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Merceron, Agathe; Blikstein, Paulo; Siemens, George – Journal of Learning Analytics, 2015
This article introduces the special issue from the 2015 Learning Analytics and Knowledge conference. We describe the current state of the field and identify some of the trends in recent research. As the field continues to expand, there seem to be at least three directions of vigorous growth: (1) the inclusion of multimodal data (gesture,…
Descriptors: Educational Research, Data, Data Collection, Data Analysis
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Schneider, Bertrand; Blikstein, Paulo – Journal of Educational Data Mining, 2015
In this paper, we describe multimodal learning analytics (MMLA) techniques to analyze data collected around an interactive learning environment. In a previous study (Schneider & Blikstein, submitted), we designed and evaluated a Tangible User Interface (TUI) where dyads of students were asked to learn about the human hearing system by…
Descriptors: Educational Research, Data Collection, Data Analysis, Educational Environment
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Blikstein, Paulo; Worsley, Marcelo – Journal of Learning Analytics, 2016
New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics…
Descriptors: Data Analysis, Data Collection, Educational Research, Constructivism (Learning)
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Worsley, Marcelo; Blikstein, Paulo – Journal of Learning Analytics, 2014
Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some…
Descriptors: Data Analysis, Data Collection, Engineering, Design
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Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne – Journal of the Learning Sciences, 2014
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…
Descriptors: Programming, Computer Science Education, Learning Processes, Introductory Courses