutualism
Language: English
Authors: Ou, Lu, Hofman, Abe D., Simmering, Vanessa R., Bechger, Timo, Maris, Gunter, van der Maas, Han L. J.
Source: International Educational Data Mining Society. 2019.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Peer Reviewed: Y
Page Count: 6
Publication Date: 2019
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Elementary Education
Descriptors: Mathematics Skills, Skill Development, Time, Models, Electronic Learning, Computation, Addition, Educational Technology, Elementary School Students
Abstract: In this study, we fitted a mixed-effects nonlinear continuous-time mutualism model of skill development proposed by van der Maas et al. (2006) to naturally collected irregularly spaced time series data from an online adaptive practice system for mathematics called Math Garden. Results showed that the mutualism model provided a better fit to the data than a g-factor model. The paper illustrates continuous-time modeling of irregularly-spaced multivariate time series data that are increasingly prevalent in modern learning systems. [For the full proceedings, see ED599096.]
Abstractor: As Provided
Entry Date: 2019
Accession Number: ED599207
Database: ERIC
FullText Text:
  Availability: 0
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  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED599207
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  Data: Modeling Person-Specific Development of Math Skills in Continuous Time: New Evidence for Mutualism
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  Data: In this study, we fitted a mixed-effects nonlinear continuous-time mutualism model of skill development proposed by van der Maas et al. (2006) to naturally collected irregularly spaced time series data from an online adaptive practice system for mathematics called Math Garden. Results showed that the mutualism model provided a better fit to the data than a g-factor model. The paper illustrates continuous-time modeling of irregularly-spaced multivariate time series data that are increasingly prevalent in modern learning systems. [For the full proceedings, see ED599096.]
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