Browsing by Author "Du, Fan"

Now showing 1 - 3 of 3
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    CODAS: Integrating Business Analytics and Report Authoring
    (The Eurographics Association, 2022) Zhang, Zhuohao; Malik, Sana; Guo, Shunan; Hoffswell, Jane; Rossi, Ryan; Du, Fan; Koh, Eunyee; Bernard, Jürgen; Angelini, Marco
    Business analysts create rich dashboards to find data insights and subsequently communicate these findings with data-driven reports that combine visualization screenshots and descriptive text. Conventional analytics reports convey findings statically and passively, which suffers from limited interactivity and adaptability to data changes. There is therefore a need to facilitate authoring of interactive reports in business analytics. To better support the needs of business analysts, we developed CODAS: a report authoring tool that allows analysts to transform dashboards into interactive, web-based reports through a no-coding user interface and a workflow that is compatible to business analysts' existing data analytics pipelines. CODAS supports authoring multiple levels of interactions, organizing story elements, and generating the final artifact. Through our case studies with two expert analysts, we discuss the usefulness of our system and report our findings on analysts' report authoring workflow. Our findings suggest that CODAS enables business analysts to create interactive, data-driven reports comfortably, and can complement their exisitng data analytics workflow without extra learning effort.
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    MOOCad: Visual Analysis of Anomalous Learning Activities in Massive Open Online Courses
    (The Eurographics Association, 2019) Mu, Xing; Xu, Ke; Chen, Qing; Du, Fan; Wang, Yun; Qu, Huamin; Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta
    The research on Massive Open Online Course (MOOC) has mushroomed worldwide due to the technical revolution and its unprecedented enrollments. Existing work mainly focuses on performance prediction, content recommendation, and learning behavior summarization. However, finding anomalous learning activities in MOOC data has posed special challenges and requires providing a clear definition of anomalous behavior, analyzing the multifaceted learning sequence data, and interpreting anomalies at different scales. In this paper, we present a novel visual analytics system, MOOCad, for exploring anomalous learning patterns and their clustering in MOOC data. The system integrates an anomaly detection algorithm to cluster learning sequences of MOOC learners into staged-based groups. Moreover, it allows interactive anomaly detection between and within groups on the basis of semantic and interpretable group-wise data summaries. We demonstrate the effectiveness of MOOCad via an in-depth interview with a MOOC lecturer with real-world course data.
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    Understanding Business Analysts' Needs for Data Report Authoring
    (The Eurographics Association, 2022) Zhang, Zhuohao; Malik, Sana; Guo, Shunan; Hoffswell, Jane; Rossi, Ryan; Du, Fan; Koh, Eunyee; Bernard, Jürgen; Angelini, Marco
    Business analysts often create static, data-driven reports to summarize and communicate findings from marketing dashboards. However, the requirements and workflow for creating data-driven reports in business analytics have not been fully investigated. In this work, we interviewed fifteen professional analysts to understand their unique needs for data-driven report authoring and identify gaps between their goals, technical skills, and existing reporting tools. Our findings suggest eight fundamental takeaways for report authoring, such as the need for persistent interactive experiences combined with more robust narrative authoring for linking story pieces and customizing the narrative layout. Based on these interviews, we synthesize the results into five design guidelines to direct future analytic reporting tools.