Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However,…

Interest in the cognitive precursors of literacy has been increasing in recent years since reading and writing are essential components of functional learning in the first years of schooling and of school success in later educational stages. However, it can be observed that while studies on the relationship between cognitive variables and reading…

The present work focuses on the performance of two types of shrinkage priors--the horseshoe prior and the recently developed regularized horseshoe prior--in the context of inducing sparsity in path analysis and growth curve models. Prior research has shown that these horseshoe priors induce sparsity by at least as much as the "gold…

In this thesis, I use a combination of simulations and empirical data to demonstrate that a small set of structural and functional principles - the basic tenets of predictive coding theory - succinctly accounts for a very wide range of properties in the language processing system. Predictive coding approximates hierarchical Bayesian inference via…

The increasing prevalence of children's attention-deficit hyperactivity disorder (ADHD) and internalizing behaviors requires investigation into putative risk factors. This longitudinal project of 105 preschoolers (54.3% girls; 90% White) examined influences of early life experiences (perinatal stressors, home environment, maternal personality) and…

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of educational datasets further catalyse this interestingness, especially in…

Based on hundreds of thousands of hours of data about how students learn in massive open online courses, educational machine learning promises to help students who are learning to code. However, in most classrooms, students and assignments do not have enough historical data for feeding these data hungry algorithms. Previous work on predicting…

Purpose: Morse code as a form of communication became widely used for telegraphy, radio and maritime communication, and military operations, and remains popular with ham radio operators. Some skilled users of Morse code are able to comprehend a full sentence as they listen to it, while others must first transcribe the sentence into its written…

In this study, we investigated participants' reactions to supportive and anomalous data in the context of population dynamics. Based on previous findings on conceptions about ecosystems and responses to anomalous data, we assumed a tendency to confirm the initial prediction after dealing with contradicting data. Our aim was to integrate a…

Questionnaires are widely used in autism assessment. However, their psychometric properties are generally not evaluated in clinical practice, and the comparability and applicability of such research is limited because questionnaires are often not simultaneously evaluated. This certainly pertains to predictive values which are highly population and…

A thorough understanding of the relationship between quality and quantity of creative productions is critically important for creativity researchers and practitioners. The current study examines the equal odds baseline as a simple model to describe the quality-quantity relationship. Among other predictions, the equal odds baseline posits the…

Strategy and research are essential parts of strategic enrollment management (SEM), yet little information exists regarding how to use research and predictive analytics for effective strategy. It is often easier to react to what is happening in the moment, rather than be proactive in predicting the future or developing long-term plans. This…

Representational competence is a target of novel learning environments given the assumption that improved representational competence improves learning in science. There exists little evidence, however, that improving representational competence is positively correlated with learning outcomes across science disciplines. In this report, we argue…

We address the problem of predicting the correctness of the student's response on the next exam question based on their previous interactions in the course of their learning and evaluation process. We model the student performance as a dynamic problem and compare the two major classes of dynamic neural architectures for its solution, namely the…

Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is…