Mediation analysis is widely used to study whether the effect of an independent variable on an outcome is transmitted through a mediator. Bayesian methods have become increasingly popular for mediation analysis. However, limited research has been done on formal Bayesian hypothesis testing of mediation. Although hypothesis testing using Bayes…

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications of CDMs, certain hierarchical structures among the latent attributes are assumed by researchers to characterize their dependence structure. Specifically, a directed acyclic…

Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…

Statistical mediation allows researchers to investigate potential causal effects of experimental manipulations through intervening variables. It is a powerful tool for assessing the presence and strength of postulated causal mechanisms. Although mediation is used in certain areas of psychology, it is rarely applied in cognitive psychology and…

In the e-learning context, social network analysis (SNA) can be used to build understanding around the ways students participate and interact in online forums. This study contributes to the growing body of research that uses statistical methods to test hypotheses about structures in social networks. Specifically, we show how statistical analysis…

Many children fail to master fraction arithmetic even after years of instruction, a failure that hinders their learning of more advanced mathematics as well as their occupational success. To test hypotheses about why children have so many difficulties in this area, we created a computational model of fraction arithmetic learning and presented it…

This study investigated the dimensionality of the physical literacy environment of early childhood education classrooms. Data on the classroom physical literacy environment were collected from 245 classrooms using the Classroom Literacy Observation Profile. A combination of confirmatory and exploratory factor analysis was used to identify five…

Women are more likely to leave science, technology, engineering, and mathematics compared to men, in part because they lack similar role models such as peers, teaching assistants, and instructors. We examined the effect of a brief, scalable online intervention that consisted of a letter from a female role model who normalized concerns about…

We investigated direct and indirect effects of component skills on writing (DIEW) using data from 193 children in Grade 1. In this model, working memory was hypothesized to be a foundational cognitive ability for language and cognitive skills as well as transcription skills, which, in turn, contribute to writing. Foundational oral language skills…

During high school, developing competence in science, technology, engineering, and mathematics (STEM) is critically important as preparation to pursue STEM careers, yet students in the United States lag behind other countries, ranking 35th in mathematics and 27th in science achievement internationally. Given the importance of STEM careers as…

Educator stress has been linked to decreased job satisfaction, negative instructional practices, and poor student outcomes. However, it is unknown whether educators with high teaching efficacy may better cope with the test stress. As such, the primary purpose of the present investigation was to examine the complex relationship between teacher…

How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint…

Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to…

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…