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.

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, ways of answering questions, or specific pages on an LMS. As a result, the analysis is only relevant to classrooms that use the specific LMS being analyzed.

For this thesis, behavior metrics obtained by the Organic Practice Environment (OPE) LMS at Arizona State University were compared to student performance in Dr. Ian Gould’s Organic Chemistry I course. Each metric gathered was generic enough to be potentially used by any LMS, allowing the results to be relevant to a larger amount of classrooms. By using a combination of bivariate correlation analysis, group mean comparisons, linear regression model generation, and outlier analysis, the metrics that correlate best to exam performance were identified. The results indicate that the total usage of the LMS, amount of cramming done before exams, correctness of the responses submitted, and duration of the responses submitted all demonstrate a strong correlation with exam scores.

With the advent of social media and micro-blogging sites, people have become active in sharing their thoughts, opinions, ideologies and furthermore enforcing them on others. Users have become the source for the production and dissemination of real time information. The content posted by the users can be used to understand them and track their behavior. Using this content of the user, data analysis can be performed to understand their social ideology and affinity towards Radical and Counter-Radical Movements. During the process of expressing their opinions people use hashtags in their messages in Twitter. These hashtags are a rich source of information in understanding the content based relationship between the online users apart from the existing context based follower and friend relationship.

An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.