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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.


Date Range
2015 2019


In the last few years, there has been a tremendous increase in the use of big data. Most of this data is hard to understand because of its size and dimensions. The importance of this problem can be emphasized by the fact that Big Data Research and Development Initiative was announced by the United States administration in 2012 to address problems faced by the government. Various states and cities in the US gather spatial data about incidents like police calls for service. When we query large amounts of data, it may lead to a lot of questions. For example, when …

Contributors
Tahir, Anique, Elsayed, Mohamed, Hsiao, Ihan, et al.
Created Date
2018

Programming is quickly becoming as ubiquitous and essential a skill as general mathematics. However, many elementary and high school students are still not aware of what the computer science field entails. To make matters worse, students who are introduced to computer science are frequently being fed only part of what it is about rather than its entire construction. Consequently, they feel out of their depth when they approach college. Research has discovered that by teaching computer science and programming through a problem-driven approach and focusing on a combination of syntax and computational thinking, students can be prepared when entering higher …

Contributors
Kury, Nizar, Nelson, Brian C, Hsiao, Ihan, et al.
Created Date
2017

Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on …

Contributors
Zhang, Lishan, VanLehn, Kurt, Baral, Chitta, et al.
Created Date
2015

Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions …

Contributors
Yildirim, Mehmet Yigit, Davulcu, Hasan, Bakkaloglu, Bertan, et al.
Created Date
2019

With the rise of Online Social Networks (OSN) in the last decade, social network analysis has become a crucial research topic. The OSN graphs have unique properties that distinguish them from other types of graphs. In this thesis, five month Tweet corpus collected from Bangladesh - between June 2016 and October 2016 is analyzed, in order to detect accounts that belong to groups. These groups consist of official and non-official twitter handles of political organizations and NGOs in Bangladesh. A set of network, temporal, spatial and behavioral features are proposed to discriminate between accounts belonging to individual twitter users, news, …

Contributors
Gore, Chinmay Chandrashekhar, Davulcu, Hasan, Hsiao, Ihan, et al.
Created Date
2017

Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model …

Contributors
Alzahrani, Sultan, Davulcu, Hasan, Corman, Steve R., et al.
Created Date
2018

In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models. Dissertation/Thesis

Contributors
Baskaran, Swetha, Davulcu, Hasan, Sen, Arunabha, et al.
Created Date
2016

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 …

Contributors
Aggarwal, Adithya, Hsiao, Ihan, Lopez, Claudia, et al.
Created Date
2016

In supervised learning, machine learning techniques can be applied to learn a model on a small set of labeled documents which can be used to classify a larger set of unknown documents. Machine learning techniques can be used to analyze a political scenario in a given society. A lot of research has been going on in this field to understand the interactions of various people in the society in response to actions taken by their organizations. This paper talks about understanding the Russian influence on people in Latvia. This is done by building an eeffective model learnt on initial set …

Contributors
Bollapragada, Lakshmi Gayatri Niharika, Davulcu, Hasan, Sen, Arunabha, et al.
Created Date
2016

The growing use of Learning Management Systems (LMS) in classrooms has enabled a great amount of data to be collected about the study behavior of students. Previously, research has been conducted to interpret the collected LMS usage data in order to find the most effective study habits for students. Professors can then use the interpretations to predict which students will perform well and which student will perform poorly in the rest of the course, allowing the professor to better provide assistance to students in need. However, these research attempts have largely analyzed metrics that are specific to certain graphical interfaces, …

Contributors
Beerman, Eric, VanLehn, Kurt, Gould, Ian, et al.
Created Date
2015