Siwei Fu

VisMOOC: Visualizing video clickstream data from massive open online courses

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. With thousands of students watching course videos, enormous amounts of clickstream data are produced and recorded by the MOOCs platforms for each course. Such large-scale data provide a great opportunity for instructors and educational analysts to gain insight into online learning behaviors on an unprecedented scale. Nevertheless, the growing scale and unique characteristics of the data also pose a special challenge for effective data analysis. In this paper, we introduce VisMOOC, a visual analytic system to help analyze user learning behaviors by using video clickstream data from MOOC platforms. We work closely with the instructors of two Coursera courses to understand the data and collect task analysis requirements. A complete user-centered design process is further employed to design and develop VisMOOC. It includes three main linked views: the List View to show an overview of the clickstream differences among course videos, the Content-based View to show temporal variations in the total number of each type of click action along the video timeline, the Dashboard View to show various statistical information such as demographic information and temporal information. We conduct two case studies with the instructors to demonstrate the usefulness of VisMOOC and discuss new findings on learning behaviors.

Visual Analysis of MOOC Forums with iForum

Discussion forums of Massive Open Online Courses (MOOC) provide great opportunities for students to interact with instructional staff as well as other students. Exploration of MOOC forum data can offer valuable insights for these staff to enhance the course and prepare the next release. However, it is challenging due to the large, complicated, and heterogeneous nature of relevant datasets, which contain multiple dynamically interacting objects such as users, posts, and threads, each one including multiple attributes. In this paper, we present a design study for developing an interactive visual analytics system, called iForum, that allows for effectively discovering and understanding temporal patterns in MOOC forums. The design study was conducted with three domain experts in an iterative manner over one year, including a MOOC instructor and two official teaching assistants. iForum offers a set of novel visualization designs for presenting the three interleaving aspects of MOOC forums (i.e., posts, users, and threads) at three different scales. To demonstrate the effectiveness and usefulness of iForum, we describe a case study involving field experts, in which they use iForum to investigate real MOOC forum data for a course on JAVA programming.

ParkVis: A visual analytic system for anomaly detection in DinoFun World

We propose ParkVis, a visual analytic system for tracking the unusual patterns of all paying park visitors. Using both communication and movement data, park officers can use our system to identify and gauge the extent of unusual activity occurring in the park.

VisMOOC: Visualizing video clickstream data from Massive Open Online Courses

Massive Open Online Courses (MOOCs) are becoming increasingly popular and have attracted much research attention. Analyzing clickstreams on MOOC videos poses a special analytical challenge but provides a good opportunity for understanding how students interact with course videos, which in turn can help instructors and educational analysts gain insights into online learning behavior. In this poster, we develop a visual analytical system, VisMOOC, to help instructors analyze the clickstream data. VisMOOC consists of three main views: the List View to list all course videos for analysts to select the video they are interested in; the Content-based View to show how each type of click actions change along the video timeline, which enables the most viewed sections to be observed and the most interesting patterns to be discovered; The Dashboard View shows the information of the clickstream data in different aspects, including the course information, the geographic distribution, the video temporal information, the video popularity, and the animation. Furthermore, case studies made by the instructors demonstrate the usefulness of VisMOOC and helped them gaining deep insights into learning behavior for MOOCs.

VisForum: A Visual Analysis System for Exploring User Groups in Online Forums

User grouping in asynchronous online forums is a common phenomenon nowadays. People with similar backgrounds or shared interests like to get together in group discussions. As tens of thousands of archived conversational posts accumulate, challenges emerge for forum administrators and analysts to effectively explore user groups in large-volume threads and gain meaningful insights into the hierarchical discussions. Identifying and comparing groups in discussion threads are nontrivial, since the number of users and posts increases with time and noises may hamper the detection of user groups. Researchers in data mining fields have proposed a large body of algorithms to explore user grouping. However, the mining result is not intuitive to understand and difficult for users to explore the details. To address these issues, we present VisForum, a visual analytic system allowing people to interactively explore user groups in a forum. We work closely with two educators who have released courses in Massive Open Online Courses (MOOC) platforms to compile a list of design goals to guide our design. Then, we design and implement a multi-coordinated interface as well as several novel glyphs, i.e., group glyph, user glyph, and set glyph, with different granularities. Accordingly, we propose the group Detecting 8 Sorting Algorithm to reduce noises in a collection of posts, and employ the concept of “forum-index” for users to identify high-impact forum members. Two case studies using real-world datasets demonstrate the usefulness of the system and the effectiveness of novel glyph designs. Furthermore, we conduct an in-lab user study to present the usability of VisForum.

Towards Easy Comparison of Local Businesses Using Online Reviews

With the rapid development of e‐commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E‐Comp, a carefully‐designed visual analytics system based on online reviews, to help customers compare local businesses at different levels of details. More specifically, intuitive glyphs overlaid on maps are designed for quick candidate selection. Grouped Sankey diagram visualizing the rating difference by common customers is chosen for more reliable comparison of two businesses. Augmented word cloud showing adjective‐noun word pairs, combined with a temporal view, is proposed to facilitate in‐depth comparison of businesses in terms of different time periods, rating scores and features. The effectiveness and usability of E‐Comp are demonstrated through a case study and in‐depth user interviews.

ParkVis: Visual analytic system for communication data in DinoFun World

We propose ParkVis, a visual analytic system for tracking the communication trends of all paying park visitors. Using our various visualizations, we can identify anomalous communication patterns amongst the visitors, allowing us to aid park officers in identifying particular groups and gauging the spectrum of communication activity occurring in the park.

ParkVis: Visual analytic system for population movement in DinoFun World

We propose ParkVis, a visual analytic system for monitoring movement tracking information for all of the paying park visitors. With various visualizations, our system can identify different kinds of traveler behaviors in the park, thus help park officers to segment the population and discover notable differences in the patterns of activity in the park.