This study utilizes an analysis of diminishing returns to examine the relationship between sample size and item parameter estimation precision when utilizing the Masters' Partial Credit Model for polytomous items. Item data from the standardization of the Batelle Developmental Inventory, 3rd Edition were used. Each item was scored with a…

Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…

Student modeling is useful in educational research and technology development due to a capability to estimate latent student attributes. Widely used approaches, such as the Additive Factors Model (AFM), have shown satisfactory results, but they can only handle binary outcomes, which may yield potential information loss. In this work, we propose a…

Educational assessment data are often collected from a set of test centers across various geographic regions, and therefore the data samples contain clusters. Such cluster-based data may result in clustering effects in variance estimation. However, in many grouped jackknife variance estimation applications, jackknife groups are often formed by a…

Measuring change in an educational or psychological construct over time is often achieved by repeatedly administering the same items to the same examinees over time. When the response data are categorical, item response theory (IRT) model can be used as the measurement model of a second-order latent growth model (referred to as LGM-IRT) to measure…

Estimation is useful in situations where an exact answer is not as important as a quick answer that is good enough. A web-based adaptive system for practicing estimates is currently being developed. We propose a simple model for estimating student's latent skill of estimation. This model combines a continuous measure of correctness and response…

Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given…

A quality educational experience for secondary students involves more than an acquisition of content knowledge; it entails providing students opportunities to develop a variety of thinking skills that enable integration of knowledge and the promotion of student self-directed learning outside of the classroom. One critical skill that is often…

This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…

Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving test scoring and better informing public policy. Yet larger…

As adaptive tutoring systems grow increasingly popular for the completion of classwork and homework, it is crucial to assess the manner in which students are scored within these platforms. The majority of systems, including ASSISTments, return the binary correctness of a student's first attempt at solving each problem. Yet for many teachers,…

This paper utilized the Rasch model and Joint Maximum Likelihood Estimation to study different scoring options for omitted and not-reached items. Three scoring treatments were studied. The first method treated omitted and not-reached items as "ignorable/blank". The second treatment, scored omits as incorrect with "0" and left not-reached as blank…

Nonparametric, or kernel, estimation of item response curve (IRC) is a concern theoretically and operationally. Accuracy of this estimation, often used in item analysis in testing programs, is biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. In this study, we investigate…

For decades, researchers and practitioners have made a great deal of effort to study a variety of methods to increase parameter accuracy, but only recently can researchers start focusing on improving parameter estimations by using a joint model that could incorporate RT and students information as CI. Given that many tests are currently…

The ability estimation procedure is one of the most important components in a computerized adaptive testing (CAT) system. Currently, all CATs that provide K-12 student scores are based on the item response theory (IRT) model(s); while such application directly violates the assumption of independent sample of a person in IRT models because ability…