The Experts below are selected from a list of 1710 Experts worldwide ranked by ideXlab platform

Mark Warschauer - One of the best experts on this subject based on the ideXlab platform.

  • The benefits and caveats of using Clickstream Data to understand student self-regulatory behaviors: opening the black box of learning processes
    International Journal of Educational Technology in Higher Education, 2020
    Co-Authors: Rachel Baker, Jihyun Park, Fernando Rodriguez, Mark Warschauer, Bianca Cung, Christian Fischer, Padhraic Smyth
    Abstract:

    Student Clickstream Data—time-stamped records of click events in online courses—can provide fine-grained information about student learning. Such Data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these Data often require advanced analytic techniques, as they only provide a partial and noisy record of students’ actions. Consequently, these Data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of Clickstream Data to define and identify behavioral patterns that are related to student learning outcomes. Through discussions of four studies, we provide examples of the complexities and particular considerations of using these Data to examine student self-regulated learning.

  • Using Clickstream Data to measure, understand, and support self-regulated learning in online courses
    The Internet and Higher Education, 2020
    Co-Authors: Rachel Baker, Mark Warschauer
    Abstract:

    Abstract The ability to regulate one's own learning is essential for success in online courses. Recent efforts have used Clickstream Data to create timely, fine-grained, and comprehensive measures of self-regulated learning (SRL) in online courses in an attempt to shed light on the process of SRL and to improve the identification of students who lack SRL skills and are at risk of low achievement. However, key questions remain: to what extent do these Clickstream measures correspond to traditional self-reported measures about specific SRL constructs? Do these Clickstream measures provide more information than existing self-reported measures in predicting course performance? This study used the Clickstream Data collected from a learning management system to measure two aspects of SRL: time management and effort regulation. We found that the Clickstream measures were significantly associated with students' self-reported time management and effort regulation after the course. In addition, these Clickstream measures significantly improved predictions of students' performance in the current and subsequent courses over predictions based on self-reported measures alone. These results provide evidence for the validity of the Clickstream measures and guide the use of Clickstream Data to understand the process of SRL and identify students who might not be well served by taking classes online.

  • detecting changes in student behavior from Clickstream Data
    International Learning Analytics & Knowledge Conference, 2017
    Co-Authors: Jihyun Park, Kameryn Denaro, Fernando Rodriguez, Padhraic Smyth, Mark Warschauer
    Abstract:

    Student Clickstream Data can provide valuable insights about student activities in an online learning environment and how these activities inform their learning outcomes. However, given the noisy and complex nature of this Data, an on-going challenge involves devising statistical techniques that capture clear and meaningful aspects of students' click patterns. In this paper, we utilize statistical change detection techniques to investigate students' online behaviors. Using Clickstream Data from two large university courses, one face-to-face and one online, we illustrate how this methodology can be used to detect when students change their previewing and reviewing behavior, and how these changes can be related to other aspects of students' activity and performance.

  • LAK - Detecting changes in student behavior from Clickstream Data
    Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017
    Co-Authors: Jihyun Park, Kameryn Denaro, Fernando Rodriguez, Padhraic Smyth, Mark Warschauer
    Abstract:

    Student Clickstream Data can provide valuable insights about student activities in an online learning environment and how these activities inform their learning outcomes. However, given the noisy and complex nature of this Data, an on-going challenge involves devising statistical techniques that capture clear and meaningful aspects of students' click patterns. In this paper, we utilize statistical change detection techniques to investigate students' online behaviors. Using Clickstream Data from two large university courses, one face-to-face and one online, we illustrate how this methodology can be used to detect when students change their previewing and reviewing behavior, and how these changes can be related to other aspects of students' activity and performance.

Qiong Luo - One of the best experts on this subject based on the ideXlab platform.

  • animated narrative visualization for video Clickstream Data
    International Conference on Computer Graphics and Interactive Techniques, 2016
    Co-Authors: Yun Wang, Zhutian Chen, Qiong Luo
    Abstract:

    Video Clickstream Data are important for understanding user behaviors and improving online video services. Various visual analytics techniques have been proposed to explore patterns in these Data. However, those techniques are mainly developed for analysis and do not sufficiently support presentations. It is still difficult for Data analysts to convey their findings to an audience without prior knowledge. In this paper, we propose to use animated narrative visualization to present video Clickstream Data. Compared with traditional methods which directly turn click events into animations, our animated narrative visualization focuses on conveying the patterns in the Data to a general audience and adopts two novel designs, non-linear time mapping and foreshadowing, to make the presentation more engaging and interesting. Our non-linear time mapping method keeps the interesting parts as the focus of the animation while compressing the uninteresting parts as the context. The foreshadowing techniques can engage the audience and alert them to the events in the animation. Our user study indicates the effectiveness of our system and provides guidelines for the design of similar systems.

