In computer science, learning abstract fundamental programming concepts requiring students to understand memory management can be very difficult and lead to misunderstandings that carry on into advanced topics. This is especially true in data structures with abstract data types. Understanding how novice students think and reason about data structures is...
This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...
As of February 2012, approximately 46% of American adults own a smartphone. The graphics quality of these devices gets better each year. However, they still have many more limitations in graphics processing and storage space than desktop computers. This means that applications on these devices should focus on optimizing their...
We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
The system described is an interface between a student and a problem-solving production system that solves some class of problems. Its purpose is to help a student learn some part of the realm of problem solving. As a student attempts to solve a problem the system controls the firing of...
While digital inclusivity researchers and software practitioners have been trying to address exclusion biases in Windows, Icons, Menus, and Pointers (WIMP) user interfaces (UIs) for a long time, little has been done to investigate if and how inclusive software design and its methods that have been devised for WIMP UIs...
Linear transformation for dimension reduction is a well established problem in the field of machine learning. Due to the numerous observability of parameters and data, processing of the data in its raw form is computationally complex and difficult to visualize. Dimension reduction by means of feature extraction offers a strong...
Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
A decision support system (DSS) incorporating domain
expertise guides, tutors, and consults a decision maker in
opportunity, problem, and crisis identification activities. The
objective for the system is to promote improved decision making.
Using an "Independent Groups" design, an experimental study was
conducted to investigate the effects of DSS use...
Computer science is, at its core, about solving problems. The "Carry out the Plan" portion of problem solving is often examined and emphasized in CS 1 and CS 2, forgetting to emphasize the other important aspects of the problem solving process. This study focuses on the other problem-solving steps, which...