Understanding the relationship between fractions and decimals is an important step in developing an overall understanding of rational numbers. Research has demonstrated the feasibility of technology in the form of virtual manipulatives for facilitating students' meaningful understanding of rational number concepts. This exploratory dissertation…

Advances in technology now make it possible to capture detailed multimodal data about real-world everyday activity. Researchers have taken advantage of these advances to address questions about activity in more systematic and precise ways. Along with exciting opportunities to record data in ways that were not possible before, there are also…

Recent scholarship in Human-Computer Interaction, science and technology studies, and design research has focused on hacker communities as sites of innovation and entrepreneurship, novel forms of education, and the democratization of technological production. However, hacking practices are more than new technical practices; they are also…

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look…

In many evolving systems, inputs can be derived from both human observations and physical sensors. Additionally, many computation and analysis tasks can be performed by either human beings or artificial intelligence (AI) applications. For example, weather prediction, emergency event response, assistive technology for various human sensory and…

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

A basic and classical assumption in the machine learning research area is "randomness assumption" (also known as i.i.d assumption), which states that data are assumed to be independent and identically generated by some known or unknown distribution. This assumption, which is the foundation of most existing approaches in the literature, simplifies…