However, although existing models for evaluating the effectiveness of universities provide a large number of modeling solutions, it is difficult to objectively evaluate dynamic coefficients based on the differences in precision ideological and political work systems of different types of universities in the evaluation process of innovative paths…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

In peer nomination research, individuals who do not provide nominations (nonparticipants) are often included on rosters as potential nominees. This can present ethical questions regarding informed consent, but psychometric consequences of excluding nonparticipants from rosters are unknown. In this investigation, Study 1 simulated both random and…

This paper details the anticipated impact of synthetic "big" data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at the sector-wide level (the Jisc learning analytics architecture) and…

The rise of digital game and simulation-based learning applications has led to new approaches in educational measurement that take account of patterns in time, high resolution paths of action, and clusters of virtual performance artifacts. The new approaches, which depart from traditional statistical analyses, include data mining, machine…

This paper outlines a novel approach to engineering education research that provides three dimensions of learning through an experiential class activity. A simulated decision activity brought current research into the classroom, explored the effect of experiential activity on learning outcomes and contributed to the research on innovation decision…

When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…

This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…

The authors aimed to incorporate learning into the cognitive assessment framework that exists for static assessment data. In order to accomplish this, they derive a common likelihood function for dynamic models and introduce Parameter Driven Process for Change + Cognitive Diagnosis Model (PDPC + CDM), a dynamic model which tracks learning…

The well established, gold standard approach to finding out what works in education research is to run a randomized controlled trial (RCT) using a standard pre-test and post-test design. RCTs have been used in the intelligent tutoring community for decades to determine which questions and tutorial feedback work best. Practically speaking, however,…

This NCEE Technical Methods report examines how to address the problem of missing data in the analysis of data in Randomized Controlled Trials (RCTs) of educational interventions, with a particular focus on the common educational situation in which groups of students such as entire classrooms or schools are randomized. Missing outcome data are a…

An empirical comparison is made of three proposed indices of relative predictor variable contribution: (1) the scaled weights of the first discriminant function; (2) the total group estimates of the correlations between each predictor variable and the first function; and (3) the within-groups estimates of the correlations between each predictor…

This paper describes a study that attempts to measure the influence of a commercially available game, Marketplace, on student achievement is economic understanding and student attitudes toward the instructional process and selected economic concepts. (Author)

Topics which range from the more popular computing applications in accounting, statistics, and administration to the less ordinary applications of the computer to the fields of fine arts, medicine, and linguistics, are discussed in this collection of 22 conference papers. The papers are divided into four tracks: the first deals with statistical…

Within-class regression is a method, developed in this paper, of comparing a large number of nonequivalent groups. This study indicated that within-class regression was a less biased method of data analysis and will yield more accurate estimates of treatment effects than analysis of covariance. (PN)