PHD, Kent State University, 2017, College of Arts and Sciences / Department of Computer Science

This research presents a novel learning assessment model to measure the student learning in terms of learning cognitive skill levels and the current knowledge state towards achieving the level. Knowledge state refers to if a student has already learned, or ready to learn, or not ready to learn a certain concept. The cognitive skill levels refers to levels such as if a student has acquired the state at the level of understanding, or applying, or analyzing, etc. a particular concept. The cognitive skill levels are based on Bloom's taxonomy (Anderson, et al., 2001). The knowledge states are defined based on the Knowledge Assessment theory proposed by (Falmagne, Cosyn, Doignon, & Thiery, 2003). Our model is comprised of four constructions. First we propose a semantic/ ontological scheme called Cognitive Level Mapped Concept Graphs (CLMCG), which maps the concepts appearing in a knowledge area and their pedagogical and ontological relations as understood by the domain experts. To capture the organization of the knowledge domain, we use formal syllabus and textbooks written by the experts. The concepts are then mapped in three relational dimensions: the syllabus dimension, the ontology dimension and the cognitive skill dimension needed to perform the analysis. Currently, we extract the relationships along the first two dimensions based on the organization of the concepts as they appear in leading text book(s) and the third dimension following the classifications given by Bloom (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956). We then propose a concept based testing and evaluation scheme for testing students. We then show methods of analysis scheme based on graph traversal and logical inference that can provide the assessment of the student's knowledge in terms to a modified Falmagne state and modified Bloom target skill levels. Finally, we also propose a new set of quantitative measures and scales that we believe can provide much insight as well (open full item for complete abstract)

Committee: Javed Khan (Advisor); Austin Melton (Committee Member); Arvind Bansal (Committee Member); Omar De LA Cruz Cabrera (Committee Member) Subjects: Computer Science

Master of Science in Engineering, University of Toledo, 2013, Engineering (Computer Science)

Radon-222 and its parent Radium-226 are naturally occurring radioactive decay products of Uranium-238. The US Environmental Protection Agency (USEPA) attributes about 10 percent of lung cancer cases that is `around 21,000 deaths per year' in the United States, caused due to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e. the average indoor radon screening level greater than 4 picocuries per liter). In order to implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This thesis presents a comparison between recently developed interpolation techniques to new techniques such as Support Vector Regression (SVR), and Random Forest Regression (RFR). Recently developed interpolation techniques include Artificial Neural Network (ANN), Knowledge Based Neural Networks (KBNN), Correction-Based Artificial Neural Networks (CBNN) and the conventional interpolation techniques such as Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI) and Radial Basis Function (RBF) using the K-fold cross validation method.

Committee: William Acosta (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ashok Kumar (Committee Member); Rob Green (Committee Member) Subjects: Computer Science

Master of Computing and Information Systems, Youngstown State University, 2008, Department of Computer Science and Information Systems

In this paper, we explore the concept of epistemic visualization in interrogative domains. Epistemic visualization is the process and result of developing visual models that capture the structure, content, justification and acquisition of knowledge obtained by a software agent in a knowledge-based system. The knowledge is the foundation in which the agent can respond to queries against a corpus containing questions and answers. The visualizations are therefore used to examine the quality of the software agent's knowledge. The visual models will include justification and commitment artifacts as well as knowledge acquisition flow. The visualization will demarcate the a priori and posteriori knowledge. The knowledge of the software agent is stored in epistemic structures which are knowledge representation schemes that supports the basic concepts of knowledge as defined by the tripartite analysis of knowledge. Epistemic visualization is used to analyze the quality of the knowledge of a software agent in an interrogative domain. For our purpose, interrogative domains are hearings, trials, interrogations, personality test or any document source in which the primary content is questions and answers pairs. In this paper, we introduce the Epistemic Structure Es that captures the agent's knowledge and the visualization of that epistemic structure using common visualization techniques.

Committee: Alina Lazar PhD (Committee Chair); John Sullins PhD (Committee Member); Yong Zhang PhD (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Systems; Linguistics; Technology

Master of Science, University of Toledo, 2010, Engineering (Computer Science)

Radon is a chemically inert, naturally occurring radioactive gas. It is one of the main causes of lung cancer second to smoking, and accounts for about 25,000 deaths every year in the US alone according to the National Cancer Institute. In order to initiate preventative measures to reduce the deaths caused by radon inhalation, it is helpful to have radon concentration data for each locality, e.g. zip code. However, such data are not available for every zip code in Ohio, owing to several reasons including inapproachability. In places where data is unavailable, radon concentrations must be estimated using interpolation techniques to take appropriate preventive measures against cancer. This thesis proposes new interpolation techniques based on Artificial Neural Networks utilizing the available knowledge in terms of Radon concentration data and Uranium concentration data for modeling and predicting Radon concentrations in Ohio, US. Several models were first trained and then validated using available data to identify the best model for each technique. Model accuracies using the proposed approaches were proven to be significantly better in comparison to conventional interpolation techniques such as Kriging and Radial Basis Functions.

Committee: Vijay Devabhaktuni PhD (Advisor); Ashok Kumar PhD (Advisor); Mohammed Niamat PhD (Committee Member) Subjects: Environmental Engineering

Doctor of Philosophy, Miami University, 2006, Psychology

An abstraction-decomposition space (ADS) has been used to characterize functional and structural relationships of complex systems. This study used an ADS to (a) describe a knowledge domain of working memory in a cognitive psychology, (b) examine whether organizing the presentation of information about that knowledge domain using an ADS could facilitate its comprehension for students in a cognitive psychology course, and (c) describe how students reasoned within that domain when trying to solve problems. Experiment 1A provided 34 students with an opportunity to study the course material for about an hour using either an ADS-based module or one based on a more traditional hierarchical approach. There was no difference between the groups on any of the pretest or posttest measures. It was proposed that students might need more extended use of the ADS module in order to benefit from its organizational structure. In Experiment 1B, 8 students from another cognitive psychology course were given the ADS module during class and asked to use it to study instead of their textbook. After one week, students were given a test equivalent to those used in Experiment 1A. After several weeks they received another test followed by a third test after another 2-3 weeks. Not only did their performance remain high across all three tests, their performance was also significantly higher than the performance of the ADS group from Experiment 1A . These results suggest that extended use may be necessary in order to take advantage of the organizational structure of the ADS. In Experiment 2 explanations/justifications provided by participants in Experiment 1 for some of their answers to the multiple choice questions on the tests were analyzed by mapping their verbal explanations onto the ADS. These explanations were then sorted into categories and compared to performance on the specific multiple choice question. The results showed that those students who correctly abstracted within the domain usu (open full item for complete abstract)

Committee: Leonard Mark (Advisor) Subjects: