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2. Predicting Learning Outcomes with MOOC Clickstreams

Author

Yu, Chen-Hsiang, Wu, Jungpin, and Liu, An-Chi

Abstract

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in education. Since 2014, the Ministry of Education in Taiwan has been promoting MOOC programs, with successful results. The ability of students to work at their own pace, however, is associated with low MOOC completion rates and has recently become a focus. The development of a mechanism to effectively improve course completion rates continues to be of great interest to both teachers and researchers. This study established a series of learning behaviors using the video clickstream records of students, through a MOOC platform, to identify seven types of cognitive participation models of learners. We subsequently built practical machine learning models by using K-nearest neighbor (KNN), support vector machines (SVM), and artificial neural network (ANN) algorithms to predict students' learning outcomes via their learning behaviors. The ANN machine learning method had the highest prediction accuracy. Based on the prediction results, we saw a correlation between video viewing behavior and learning outcomes. This could allow teachers to help students needing extra support successfully pass the course. To further improve our method, we classified the course videos based on their content. There were three video categories: theoretical, experimental, and analytic. Different prediction models were built for each of these three video types and their combinations. We performed the accuracy verification; our experimental results showed that we could use only theoretical and experimental video data, instead of all three types of data, to generate prediction models without significant differences in prediction accuracy. In addition to data reduction in model generation, this could help teachers evaluate the effectiveness of course videos.

Published
2019

34. Rewriting the Web with Chickenfoot

Author

Miller, Robert C., primary, Bolin, Michael, additional, Chilton, Lydia B., additional, Little, Greg, additional, Webber, Matthew, additional, and Yu, Chen-Hsiang, additional

Published
2010
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35. Contributors

Author

Adar, Eytan, primary, Ahmad, Moin, additional, Allen, James, additional, André, Paul, additional, Asakawa, Chieko, additional, Barton, John, additional, Bernstein, Michael, additional, Bigham, Jeffrey P., additional, Blaylock, Nate, additional, Blythe, Jim, additional, Bolin, Michael, additional, Borodin, Yevgen, additional, Brandt, Joel, additional, Chilton, Lydia B., additional, Chou, Victoria H., additional, Churchill, Elizabeth F., additional, Cohen, Michael F., additional, Collins, Kevin, additional, Cypher, Allen, additional, Beaumont, William de, additional, Dontcheva, Mira, additional, Drucker, Steven M., additional, Drews, Clemons, additional, Ennals, Rob, additional, Fogarty, James A., additional, Hartmann, Björn, additional, Faaborg, Alexander, additional, Ferguson, George, additional, Fujima, Jun, additional, Galescu, Lucien, additional, Gervasio, Melinda, additional, Guo, Philip J., additional, Haber, Eben, additional, Haines, Will, additional, Hornbæk, Kasper, additional, Hua, Zhigang, additional, Jones, M. Cameron, additional, Jung, Hyuckchul, additional, Kandogan, Eser, additional, Karger, David, additional, Kawanaka, Shinya, additional, Kobayashi, Masatomo, additional, Klemmer, Scott R., additional, Lau, Tessa, additional, Lerman, Kristina, additional, Leshed, Gilly, additional, Lewenstein, Joel, additional, Lieberman, Henry, additional, Lin, James, additional, Little, Greg, additional, Lunzer, Aran, additional, Matthews, Tara, additional, Miller, Robert C., additional, Moore, Brennan, additional, Nelson, Les, additional, Nichols, Jeffrey, additional, Rosson, Mary Beth, additional, Salesin, David, additional, Sato, Daisuke, additional, Scaffidi, Christopher, additional, schraefel, m.c., additional, Shaw, Mary, additional, Spaulding, Aaron, additional, Swift, Mary, additional, Takagi, Hironobu, additional, Tanaka, Yuzura, additional, Kleek, Max Van, additional, Webber, Matthew, additional, Weld, Daniel S., additional, Wilcox, Eric, additional, Wu, Leslie, additional, Yu, Chen-Hsiang, additional, and Zang, Nan, additional

Published
2010
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46. Rewriting the Web with Chickenfoot

Author

Miller, Robert C., Bolin, Michael T, Chilton, Lydia B, Little, Danny Greg, Webber, Matthew, Yu, Chen-Hsiang, Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science, and Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory

Abstract

Unlike desktop applications, Web applications are much more exposed and open to modification. This chapter describes Chickenfoot, a programming system embedded in the Firefox Web browser, which enables end users to automate, customize, and integrate Web applications without examining their source code. One way Chickenfoot addresses this goal is a technique for identifying page components by keyword pattern matching. Web automation includes navigating pages, filling in forms, and clicking on links. For example, many conferences now use a Web site to receive papers, distribute them to reviewers, and collect the reviews. A reviewer assigned 10 papers must download each paper, print it, and (later) upload a review for it. Tedious repetition is a good argument for automation. While integrating multiple Web sites, the simplest kind of integration is just adding links from one site to another, but much richer integration is possible. Techniques are developed through studying how users name Web page components and present a heuristic keyword-matching algorithm that identifies the desired component from the user's name. It describes a range of applications that have been created using Chickenfoot and reflects on its advantages and limitations. © 2010 Elsevier Inc. All rights reserved., National Science Foundation (U.S.) (award number IIS-0447800), Quanta Computer Incorporated (T-Party project)

Published
2019

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