Matching Items (260)
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- All Subjects: Machine Learning
- Genre: Doctoral Dissertation
- Creators: Arizona State University
- Resource Type: Text
Description
This qualitative, action research study examines how teacher-writers' identities are constructed through the practice of revision in an extra-curriculum writing group. The writing group was designed to support the teacher-writers as they revised classroom research projects for submission for a scholarly journal. Using discourse analysis, the researcher explores how the teacher-writers' identities are constructed in the contested spaces of revision. This exploration focuses on contested issues that invariably emerge in a dynamic binary of reader/writer, issues of authority, ownership, and unstable reader and writer identities. By negotiating these contested spaces--these contact zones--the teacher-writers construct opportunities to flex their rhetorical agency. Through rhetorical agency, the teacher-writers shift their discoursal identities by discarding and acquiring a variety of discourses. As a result, the practice of revision constructs the teacher-writers identities as hybrid, as consisting of self and other.
ContributorsClark-Oates, Angela (Author) / Smith, Karen (Thesis advisor) / Roen, Duane (Thesis advisor) / Fischman, Gustavo (Committee member) / Early, Jessica (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect to the unknown underlying model. In that regard, this study proposes alternative ways to rank teacher effects that are not dependent on a given model by introducing two variable importance measures (VIMs), the node-proportion and the covariate-proportion. These VIMs are novel because they take into account the final configuration of the terminal nodes in the constitutive trees in a random forest. In a simulation study, under a variety of conditions, true rankings of teacher effects are compared with estimated rankings obtained using three sources: the newly proposed VIMs, existing VIMs, and EBLUPs from the assumed linear model specification. The newly proposed VIMs outperform all others in various scenarios where the model was misspecified. The second study develops two novel interaction measures. These measures could be used within but are not restricted to the VAM framework. The distribution-based measure is constructed to identify interactions in a general setting where a model specification is not assumed in advance. In turn, the mean-based measure is built to estimate interactions when the model specification is assumed to be linear. Both measures are unique in their construction; they take into account not only the outcome values, but also the internal structure of the trees in a random forest. In a separate simulation study, under a variety of conditions, the proposed measures are found to identify and estimate second-order interactions.
ContributorsValdivia, Arturo (Author) / Eubank, Randall (Thesis advisor) / Young, Dennis (Committee member) / Reiser, Mark R. (Committee member) / Kao, Ming-Hung (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this mixed-methods study, I examined the relationship between professional development based on the Common Core State Standards for Mathematics and teacher knowledge, classroom practice, and student learning. Participants were randomly assigned to experimental and control groups. The 50-hour professional development treatment was administered to the treatment group during one semester, and then a follow-up replication treatment was administered to the control group during the subsequent semester. Results revealed significant differences in teacher knowledge as a result of the treatment using two instruments. The Learning Mathematics for Teaching scales were used to detect changes in mathematical knowledge for teaching, and an online sorting task was used to detect changes in teachers' knowledge of their standards. Results also indicated differences in classroom practice between pairs of matched teachers selected to participate in classroom observations and interviews. No statistical difference was detected between the groups' student assessment scores using the district's benchmark assessment system. This efficacy study contributes to the literature in two ways. First, it provides an evidence base for a professional development model designed to promote effective implementation of the Common Core State Standards for Mathematics. Second, it addresses ways to impact and measure teachers' knowledge of curriculum in addition to their mathematical content knowledge. The treatment was designed to focus on knowledge of curriculum, but it also successfully impacted teachers' specialized content knowledge, knowledge of content and students, and knowledge of content and teaching.
ContributorsRimbey, Kimberly A (Author) / Middleton, James A. (Thesis advisor) / Sloane, Finbarr (Committee member) / Atkinson, Robert K (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
Description
Concerted efforts have been made within teacher preparation programs to integrate teaching with technology into the curriculum. Unfortunately, these efforts continue to fall short as teachers' application of educational technology is unsophisticated and not well integrated. The most prevalent approaches to integrating technology tend to ignore pedagogy and content and assume that the technology integration knowledge for all contexts is the same. One theoretical framework that does acknowledge content, pedagogy, and context in conjunction with technology is Technological Pedagogical Content Knowledge (TPACK) and was the lens through which teacher development was measured and interpreted in this study. The purpose of this study was to investigate graduate teacher education students' knowledge and practice of teaching with technology as well as how that knowledge and practice changes after participation in an educational technology course. This study used a mixed-methods sequential explanatory research design in which both quantitative and qualitative data were gathered from 82 participants. TPACK pre- and postcourse surveys were administered to a treatment group enrolled in an educational technology course and to a nonequivalent control group enrolled in a learning theories course. Additionally, pre- and postcourse lesson plans were collected from the treatment group. Select treatment group participants also participated in phone interviews. Analyses compared pre- and post-course survey response differences within and between the treatment and control groups. Pre- and postlesson plan rubric score differences were compared within the treatment group. Quantitative text analyses were performed on the collected lesson plans. Open and axial coding procedures were followed to analyze interview transcripts. The results of the study revealed five significant findings: 1) graduate students entering an educational technology course reported lower ability in constructs related to teaching with technology than in constructs related to teaching in a traditional setting; 2) TPACK was malleable and TPACK instruments were sensitive to that malleability; 3) significant gains in reported and demonstrated TPACK constructs were found after participating in an educational technology course; 4) TPACK construct ability levels vary significantly by participant characteristics; and 5) influences on teaching knowledge and practice range from internet resources, to mentor teachers, and to standardized curriculum packages.
ContributorsSabo, Kent (Author) / Atkinson, Robert (Thesis advisor) / Archambault, Leanna (Committee member) / Savenye, Wilhelmina (Committee member) / Arizona State University (Publisher)
Created2013
Description
Multivariate forms of social oppression, such as racism, linguicism, and heterosexism, are manifested in schools that, as part of our communities, reflect the societal stratification and structural inequalities of a larger society. Teacher educators engaged in multicultural education are responsible for providing pre-service teachers with opportunities to critically examine the intricacies of cultural diversity in U.S. classrooms, developing critical multicultural dispositions. What are effective pedagogical strategies that encourage pre-service teachers to develop such critical multicultural practices? The researcher has found that participatory theatre, including Boalian theatre games, Forum Theatre, Image Theatre, and ethnodrama, can be a transformative, emancipatory pedagogical tool to engage students in critical and creative exploration of cultural diversity. The primary objective of this study is to illustrate how pre-service teachers develop critical consciousness through attending the researcher's multicultural teacher education classroom, which was designed at the nexus of Freirean and Boalian critical (performance) pedagogy, followed by analyzing his teaching practice, which focuses mainly on participatory theatre exercises. This doctoral dissertation is an ethnographic documentary of the researcher's striving to challenge the hegemonic status quo in teacher education by liberating himself from the anti-dialogical banking educator, and encouraging his students to liberate themselves as passive consumers of education. Such reflection may provide teacher educators with examples of counter-hegemonic (artistic) practice for social change relating to their own work.
ContributorsMitsumura, Masakazu (Author) / Tobin, Joseph (Thesis advisor) / Saldana, Johnny (Committee member) / Sterling, Pamela (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
First-year alternatively certified teachers face significant challenges as they attempt to address the complexities of classroom teaching, particularly when they are assigned to teach in urban school settings. As the number of alternatively certified teachers continues to increase, it is important to provide them with professional development opportunities that address the challenges that they encounter in their first year of teaching. This action research study was conducted to examine a professional development model designed to support the development of a small group of first-year alternatively certified teachers in the Mary Lou Fulton Teachers College (MLFTC) at Arizona State University. As first-year teachers within the Induction, Masters, and Certification (InMAC) program, their professional learning needs were unique. They had an immediate need to effectively acquire knowledge and apply it in their teaching practice as they concurrently completed coursework to obtain their master's degree and certification while serving as the teacher of record. This study provided the opportunity for five first-year alternatively certified teachers to participate in a project that provided professional development to meet their specific needs. This two-pronged professional development model included two components: (a) a mentoring component provided by a recently retired master teacher, and (b) a learning community that included opportunities for observation, collaboration, and reflection with National Board Certified teachers. This study was designed to improve teaching practices and increase teaching self-efficacy among the first-year alternatively certified teacher participants. Results from the mixed-method study provided evidence that the model benefited the participants by improving their teaching practices and increasing their teaching self-efficacy. In the discussion, the importance of non-evaluative feedback provided by the mentors was emphasized. Further, highly developed interpersonal relationships, effective communication processes, and helpful collaborative procedures were useful in understanding how alternatively certified teachers benefited from mentor feedback and guidance. Finally, implications for future practice and further research were offered.
ContributorsPreach, Deborah (Author) / Buss, Ray R (Thesis advisor) / Barnett, Joshua (Committee member) / Gasket, Karen (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013