Browsing by Author "Chon, Jieun"

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    CS5604: Information and Storage Retrieval ​Fall 2017 - FE (Front-End Team) 
    Chon, Jieun; Wang, Haitao; Bian, Yali; Niu, Shuo (Virginia Tech, 2017-12-24)
    Social media and Web data are becoming important sources of information for researchers to monitor and study global events. GETAR, led by Dr. Edward Fox, is a project aiming to collect, organize, browse, visualize, study, analyze, summarize, and explore content and sources related to biodiversity, climate change, crises, disasters, elections, energy policy, environmental policy/planning, geospatial information, green engineering, human rights, inequality, migrations, nuclear power, population growth, resiliency, shootings, sustainability, violence, etc. The report introduces the work of the Front End (FE) team analyzing users' requirements and building user interfaces for people to explore tweet/webpage data. The work of the FE team highly relies on the results from other teams. Our duty includes presenting the collected tweets/webpages, visualizing the clusters and topics, showing the indexed and clustered search results, and last but not least allowing users to perform customized queries and exploration. Therefore the team needs to consider how other teams collect and manage the data, as well as how people utilize the information to gain insights from the data repository. Throughout Fall 2017, our team aims to bridge the data archive and users’ need, focusing on providing various user interfaces for tweet/webpage exploration and analysis. Overall, two main user interfaces are designed and implemented throughout the semester. (1) A visualization-based analytical tool for people to create categories by searching and interacting with filtering tools, which are presented in visualizations such as bar-chart, tag cloud, and node-link graph. (2) A geo-based interface for location-based information, implemented with GeoBlacklight, enabling users to view tweets/webpages on maps. This report documents the background, plans, schedule, design, implementation, software installation, and other related useful information. We used Solr and a triple-store to provide data, and the "getar-cs5604f17-final_shard1_replica1" collection was used in the final testing and delivery. An overview of the team work and detailed design and implementation are both provided. We highlight the visualization-based interface and the location-based interface, as they provide visual tools for people to better understand the data collected by all the teams. We seek to provide information on how we extract users' requirements, how user needs are reflected in light of the related literature, and how that leads to the design of the visualization and geo-interface. An installation manual is also detailed, seeking to help other software engineers who will keep working on GETAR to reuse our work.
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    Interactive Visualization for Novice Learners
    Chon, Jieun (Virginia Tech, 2019-07-09)
    Iteration, the repetition of computational steps, is a core concept in programming. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech's Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students' knowledge of iteration did not satisfy the course objectives in Spring 2018, because the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises for Fall 2018 and Spring 2019. For three semesters we conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). We found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted surveys in Fall 2018 and Spring 2019 from students to learn more about their recall, helpfulness, and reuse of the interactive visualizations. Finally, we analyzed data from the interactive exercises and page use to investigate students' usage behavior.