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 |
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| 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 |
| 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.] |
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