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This dissertation conducted a science classroom intervention using two instructional approaches, computational modeling and paper-based pictorial modeling, in the context of food webs. A series of research papers were written on the impact of the intervention on students' attitudes and learning, and on teachers via professional development and teaching. The first study employed quasi-experimental design, examining the impacts of computational modeling and paper-based pictorial modeling on students' systems thinking and CT skills. A total of 365 seventh-grade students were involved in week-long online learning activities. The students were purposefully assigned to either synchronous computational modeling condition (n = 224) or asynchronous paper-based pictorial modeling condition (n = 141). Students modeled the interrelation between concepts and components of a food web in both conditions. The students took CT and systems thinking-embedded food web assessments before and after the activity and formative assessment after each activity to gather their perceptions of what they thought they learned from the activities. Multilevel modeling and Epistemic Network Analysis were used to analyze the data. The findings indicated a significant increase in students' systems thinking skills regardless of the conditions. Students in computational modeling condition had a substantial increase in their CT scores, while such a result was not detected in the paper-based pictorial modeling condition. ENA results supported this finding, showing that students in computational modeling condition expressed that they learned both science and CT concepts. The second study explored the predictors of students' interests in computationally intensive (or AI-informed) science careers and the impact of computational modeling activity on middle school students' interests in such careers. In parallel, an instrument to measure students' career interests called the Computationally Intensive Science Career Interest (CISCI), was developed and validated. A combination of classical test theory and item response theory was used to validate the CISCI instrument using a sample N = 934. Multiple linear regression and paired-sample "t"-tests were performed to analyze the data. The results showed that the CISCI is a valid and reliable instrument consisting of five constructs. Results from regression tests revealed that students' science attitudes, computer science attitudes, CT skills, and prior computer science-related activities significantly predicted students' career interests. Furthermore, a significant increase in students' perceptions of intensity of discussing computationally intensive careers with their parents was detected after participating in a computational modeling activity. The third study employed a mixed-methods design to investigate the changes in, and sources of, middle school teachers' self-efficacy for teaching science in a computationally rich environment. A total of eleven middle school teachers participated in this study. They took two questionnaires four times--before and after training and teaching--measuring their self-efficacy for teaching science and CT. The teachers were also either interviewed or asked to provide written reflections after taking the questionnaires. Non-parametric Skillings-Mack tests and thematic analysis were used to analyze the data. The results revealed a significant increase in teachers' self-efficacy for teaching science and CT after training and teaching. These changes in self-efficacy were tied to three sources, namely: (1) exposure to computer programming, (2) students' interests and responses to a computationally rich science environment, and (3) teaching repetition and field experience. Results from the three studies highlight the potential benefit of engaging students with learning science in a computationally rich environment to positively influence both their CT and systems thinking skills as well as content knowledge. The study also highlighted the challenges and possibilities for preparing science teachers to teach computational modeling with little or no background in programming or computer science. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The emerging demands for healthcare where access is limited due to political, environmental, or socio-economic factors have been driving research into bio-medical devices that perform in both diagnostic and therapeutic roles at lower costs and greater accessibility. Paper microfluidic devices are used in many applications, particularly medical diagnostics and offer an excellent combination of utility and low cost making them particularly valuable in resource-limited applications and point-of-care usage across a wide variety environmental conditions. Microfluidic biological diagnostics continue to mature as researchers discover new ways to exploit the technological possibilities, and address liabilities. The increasing complexity of paper-based microfluidic devices beyond home pregnancy tests is driving the need to produce new tools and methodologies that enable more robust biological diagnostics and potential therapeutic applications. However, the process of developing new paper microfluidic devices is limited due to having to manually design and fabricate designs to research. Often, researchers must design scores of different devices to find a combination of parameters that functions as expected. In this work, a novel software framework to support automated development of paper-based microfluidic devices is introduced to facilitate both research and fabrication to accelerate the investigative process and reduce material utilization and manpower. Unlike to existing lab-on-a-chip technologies, paper-based microfluidics differs in terms of substrate technologies and use a passive flow method to deliver fluids and reagents for assays. While numerous analogies between microfluidics and semiconductor technologies have been espoused, the physical differences between the fluid dynamics and electrical current are significant which suggests that current trends in physical design for microfluidics must change course in order to be of practical use to designers. Within this framework, a methodology is introduced to address design automation such as dynamically placing and routing microfluidic components in a non-discrete design space while avoiding invalid design layouts, accounting for fluid volume usage, surface area utilization, and the timing required to perform specified biological assays and also optimizing device parameters, enabling researchers to focus on the science and thereby accelerating the development of new, low-resource paper microfluidic devices for a developing world.

