ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 4 of 4
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- All Subjects: Educational technology
- Creators: Hsiao, Ihan
Description
Online programming communities are widely used by programmers for troubleshooting or various problem solving tasks. Large and ever increasing volume of posts on these communities demands more efforts to read and comprehend thus making it harder to find relevant information. In my thesis; I designed and studied an alternate approach by using interactive network visualization to represent relevant search results for online programming discussion forums.
I conducted user study to evaluate the effectiveness of this approach. Results show that users were able to identify relevant information more precisely via visual interface as compared to traditional list based approach. Network visualization demonstrated effective search-result navigation support to facilitate user’s tasks and improved query quality for successive queries. Subjective evaluation also showed that visualizing search results conveys more semantic information in efficient manner and makes searching more effective.
I conducted user study to evaluate the effectiveness of this approach. Results show that users were able to identify relevant information more precisely via visual interface as compared to traditional list based approach. Network visualization demonstrated effective search-result navigation support to facilitate user’s tasks and improved query quality for successive queries. Subjective evaluation also showed that visualizing search results conveys more semantic information in efficient manner and makes searching more effective.
ContributorsMehta, Vishal Vimal (Author) / Hsiao, Ihan (Thesis advisor) / Walker, Erin (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2015
Description
Online discussion forums have become an integral part of education and are large repositories of valuable information. They facilitate exploratory learning by allowing users to review and respond to the work of others and approach learning in diverse ways. This research investigates the different comment semantic features and the effect they have on the quality of a post in a large-scale discussion forum. We survey the relevant literature and employ the key content quality identification features. We then construct comment semantics features and build several regression models to explore the value of comment semantics dynamics. The results reconfirm the usefulness of several essential quality predictors, including time, reputation, length, and editorship. We also found that comment semantics are valuable to shape the answer quality. Specifically, the diversity of comments significantly contributes to the answer quality. In addition, when searching for good quality answers, it is important to look for global semantics dynamics (diversity), rather than observe local differences (disputable content). Finally, the presence of comments shepherd the community to revise the posts by attracting attentions to the posts and eventually facilitate the editing process.
ContributorsAggarwal, Adithya (Author) / Hsiao, Ihan (Thesis advisor) / Lopez, Claudia (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming domain. They may choose to utilize these opportunities to self-assess their learning progress and practice their skill. My objective in this thesis is to understand to what extent self-assess process can impact novice programmers learning and what advanced learning technologies can I provide to enhance the learner’s outcome and the progress. In this dissertation, I conducted a series of studies to investigate learning analytics and students’ behaviors in working on self-assessments and reflection opportunities. To enable this objective, I designed a personalized learning platform named QuizIT that provides daily quizzes to support learners in the computer science domain. QuizIT adopts an Open Social Student Model (OSSM) that supports personalized learning and serves as a self-assessment system. It aims to ignite self-regulating behavior and engage students in the self-assessment and reflective procedure. I designed and integrated the personalized practice recommender to the platform to investigate the self-assessment process. I also evaluated the self-assessment behavioral trails as a predictor to the students’ performance. The statistical indicators suggested that the distributed reflections were associated with the learner's performance. I proceeded to address whether distributed reflections enable self-regulating behavior and lead to better learning in CS introductory courses. From the student interactions with the system, I found distinct behavioral patterns that showed early signs of the learners' performance trajectory. The utilization of the personalized recommender improved the student’s engagement and performance in the self-assessment procedure. When I focused on enhancing reflections impact during self-assessment sessions through weekly opportunities, the learners in the CS domain showed better self-regulating learning behavior when utilizing those opportunities. The weekly reflections provided by the learners were able to capture more reflective features than the daily opportunities. Overall, this dissertation demonstrates the effectiveness of the learning technologies, including adaptive recommender and reflection, to support novice programming learners and their self-assessing processes.
ContributorsAlzaid, Mohammed (Author) / Hsiao, Ihan (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / VanLehn, Kurt (Committee member) / Nelson, Brian (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2022
Description
Computer supported collaborative learning (CSCL) has made great inroads in classroom teaching marked by the use of tools and technologies to support and enhance collaborative learning. Computer mediated learning environments produce large amounts of data, capturing student interactions, which can be used to analyze students’ learning behaviors (Martinez-Maldonado et al., 2013a). The analysis of the process of collaboration is an active area of research in CSCL. Contributing towards this area, Meier et al. (2007) defined nine dimensions and gave a rating scheme to assess the quality of collaboration. This thesis aims to extract and examine frequent patterns of students’ interactions that characterize strong and weak groups across the above dimensions. To achieve this, an exploratory data mining technique, differential sequence mining, was employed using data from a collaborative concept mapping activity where collaboration amongst students was facilitated by an interactive tabletop. The results associate frequent patterns of collaborative concept mapping process with some of the dimensions assessing the quality of collaboration. The analysis of associating these patterns with the dimensions of collaboration is theoretically grounded, considering aspects of collaborative learning, concept mapping, communication, group cognition and information processing. The results are preliminary but still demonstrate the potential of associating frequent patterns of interactions with strong and weak groups across specific dimensions of collaboration, which is relevant for students, teachers, and researchers to monitor the process of collaborative learning. The frequent patterns for strong groups reflected conformance to the process of conversation for dimensions related to “communication” aspect of collaboration. In terms of the concept mapping sub-processes the frequent patterns for strong groups reflect the presentation phase of conversation with processes like talking, sharing individual maps while constructing the groups concept map followed by short utterances which represents the acceptance phase. For “joint information processing” aspect of collaboration, the frequent patterns for strong groups were marked by learners’ contributing more upon each other’s work. In terms of the concept mapping sub-processes the frequent patterns were marked by learners adding links to each other’s concepts or working with each other’s concepts, while revising the group concept map.
ContributorsChaudhry, Rishabh (Author) / Walker, Erin A (Thesis advisor) / Maldonado-Martinez, Roberto (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2015