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Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
There is a documented gap between research-based recommendations produced by university-based scholars in the field of education in the United States and the evidence that U.S. politicians' use when deciding which educational policies to implement or amend. This is a problem because university-based education scholars produce vast quantities of research each year, some of which could, and more importantly should, be useful to politicians in their decision-making processes and yet, politicians continue to make policy decisions about education without the benefit of much of the knowledge that has been gained through scholarly research. I refer to the small fraction of university-based education scholars who are demonstrably successful at getting scholarly research into the hands of politicians to be used for decision-making purposes as "university-based bipartisan scholarship brokers". They are distinct from other university-based education scholars in that they engage with politicians from both political parties around research and, as such, are able to use scholarly research to influence the education policymaking process. The problem that this dissertation addresses is the lack of use, by U.S. politicians, of scholarly research produced by United States university-based education scholars as input in education policy decisions. The way in which this problem is explored is through studying university-based bipartisan scholarship brokers. I focused on three areas for exploration: the methods university-based bipartisan scholarship brokers use to successfully get U.S. politicians to consider scholarly research as an input in their decision-making processes around education policy, how these scholars are different than the majority of university-based education policy scholars, and how they conceive of the education policy-setting agenda. What I uncovered in this dissertation is that university-based bipartisan scholarship brokers are a complete sub-group of university-based education scholars. They work above the rigorous promotion and tenure requirements of their home universities in order to use scholarly research to help serve the research needs of politicians. Their engagement is distinct among university-based education scholars and through this dissertation their perspective is presented in participants' own authentic language.
ContributorsAckman, Emily Rydel (Author) / Garcia, David R. (Thesis advisor) / Powers, Jeanne (Committee member) / Fischman, Gustavo E (Committee member) / Arizona State University (Publisher)
Created2013
Description
As the complexity of robotic systems and applications grows rapidly, development of high-performance, easy to use, and fully integrated development environments for those systems is inevitable. Model-Based Design (MBD) of dynamic systems using engineering software such as Simulink® from MathWorks®, SciCos from Metalau team and SystemModeler® from Wolfram® is quite popular nowadays. They provide tools for modeling, simulation, verification and in some cases automatic code generation for desktop applications, embedded systems and robots. For real-world implementation of models on the actual hardware, those models should be converted into compilable machine code either manually or automatically. Due to the complexity of robotic systems, manual code translation from model to code is not a feasible optimal solution so we need to move towards automated code generation for such systems. MathWorks® offers code generation facilities called Coder® products for this purpose. However in order to fully exploit the power of model-based design and code generation tools for robotic applications, we need to enhance those software systems by adding and modifying toolboxes, files and other artifacts as well as developing guidelines and procedures. In this thesis, an effort has been made to propose a guideline as well as a Simulink® library, StateFlow® interface API and a C/C++ interface API to complete this toolchain for NAO humanoid robots. Thus the model of the hierarchical control architecture can be easily and properly converted to code and built for implementation.
ContributorsRaji Kermani, Ramtin (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Our research focuses on finding answers through decentralized search, for complex, imprecise queries (such as "Which is the best hair salon nearby?") in situations where there is a spatiotemporal constraint (say answer needs to be found within 15 minutes) associated with the query. In general, human networks are good in answering imprecise queries. We try to use the social network of a person to answer his query. Our research aims at designing a framework that exploits the user's social network in order to maximize the answers for a given query. Exploiting an user's social network has several challenges. The major challenge is that the user's immediate social circle may not possess the answer for the given query, and hence the framework designed needs to carry out the query diffusion process across the network. The next challenge involves in finding the right set of seeds to pass the query to in the user's social circle. One other challenge is to incentivize people in the social network to respond to the query and thereby maximize the quality and quantity of replies. Our proposed framework is a mobile application where an individual can either respond to the query or forward it to his friends. We simulated the query diffusion process in three types of graphs: Small World, Random and Preferential Attachment. Given a type of network and a particular query, we carried out the query diffusion by selecting seeds based on attributes of the seed. The main attributes are Topic relevance, Replying or Forwarding probability and Time to Respond. We found that there is a considerable increase in the number of replies attained, even without saturating the user's network, if we adopt an optimal seed selection process. We found the output of the optimal algorithm to be satisfactory as the number of replies received at the interrogator's end was close to three times the number of neighbors an interrogator has. We addressed the challenge of incentivizing people to respond by associating a particular amount of points for each query asked, and awarding the same to people involved in answering the query. Thus, we aim to design a mobile application based on our proposed framework so that it helps in maximizing the replies for the interrogator's query by diffusing the query across his/her social network.