Although a great deal of work has been done to develop new educational innovations, in the field of engineering, few innovations have found widespread acceptance in the classroom. Therefore, frameworks that conceptualize the interactions among the variables that influence successful dissemination are critical. In this dissertation, dissemination is defined as a process of first creating an awareness of an educational innovation, then influencing the intention to adopt, the actual adoption, and finally the routine use of that innovation (Fincher, 2000; Gravestock, 2002; Hutchinson & Huberman, 1994; King, 2003). This dissertation establishes the importance of achieving three research goals: (1) developing a framework that describes the interrelationships among the characteristics of educational innovations and an organizations' readiness to disseminate, (2) having an expert panel rank the characteristics of the educational innovations and readiness of faculty members, administrators, and students to disseminate, and (3) testing whether important characteristics of educational innovations and readiness of faculty members variables are significant predictors of successful dissemination. Three papers using different methodologies were sequentially developed to achieve the research goals: (1) a systematic literature review of recent dissemination literature, (2) a Delphi study of grant recipients who had disseminated educational innovations, and (3) a survey of electrical engineering and computer science faculty members. Analysis of the results from these papers led to the following findings: (1) Ease of use of an educational innovation and care about student learning outcomes were significant predictors of intention to adopt that innovation; (2) Efficacy of faculty members toward change and valence moderated the relationship between ease of use and intention to adopt educational innovations; (3) Attitude to educational innovation moderated the relationship between ease of use and intention to adopt educational innovations and moderated the relationship between intention to adopt and adoption; and (4) Awareness of others using an educational innovation was a significant predictor of adopting an educational innovation. The results of this dissertation may be leveraged by developers, faculty members, department chairs, college deans, and grant program managers, to successfully disseminate educational innovations in engineering and technology disciplines. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

With the increasing wealth of information on the Web, information integration is ubiquitous as the same real-world entity may appear in a variety of forms extracted from different sources. This dissertation proposes supervised and unsupervised algorithms that are naturally integrated in a scalable framework to solve the entity resolution problem, which lies at the heart of the information integration process. This dissertation focuses on two incarnations of the entity resolution problem that arise in the data mining and natural language processing areas. First, "name disambiguation" occurs when one is seeking a list of publications of an author in a digital library, who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework that disambiguates the extracted author metadata from paper headers in a divide-and-conquer fashion: based on the metadata records extracted from paper headers, a blocking method retrieves candidate classes of authors with similar names and a density-based clustering method, DBSCAN, clusters the records by author. The distance metric between papers used for clustering is calculated by an online active selection Support Vector Machines algorithm LASVM. We prove that by recasting transitivity as density connectivity in DBSCAN, transitivity is guaranteed for core points. The method achieves high accuracy on a manually labeled dataset and readily disambiguates about a million author metadata records in CiteSeer, which paves the way for the fielded search by author name feature in CiteSeer[superscript X]. Second, as a key step towards document understanding in natural language processing, we investigate the problem of "cross document coreference" (CDC), which aims to decipher the true reference of a named entity across the boundary of documents. This dissertation presents a novel cross document coreference approach that leverages the profiles of entities which are constructed by information extraction tools and reconciled using a within-document coreference module. We propose to match the profiles by using a learned ensemble distance function comprised of a suite of similarity specialists. We develop a kernelized soft relational clustering algorithm that makes use of the learned distance function to partition the entities into fuzzy sets of identities. Evaluation on a large benchmark collection shows that the proposed methods achieve competitive coreference results. We further discuss the details of the implementation of the CDC and web person search system. This dissertation surveys the literature on author name disambiguation in citations and paper headers, citation matching and cross document coreference. Additionally, we explore the social networks of the disambiguated authors, performing a comprehensive study of the network and community level characteristics and proposing a stochastic model to predict collaborations of individuals. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The surface-level experience of hypertextuality as formless and unbounded, blurring boundaries among texts and between readers and writers, is created by a deep structure which is not normally presented to readers and which, like the ultrastructure of living cells, defines and controls texts' nature and functions. Most readers, restricted to surface-level interaction with texts, have little access to the deep structure of any hypertext. In this dissertation, I argue that digital hypertexts differ essentially from paper texts in that hypertexts are constructed in multiple layers, with surface-level appearance and behavior controlled by sub-surface ultrastructure, and that these multiple layers of structure enable and necessitate new methods of textual study designed for digital texts. Using participant-observation from within my own practice as a webmaster, I closely examine the sub-surface structural layers that create several kinds of Web-based digital hypertexts: blogs, forums, static Web pages, and dynamic Web pages. With these hypertexts as the primary models, along with their enabling software and additional digital texts--wikis, news aggregators, word processing documents, digital photographs, electronic mail, electronic forms--available to me as a reader/author rather than a webmaster, I demonstrate methods of investigating and describing the development of digital texts. These methods, like methods already established within textual studies to trace the development of printed texts, can answer questions about accidental and intentional textual change, the roles of collaborators, and the ways texts are shaped by production processes and mediating technologies. As a step toward a formalist criticism of hypertext, I propose concrete ways of categorizing, describing, and comparing hypertexts and their components. I also demonstrate techniques for visualizing the structures, histories, and interrelationships of hypertexts and explore methods of using self-descriptive surface elements in paper-like texts as partial substitutes for the sub-surface self-description available in software-like texts. By identifying digitization as a gateway to cooperation between human and artificial intelligences rather than an end in itself, I suggest natural areas of expansion for the humanities computing collaboration as well as new methodologies by which originally-printed texts can be studied in their digital forms alongside originally-digital texts. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Since the 1980s, personal computer ownership has become ubiquitous, and people are increasingly using household technologies for a wide variety of purposes. Extensive research has resulted in useful models to explain workplace technology acceptance and household technology adoption. Studies have also found that the determinants underlying technology acceptance and adoption in these two environments are very different from the determinants of household use. Thus, while a great deal is known about workplace acceptance and household adoption, less is known about actual household technology use. As home computer use becomes more prevalent, fewer households remain to adopt, and it becomes increasingly important to understand household use. This paper presents the results of an exploratory study of 121 users of household personal computer use. Salient factors from a study that focuses on socioeconomic differences among advantaged and disadvantaged individuals are used to extend the belief structure of the MATH model for prediction of Use of technology in the household. To extend the model two variables were added: "Availability" and "Personal Network Exposure." Of the two variables added to extend the model, only "Availability" returned as important when predicting use of technology in the household. Also, "Perceived Ease of Use," "Self Efficacy," "Utility for Work," and "HLC" were found important. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

