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…

When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type…

This paper explores how factors operating at the state and community levels are associated with the prevalence of late school enrolment in Nigeria. We investigate the following three research themes. First, whether late entry varies across states and across communities and how much of this variation can be explained by the composition of…

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…

This paper presents findings from a quantitative, correlational study that examined selected school nursing services, student academic outcomes, and school demographics. Ex post facto data from the 2011-2012 school year of Delaware public schools were used in the research. The selected variables were school nurse interventions provided to students…

The purpose of this study was to determine if professional development using school contextual data would provide deeper understanding and attention to the important issue of student bullying. This was primarily a quantitative study. Phase 1 involved using data from the pre-existing student and teacher needs assessments to create the school…

Faced with expectations to improve student learning and increasing student alternatives, school districts are challenged to make system-wide and sustainable improvement in student learning outcomes. The national accountability movement is associated with a growing body of research that has considered the role of the district to influence student…

The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…

This study addresses the question: "To what extent are teacher assigned subject specific grades useful for data driven decision making in schools?" Recently, schools have been urged to bring teachers and school leaders together around student-level data in an effort to increase dialogue, collaboration and professional communities to…

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…