Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
This study empirically evaluated the effectiveness of the instructional design, learning tools, and role of the teacher in three versions of a semester-long, high-school remedial Algebra I course to determine what impact self-regulated learning skills and learning pattern training have on students' self-regulation, math achievement, and motivation. The 1st version was a business-as-usual traditional classroom teaching mathematics with direct instruction. The 2rd version of the course provided students with self-paced, individualized Algebra instruction with a web-based, intelligent tutor. The 3rd version of the course coupled self-paced, individualized instruction on the web-based, intelligent Algebra tutor coupled with a series of e-learning modules on self-regulated learning knowledge and skills that were distributed throughout the semester. A quasi-experimental, mixed methods evaluation design was used by assigning pre-registered, high-school remedial Algebra I class periods made up of an approximately equal number of students to one of the three study conditions or course versions: (a) the control course design, (b) web-based, intelligent tutor only course design, and (c) web-based, intelligent tutor + SRL e-learning modules course design. While no statistically significant differences on SRL skills, math achievement or motivation were found between the three conditions, effect-size estimates provide suggestive evidence that using the SRL e-learning modules based on ARCS motivation model (Keller, 2010) and Let Me Learn learning pattern instruction (Dawkins, Kottkamp, & Johnston, 2010) may help students regulate their learning and improve their study skills while using a web-based, intelligent Algebra tutor as evidenced by positive impacts on math achievement, motivation, and self-regulated learning skills. The study also explored predictive analyses using multiple regression and found that predictive models based on independent variables aligned to student demographics, learning mastery skills, and ARCS motivational factors are helpful in defining how to further refine course design and design learning evaluations that measure achievement, motivation, and self-regulated learning in web-based learning environments, including intelligent tutoring systems.
ContributorsBarrus, Angela (Author) / Atkinson, Robert K (Thesis advisor) / Van de Sande, Carla (Committee member) / Savenye, Wilhelmina (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
ABSTRACT This study describes student interactions in the academic social network site Edmodo versus student interactions in Facebook. This qualitative case study relies upon four high school juniors enrolled in Advanced Placement Language and Composition who use Edmodo to complete assignments for their English class. Their experiences were gathered in an attempt to describe specific experiences in a complex system. Students were selected using an Internet Connectedness Index survey. Using a Virtual Community of Practice framework, students were asked about their experiences in Edmodo. This study concludes that Edmodo and Facebook can be compared in three categories: accessibility, functionality, and environment. Unlike Facebook, which students access regularly, students access Edmodo only to fulfill the teacher's participation expectations for the specific grade they wish to receive. Additionally, students appreciated the convenience of using Edmodo to complete assignments. The functionality of Edmodo is quite similar in layout and appearance to Facebook, yet students were unaware of the media sharing capability, wished for private messaging options, and desired the ability to tag peers for direct comment using the @ sign, all options that are available in Facebook. Students felt the environment in Edmodo could best be characterized as intellectual and academic, which some mentioned might best be used with honors or AP students. A surprising benefit of Edmodo is the lack of social cues enable students to feel free of judgment when composing writing. Some felt this allowed students to know their classmates better and share their true personae free from judgment of classmates. As a result of the case studies of four students, this study seeks to illustrate how students interact in Edmodo versus Facebook to provide a robust image of the academic social network site for teachers seeking to implement educational technology in their classes.