As the number of publications in the biomedical field continues its exponential increase, techniques for automatically summarizing information from this body of literature have become more diverse. In addition, the targets of summarization have become more subtle; initial work focused on extracting the factual assertions from full-text papers, while recent interest has shifted to recovering statements involving certainty, like speculations and agreements or disagreements with other research. Scientific writing is rife with such argumentation, and the premises, evidence, conjectures, objections and rebuttals that writers use to persuade the reader represent a rich vein of expert knowledge for summarization. However, recovering these presents substantial challenges as well: processing natural language leads to ambiguity; arguments are made implicitly instead of explicitly; and arguments are nested into complex structures. Agreement, disagreement, and conjecture are often expressed in highly scripted ways in scientific writing, and this feature makes these arguments recoverable by pattern-based search. Here, I present the PARROT software; it recognizes claims pertaining to scientific methods, cognition, discourse, negation, causation, and modality and uses discourse cues to combine these claims recursively into larger rhetorical structures to recover the shape of the arguments made in a scientific publication, which uses OpenDMAP patterns in combination with a Protege ontology. PARROT outperforms an SVM classifier in identifying statements of support and conflict at the sentence level. Additionally, PARROT adapts to graphical representation of the arguments it finds, which makes it an valuable tool for summarizing the reasoning behind scientists' conclusions and identifying areas of consensus and contention. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Inspired by the success of convolutional neural networks in image classification, and other higher level vision tasks, we explore two applications of such deep convolutional neural networks to model tasks typically involving human assessment, viz. i) prediction of personality from social media images, and ii) prediction of citations from the visual elements of an academic paper. The aim in this context is to discover if there is any predictable and learnable signal in the input data. As an extension, we attempt to discover what aspects of the signal are indeed learnt that lead to the results presented. For instance, if personality can be predicted, what aspects of the image are causing that? Similarly if an academic paper is highly cited, what are the characteristic visual elements that cause this? We employ convolutional neural networks in order to understand what imputable attributes we may derive that are simpler to reason.

The 2000 U.S. Presidential Election brought national attention to electronic voting when an estimated 50,000 ballots were discarded and not counted. The body of research presented in this thesis will describe a method that solves electronic voting issues entitled the Accessible Voter Veri able Paper Ballot(AVVPB), which prints a barcode image containing voters' selections made on an electronic voting system, Prime III. Once done printing, voters scan the barcode and through a multimodal user interface voters can either view their vote selections printed on the ballot or listen to their vote selections as it is read back to them by the computer. The multimodality of AVVPB allows voters with various disabilities, e.g. visual impairments, hearing impairments, physical impairments, etc, to independently verify their ballot. Essentially, everyone can be ensured that their vote will be cast as intended, regardless of their ability or disability.

This study was undertaken to determine whether a significant correlation exists between the student's performance on the Advanced Placement (AP) Computer Science Examination and three factors: (1) mathematics scores on the Preliminary Scholastic Aptitude Test (PSAT); (2) a mathematics index score reflecting mathematics courses taken by the student in high and his/her grades in those courses; and (3) the student's overall grade point average. The data were analyzed by calculating the Pearson product-moment correlation between the scores on the AP examination and the selected factors. A significant correlation at the 5% level was found for the selected factors. Only the highest correlating factor, the PSAT mathematics score, was selected for a regression formula after it was found that the inclusion on the other factors did not contribute significantly to the quality of the prediction. Four appendices provide tables displaying the data collected for the study and a description of the data analysis process. (Author/EW)