ContributorsSwaminathan, Neelakantan (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the advent of social media (like Twitter, Facebook etc.,) people are easily sharing their opinions, sentiments and enforcing their ideologies on others like never before. Even people who are otherwise socially inactive would like to share their thoughts on current affairs by tweeting and sharing news feeds with their friends and acquaintances. In this thesis study, we chose Twitter as our main data platform to analyze shifts and movements of 27 political organizations in Indonesia. So far, we have collected over 30 million tweets and 150,000 news articles from RSS feeds of the corresponding organizations for our analysis. For Twitter data extraction, we developed a multi-threaded application which seamlessly extracts, cleans and stores millions of tweets matching our keywords from Twitter Streaming API. For keyword extraction, we used topics and perspectives which were extracted using n-grams techniques and later approved by our social scientists. After the data is extracted, we aggregate the tweet contents that belong to every user on a weekly basis. Finally, we applied linear and logistic regression using SLEP, an open source sparse learning package to compute weekly score for users and mapping them to one of the 27 organizations on a radical or counter radical scale. Since, we are mapping users to organizations on a weekly basis, we are able to track user's behavior and important new events that triggered shifts among users between organizations. This thesis study can further be extended to identify topics and organization specific influential users and new users from various social media platforms like Facebook, YouTube etc. can easily be mapped to existing organizations on a radical or counter-radical scale.
ContributorsPoornachandran, Sathishkumar (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2013
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study sought to analyze the messages being conveyed through the discourse utilized in presenting the public face of The Arizona Early Childhood Development and Health Board, popularly known as First Things First (FTF) and to reveal how the different discourses and ideologies within FTF have been in the past and currently are "contending and struggling for dominance (Wodak, 2007)." FTF is located within the policy realm of Early Childhood Education and Care (ECEC). The people and the system have been very influential in guiding the course and policies set forth in Arizona since the citizen initiative, Proposition 203, passed in 2006, which allowed for the creation of the Early Childhood Development and Health Board. Lakoff's techniques for analyzing frames of discourse were utilized in conjunction with critical discourse analysis in order to tease out frames of reference, shifts in both discourse and frames, specific modes of messaging, and consistencies and inconsistencies within the public face presented by FTF.
ContributorsMiller, Lisa (Author) / Swadener, Elizabeth B (Thesis advisor) / Nakagawa, Kathy (Committee member) / Romero, Mary (Committee member) / Arizona State University (Publisher)
Created2013
Description
As digital media practices become readily available in today's classrooms, literacy and literacy instruction are changing in profound ways (Alvermann, 2010). Professional organizations emphasize the importance of integrating new literacies (New London Group, 1996) practices into language-arts instruction (IRA, 2009; NCTE, 2005). As a result, teachers search for effective ways to incorporate the new literacies in an effort to engage students. Therefore, this study was designed to investigate the potential of digital storytelling as participatory media for writing instruction. This case study was conducted during the fall semester of 2012 in one first-grade classroom and one second-grade classroom in the Southwestern United States. The study addressed ten interrelated research questions relating to how primary-grade students performed in relation to the Common Core writing standards, how they were motivated, how they formed a meta- language to talk about their writing, how they developed identities as writers, and how they were influenced by their teachers' philosophies and instructional approaches. Twenty-two first-grade students and 24 second-grade students used the MovieMaker software to create digital stories of personal narratives. Data included field notes, interviews with teachers and students, teacher journals, my own journal, artifacts of teachers' lesson plans, photographs, students' writing samples, and their digital stories. Qualitative data were analyzed by thematic analysis (Patton, 1990) and discourse analysis (Gee, 2011). Writing samples were scored by rubrics based on the Common Core State Standards. The study demonstrated how digital storytelling can be used to; (a) guide teachers in implementing new literacies in primary grades; (b) illustrate digital storytelling as writing; (c) develop students' meta-language to talk about writing; (d) impact students' perceptions as writers; (e) meet Common Core State Standards for writing; (f) improve students' skills as writers; (g) build students' identities as writers; (h) impact academic writing; (i) engage students in the writing process; and (j) illustrate the differences in writing competencies between first- and second-grade students. The study provides suggestions for teachers interested in incorporating digital storytelling in primary-grade classrooms.
ContributorsFoley, Leslie M (Author) / Guzzetti, Barbara J. (Thesis advisor) / Hayes, Elisabeth R. (Committee member) / Gee, James P (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The purpose of this study was to investigate critical literacy practices in two prehistoric exhibits in a natural history museum. Bourdieu's habitus and Bakhtin's dialogism served as theoretical frames to collect and analyze data. Data were collected and triangulated using field notes, interview transcriptions, archives, and other data sources to critically scrutinize textual meaning and participant responses. Spradley's (1979) domain analysis was used to sort and categorize data in the early stage. Glaser and Strauss's (1967) constant comparative method was used to code data. My major findings were that museum texts within this context represent embedded beliefs and values that were interwoven with curators` habitus, tastes and capital, as well as institutional policies. The texts in the two Hohokam exhibits endorse a certain viewpoint of learning. Teachers and the public were not aware of the communicative role that the museum played in the society. In addition, museum literacy/ies were still practiced in a fundamental way as current practices in the classroom, which may not support the development of critical literacy. In conclusion, the very goal for critical museum literacy is to help students and teachers develop intellectual strategies to read the word and the world in informal learning environments.
ContributorsLiang, Sheau-yann (Author) / Mccarty, Teresa (Thesis advisor) / Marsh, Josephine (Committee member) / Blumenfeld-Jones, Donald (Committee member) / Welsh, Peter (Committee member) / Arizona State University (Publisher)
Created2013