  • SIGGRAPH Asia Symposium on Visualization - Animated narrative visualization for video Clickstream Data
    SIGGRAPH ASIA 2016 Symposium on Visualization, 2016
    Co-Authors: Yun Wang, Zhutian Chen, Qiong Luo
    Abstract:

    Video Clickstream Data are important for understanding user behaviors and improving online video services. Various visual analytics techniques have been proposed to explore patterns in these Data. However, those techniques are mainly developed for analysis and do not sufficiently support presentations. It is still difficult for Data analysts to convey their findings to an audience without prior knowledge. In this paper, we propose to use animated narrative visualization to present video Clickstream Data. Compared with traditional methods which directly turn click events into animations, our animated narrative visualization focuses on conveying the patterns in the Data to a general audience and adopts two novel designs, non-linear time mapping and foreshadowing, to make the presentation more engaging and interesting. Our non-linear time mapping method keeps the interesting parts as the focus of the animation while compressing the uninteresting parts as the context. The foreshadowing techniques can engage the audience and alert them to the events in the animation. Our user study indicates the effectiveness of our system and provides guidelines for the design of similar systems.

Qing Chen - One of the best experts on this subject based on the ideXlab platform.

  • VisMOOC: Visualizing video Clickstream Data from Massive Open Online Courses
    2015 IEEE Pacific Visualization Symposium (PacificVis), 2015
    Co-Authors: Siwei Fu, Qing Chen, Huamin Qu
    Abstract:

    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.

  • PacificVis - VisMOOC: Visualizing video Clickstream Data from Massive Open Online Courses
    2015 IEEE Pacific Visualization Symposium (PacificVis), 2015
    Co-Authors: Conglei Shi, Qing Chen
    Abstract:

    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.

  • vismooc visualizing video Clickstream Data from massive open online courses
    Visual Analytics Science and Technology, 2014
    Co-Authors: Conglei Shi, Qing Chen
    Abstract:

    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.

  • IEEE VAST - VisMOOC: Visualizing video Clickstream Data from massive open online courses
    2014 IEEE Conference on Visual Analytics Science and Technology (VAST), 2014
    Co-Authors: Conglei Shi, Qing Chen
    Abstract:

    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.

Yun Wang - One of the best experts on this subject based on the ideXlab platform.

  • animated narrative visualization for video Clickstream Data
    International Conference on Computer Graphics and Interactive Techniques, 2016
    Co-Authors: Yun Wang, Zhutian Chen, Qiong Luo
    Abstract:

    Video Clickstream Data are important for understanding user behaviors and improving online video services. Various visual analytics techniques have been proposed to explore patterns in these Data. However, those techniques are mainly developed for analysis and do not sufficiently support presentations. It is still difficult for Data analysts to convey their findings to an audience without prior knowledge. In this paper, we propose to use animated narrative visualization to present video Clickstream Data. Compared with traditional methods which directly turn click events into animations, our animated narrative visualization focuses on conveying the patterns in the Data to a general audience and adopts two novel designs, non-linear time mapping and foreshadowing, to make the presentation more engaging and interesting. Our non-linear time mapping method keeps the interesting parts as the focus of the animation while compressing the uninteresting parts as the context. The foreshadowing techniques can engage the audience and alert them to the events in the animation. Our user study indicates the effectiveness of our system and provides guidelines for the design of similar systems.

  • SIGGRAPH Asia Symposium on Visualization - Animated narrative visualization for video Clickstream Data
    SIGGRAPH ASIA 2016 Symposium on Visualization, 2016
    Co-Authors: Yun Wang, Zhutian Chen, Qiong Luo
    Abstract:

    Video Clickstream Data are important for understanding user behaviors and improving online video services. Various visual analytics techniques have been proposed to explore patterns in these Data. However, those techniques are mainly developed for analysis and do not sufficiently support presentations. It is still difficult for Data analysts to convey their findings to an audience without prior knowledge. In this paper, we propose to use animated narrative visualization to present video Clickstream Data. Compared with traditional methods which directly turn click events into animations, our animated narrative visualization focuses on conveying the patterns in the Data to a general audience and adopts two novel designs, non-linear time mapping and foreshadowing, to make the presentation more engaging and interesting. Our non-linear time mapping method keeps the interesting parts as the focus of the animation while compressing the uninteresting parts as the context. The foreshadowing techniques can engage the audience and alert them to the events in the animation. Our user study indicates the effectiveness of our system and provides guidelines for the design of similar systems.

Catarina Sismeiro - One of the best experts on this subject based on the ideXlab platform.

  • click here for internet insight advances in Clickstream Data analysis in marketing
    Journal of Interactive Marketing, 2009
    Co-Authors: Randolph E Bucklin, Catarina Sismeiro
    Abstract:

    Abstract Clickstream Data are defined as the electronic record of Internet usage collected by Web servers or third-party services. The authors discuss the nature of Clickstream Data, noting key strengths and limitations of these Data for research in marketing. The paper reviews major developments from the analysis of these Data, covering advances in understanding (1) browsing and site usage behavior on the Internet, (2) the Internet's role and efficacy as a new medium for advertising and persuasion, and (3) shopping behavior on the Internet (i.e., electronic commerce). The authors outline opportunities for new research and highlight several emerging areas likely to grow in future importance. Inherent limitations of Clickstream Data for understanding and predicting the behavior of Internet users or researching marketing phenomena are also discussed.

  • A Model of Web Site Browsing Behavior Estimated on Clickstream Data
    Journal of Marketing Research, 2003
    Co-Authors: Randolph E Bucklin, Catarina Sismeiro
    Abstract:

    Abstract Using the Clickstream Data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor’s decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file Data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors’ propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with “within-site lock-in” or site “stickiness” and with learning that carries over repeat visits. In particular, repeat vis...