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Vidotto, Davide; Vermunt, Jeroen K.; van Deun, Katrijn – Journal of Educational and Behavioral Statistics, 2018
With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex…
Descriptors: Bayesian Statistics, Multivariate Analysis, Data, Hierarchical Linear Modeling
Ahmadi, Mohammad; Dileepan, Parthasarati; Wheatley, Kathleen K. – Journal of Education for Business, 2016
This is the decade of data analytics and big data, but not everyone agrees with the definition of big data. Some researchers see it as the future of data analysis, while others consider it as hype and foresee its demise in the near future. No matter how it is defined, big data for the time being is having its glory moment. The most important…
Descriptors: Strategic Planning, Data, Data Analysis, Statistical Analysis
Madhyastha, Tara M.; Tanimoto, Steven – International Working Group on Educational Data Mining, 2009
Most of the emphasis on mining online assessment logs has been to identify content-specific errors. However, the pattern of general "consistency" is domain independent, strongly related to performance, and can itself be a target of educational data mining. We demonstrate that simple consistency indicators are related to student outcomes,…
Descriptors: Web Based Instruction, Computer Assisted Testing, Computer Software, Computer Science Education