Many recent studies have looked at the viability of applying recurrent neural networks (RNNs) to educational data. In most cases, this is done by comparing their performance to existing models in the artificial intelligence in education (AIED) and educational data mining (EDM) fields. While there is increasing evidence that, in many situations,…

The four-parameter logistic (4PL) Item Response Theory (IRT) model has recently been reconsidered in the literature due to the advances in the statistical modeling software and the recent developments in the estimation of the 4PL IRT model parameters. The current simulation study evaluated the performance of expectation-maximization (EM),…

Purpose: This study aims to investigate the orthogonality assumption, which restricts the use of Bifactor item response theory under different conditions. Method: Data of the study have been obtained in accordance with the Bifactor model. It has been produced in accordance with two different models (Model 1 and Model 2) in a simulated way.…

Student misconceptions have been studied for decades from a curricular/instructional perspective and from the assessment/test level perspective. Numerous misconception assessment tools have been developed in order to measure students' misconceptions relative to the correct content. Often, these tools are used to make a variety of educational…

This study investigates the effects of sample size and test length on item-parameter estimation in test development utilizing three unidimensional dichotomous models of item response theory (IRT). For this purpose, a real language test comprised of 50 items was administered to 6,288 students. Data from this test was used to obtain data sets of…

This dissertation proposes two modified cognitive diagnostic models (CDMs), the deterministic, inputs, noisy, "and" gate with hierarchy (DINA-H) model and the deterministic, inputs, noisy, "or" gate with hierarchy (DINO-H) model. Both models incorporate the hierarchical structures of the cognitive skills in the model estimation…

Content balancing is one of the most important components in the computerized adaptive testing (CAT) especially in the K to 12 large scale tests that complex constraint structure is required to cover a broad spectrum of content. The purpose of this study is to compare the weighted penalty model (WPM) and the weighted deviation method (WDM) under…

Several complex sample standard error estimators based on linearization and resampling for the latent regression model of the National Assessment of Educational Progress (NAEP) are studied with respect to design choices such as number of items, number of regressors, and the efficiency of the sample. This paper provides an evaluation of the extent…

This dissertation includes three essays: one essay focuses on the effect of teacher preparation programs on teacher knowledge while the other two focus on test-takers' response times on test items. Essay One addresses the problem of how opportunities to learn in teacher preparation programs influence future elementary mathematics teachers'…