Computational learning theory studies the design and analysis of learning algorithms, and it is integral to the foundation of machine learning. In the modern era, classical computational learning theory is growingly unable to catch up with new practical demands. In particular, problems arise in the following aspects: i). "scalability":…

Multidimensional Item Response Theory (MIRT) has been widely used in educational and psychological assessments. It estimates multiple constructs simultaneously and models the correlations among latent constructs. While it provides more accurate results, the unidimensional IRT model is still dominant in real applications. One major reason is that…

This study was conducted to further research into the impact of student-group item parameter drift (SIPD) --referred to as subpopulation item parameter drift in previous research-- on ability estimates and proficiency classification accuracy when occurring in the discrimination parameter of a 2-PL item response theory (IRT) model. Using Monte…

As online learning platforms and computerized testing become more common, an increasing amount of data are collected about users. These data include, but are not limited to, response time, keystroke logs, and raw text. The desire to observe these features of the response process reflect an underlying interest in the cognitive processes and…

Previous research has been limited regarding the measurement of computational thinking, particularly as a learning progression in K-12. This study proposes to apply a multidimensional item response theory (IRT) model to a newly developed measure of computational thinking utilizing both selected response and open-ended polytomous items to establish…

The Rasch model is commonly used to calibrate multiple choice items. However, the sample sizes needed to estimate the Rasch model can be difficult to attain (e.g., consider a small testing company trying to pretest new items). With small sample sizes, auxiliary information besides the item responses may improve estimation of the item parameters.…

Observable data in empirical social and behavioral science studies are often categorical (i.e., binary, ordinal, or nominal). When categorical data are outcomes, they fail to maintain the scale and distributional properties of linear regression and factor analysis. Attempting to estimate model parameters for categorical outcome data with the…

This dissertation is composed of a study of estimation methods in classical and test theories and the elaboration and application of a cluster-robust variance estimator. Variance estimators derived from generalized estimating equations are known to be robust to most covariance structures and are therefore well suited for psychometric analysis of…

In Huggins-Manley & Han (2017), it was shown that WLSMV global model fit indices used in structural equating modeling practice are sensitive to person parameter estimate RMSE and item difficulty parameter estimate RMSE that results from local dependence in 2-PL IRT models, particularly when conditioning on number of test items and sample size.…

Educational data mining focuses on exploring increasingly large-scale data from educational settings, such as Learning Management Systems (LMS), and developing computational methods to understand students' behaviors and learning settings better. There has been a multitude of research dedicated to studying the student learning process, leading to…

As science becomes increasingly computationally intensive, the need for computational thinking (CT) and computer science (CS) practices in K-12 science education is becoming paramount. Incorporation of CT/CS practices in K-12 education can be seen in national standards and a variety of allied initiatives. One way to build capacity around an…

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 is part of a larger design study that iteratively improves a robotics programming curriculum as well as a computational thinking (CT) instrument. Its focus was majorly on CT assessment and particularly on an online CT instrument with logging functionality that can store a student's problem-solving process by recording interactions…

This study aims to evaluate a multidimensional latent trait model to determine how well the model works in various empirical contexts. Contrary to the assumption of these latent trait models that the traits are normally distributed, situations in which the latent trait is not shaped with a normal distribution may occur (Sass et al, 2008; Woods…

Item response theory (IRT) has gained an increasing popularity in large-scale educational and psychological testing situations because of its theoretical advantages over classical test theory. Unidimensional graded response models (GRMs) are useful when polytomous response items are designed to measure a unified latent trait. They are limited in…