ContributorsCurran-Sejkora, Elizabeth (Author) / Blasingame, James (Thesis advisor) / Nilsen, Alleen (Committee member) / Rodrigo, Rochelle (Committee member) / Turchi, Laura (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 dissertation describes the development of a state-of-the-art immersive media environment and its potential to motivate high school youth with autism to vocally express themselves. Due to the limited availability of media environments in public education settings, studies on the use of such systems in special education contexts are rare. A study called Sea of Signs utilized the Situated Multimodal Art Learning Lab (SMALLab), to present a custom-designed conversational scenario for pairs of youth with autism. Heuristics for building the scenario were developed following a 4-year design-based research approach that fosters social interaction, communication, and self-expression through embodied design. Sea of Signs implemented these heuristics through an immersive experience, supported by spatial and audio-visual feedback that helped clarify and reinforce students' vocal expressions within a partner-based conversational framework. A multiple-baseline design across participants was used to determine the extent to which individuals exhibited observable change as a result of the activity in SMALLab. Teacher interviews were conducted prior to the experimental phase to identify each student's pattern of social interaction, communication, and problem-solving strategies in the classroom. Ethnographic methods and video coding were used throughout the experimental phase to assess whether there were changes in (a) speech duration per session and per turn, (b) turn-taking patterns, and (c) teacher prompting per session. In addition, teacher interviews were conducted daily after every SMALLab session to further triangulate the nature of behaviors observed in each session. Final teacher interviews were conducted after the experimental phase to collect data on possible transfer of behavioral improvements into students' classroom lives beyond SMALLab. Results from this study suggest that the activity successfully increased independently generated speech in some students, while increasing a focus on seeking out social partners in others. Furthermore, the activity indicated a number of future directions in research on the nature of voice and discourse, rooted in the use of aesthetics and phenomenology, to augment, extend, and encourage developments in directed communication skills for youth with autism.
ContributorsTolentino, Lisa (Author) / Paine, Garth (Thesis advisor) / Kozleski, Elizabeth B. (Thesis advisor) / Kelliher, Aisling (Committee member) / Megowan-Romanowicz, Colleen (Committee member) / Arizona State University (Publisher)
Created2013
Description
An integral part of teacher development are teacher observations. Many teachers are observed once or twice a year to evaluate their performance and hold them accountable for meeting standards. Instructional coaches, however, observe and work with teachers to help them reflect on their performance, with the goal of improving their practice. Video-based evidence has long been used in connection with teacher reflection and as the technology necessary to record video has become more readily available, video recordings have found an increasing presence in teacher observations. In addition, more and more schools are turning to mobile technology to help record evidence during teacher observations. Several mobile applications have been developed, which are designed to help instructional coaches, administrators, and teachers make the most of teacher observations. This study looked at the use of the DataCapture mobile application to record video-based evidence in teacher observations as part of an instructional coaching program in a large public school district in the Southwestern United States. Six instructional coaches and two teachers participated in interviews at the end of the study period. Additional data was collected from the DataCapture mobile application and from a survey of instructional coaches conducted by the school district in connection with its Title I programs. Results show that instructional coaches feel that using video-based evidence for teacher reflection is effective in a number of ways. Teachers who have experienced seeing themselves on video also felt that video-based evidence is effective at improving teacher reflection, while teachers who have not yet experienced seeing themselves on video displayed extreme apprehensiveness about being video recorded in the classroom. Instructional coaches felt the DataCapture mobile application was beneficial in teacher evaluation, but there were several issues that impacted the use of the mobile application and video-based evidence, including logistics, time requirements, and administrative support. The discussion focuses on recommendations for successfully using video-based evidence in an instructional coaching context, as well as some suggestions for other researchers attempting to study how video-based evidence impacts teachers' ability to reflect on their own teaching.
ContributorsShewell, Justin Reed (Author) / Bitter, Gary (Thesis advisor) / Dawson, Edwin (Committee member) / Blair, Heidi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Random peptide microarrays are a powerful tool for both the treatment and diagnostics of infectious diseases. On the treatment side, selected random peptides on the microarray have either binding or lytic potency against certain pathogens cells, thus they can be synthesized into new antimicrobial agents, denoted as synbodies (synthetic antibodies). On the diagnostic side, serum containing specific infection-related antibodies create unique and distinct "pathogen-immunosignatures" on the random peptide microarray distinct from the healthy control serum, and this different mode of binding can be used as a more precise measurement than traditional ELISA tests. My thesis project is separated into these two parts: the first part falls into the treatment side and the second one focuses on the diagnostic side. My first chapter shows that a substitution amino acid peptide library helps to improve the activity of a recently reported synthetic antimicrobial peptide selected by the random peptide microarray. By substituting one or two amino acids of the original lead peptide, the new substitutes show changed hemolytic effects against mouse red blood cells and changed potency against two pathogens: Staphylococcus aureus and Pseudomonas aeruginosa. Two new substitutes are then combined together to form the synbody, which shows a significantly antimicrobial potency against Staphylococcus aureus (<0.5uM). In the second chapter, I explore the possibility of using the 10K Ver.2 random peptide microarray to monitor the humoral immune response of dengue. Over 2.5 billion people (40% of the world's population) live in dengue transmitting areas. However, currently there is no efficient dengue treatment or vaccine. Here, with limited dengue patient serum samples, we show that the immunosignature has the potential to not only distinguish the dengue infection from non-infected people, but also the primary dengue infection from the secondary dengue infections, dengue infection from West Nile Virus (WNV) infection, and even between different dengue serotypes. By further bioinformatic analysis, we demonstrate that the significant peptides selected to distinguish dengue infected and normal samples may indicate the epitopes responsible for the immune response.
ContributorsWang, Xiao (Author) / Johnston, Stephen Albert (Thesis advisor) / Blattman, Joseph (Committee member) / Arntzen, Charles (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.
ContributorsVerdicchio, Michael (Author) / Kim, Seungchan (Thesis advisor) / Baral, Chitta (Committee member) / Stolovitzky, Gustavo (Committee member) / Collofello, James (Committee member) / Arizona State University (Publisher)
Created2013
Description
Researchers have postulated that math academic achievement increases student success in college (Lee, 2012; Silverman & Seidman, 2011; Vigdor, 2013), yet 80% of universities and 98% of community colleges require many of their first-year students to be placed in remedial courses (Bettinger & Long, 2009). Many high school graduates are entering college ill prepared for the rigors of higher education, lacking understanding of basic and important principles (ACT, 2012). The desire to increase academic achievement is a wide held aspiration in education and the idea of adapting instruction to individuals is one approach to accomplish this goal (Lalley & Gentile, 2009a). Frequently, adaptive learning environments rely on a mastery learning approach, it is thought that when students are afforded the opportunity to master the material, deeper and more meaningful learning is likely to occur. Researchers generally agree that the learning environment, the teaching approach, and the students' attributes are all important to understanding the conditions that promote academic achievement (Bandura, 1977; Bloom, 1968; Guskey, 2010; Cassen, Feinstein & Graham, 2008; Changeiywo, Wambugu & Wachanga, 2011; Lee, 2012; Schunk, 1991; Van Dinther, Dochy & Segers, 2011). The present study investigated the role of college students' affective attributes and skills, such as academic competence and academic resilience, in an adaptive mastery-based learning environment on their academic performance, while enrolled in a remedial mathematics course. The results showed that the combined influence of students' affective attributes and academic resilience had a statistically significant effect on students' academic performance. Further, the mastery-based learning environment also had a significant effect on their academic competence and academic performance.
ContributorsFoshee, Cecile Mary (Author) / Atkinson, Robert K (Thesis advisor) / Elliott, Stephen N. (Committee member) / Horan, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
The field of education has been immensely benefited by major breakthroughs in technology. The arrival of computers and the internet made student-teacher interaction from different parts of the world viable, increasing the reach of the educator to hitherto remote corners of the world. The arrival of mobile phones in the recent past has the potential to provide the next paradigm shift in the way education is conducted. It combines the universal reach and powerful visualization capabilities of the computer with intimacy and portability. Engineering education is a field which can exploit the benefits of mobile devices to enhance learning and spread essential technical know-how to different parts of the world. In this thesis, I present AJDSP, an Android application evolved from JDSP, providing an intuitive and a easy to use environment for signal processing education. AJDSP is a graphical programming laboratory for digital signal processing developed for the Android platform. It is designed to provide utility; both as a supplement to traditional classroom learning and as a tool for self-learning. The architecture of AJDSP is based on the Model-View-Controller paradigm optimized for the Android platform. The extensive set of function modules cover a wide range of basic signal processing areas such as convolution, fast Fourier transform, z transform and filter design. The simple and intuitive user interface inspired from iJDSP is designed to facilitate ease of navigation and to provide the user with an intimate learning environment. Rich visualizations necessary to understand mathematically intensive signal processing algorithms have been incorporated into the software. Interactive demonstrations boosting student understanding of concepts like convolution and the relation between different signal domains have also been developed. A set of detailed assessments to evaluate the application has been conducted for graduate and senior-level undergraduate students.
ContributorsRanganath, Suhas (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013