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交叉学科论坛系列学术报告: Learning Analytics for Collaborative Learning on Social Media Platforms


发布日期:2017-04-17 浏览数:

时间:4月20日(周四)上午10:00
地点:信工楼E401会议室
报告人:
     Dr. Xiao Hu is an Assistant Professor in the Division of Information and Technology Studies in the Faculty of Education of the University of Hong Kong. Her research interests include learning analytics, applied data/text mining, and information retrieval. She is leading several projects on using learning analytics to improve teaching and learning, and has co-organized Learning Analytics Summer Institute, LASI-Hong Kong in 2013 and 2014. Dr. Hu has experience and background in multiple disciplines. Before joining HKU she was an Assistant Professor in the Morgridge College of Education at the University of Denver. Dr. Hu holds a PhD degree in Library and Information Science and a Master's degree in Computer Science from University of Illinois at Urbana-Champaign, a Master's degree in Electrical Engineering from Beijing University of Posts and Telecommunications, and a Bachelor's degree in Electronics and Information
Systems from Wuhan University. 

Abstract:

Learning analytics is an emerging and fast growing multidisciplinary field in which data about learners and their contexts are analyzed for understanding and optimizing learning and learning environments. This seminar will discuss two recent and ongoing studies in learning analytics and social media platforms. The first study is on a learning analytic tool for visualizing the statistics and timelines of collaborative Wikis built by secondary school students during their group projects in inquiry-based learning. Text categorization is used to detect thinking orders of student writings. The tool is now being evaluated in classrooms of two secondary schools in Hong Kong. The second study is a longitudinal analysis of student interactions on Blogs and Facebook in an experiential learning course across six years. Social network analysis was applied to investigate how student interactions are related to learning outcomes. Student postings were analyzed using association rule mining and text categorization. Implications of the findings on teaching and learning will be discussed.

南昌大学科技处
信息工程学院
2014.4.17

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