Elusive Learning—Using Learning Analytics to Support Reflective Sensemaking of Ill-Structured Ethical Problems: A Learner-Managed Dashboard Solution
by
Yianna Vovides 1,* and Sarah Inman 2
Yianna Vovides 1,* and Sarah Inman 2
1
Center for New Designs in Learning and Scholarship, Georgetown University, Washington, DC 20007, USA
2
Learning Technology, Stevens Institute of Technology, Hoboken, NJ 07030, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Maged N. Kamel Boulos
Future Internet 2016, 8(2), 26; https://doi.org/10.3390/fi8020026
Received: 17 December 2015 / Revised: 12 May 2016 / Accepted: 12 May 2016 / Published: 11 June 2016
(This article belongs to the Special Issue eLearning)
Since the turn of the 21st century, we have seen a surge of studies on the state of U.S. education addressing issues such as cost, graduation rates, retention, achievement, engagement, and curricular outcomes. There is an expectation that graduates should be able to enter the workplace equipped to take on complex and “messy” or ill-structured problems as part of their professional and everyday life. In the context of online learning, we have identified two key issues that are elusive (hard to capture and make visible): learning with ill-structured problems and the interaction of social and individual learning. We believe that the intersection between learning and analytics has the potential, in the long-term, to minimize the elusiveness of deep learning. A proposed analytics model is described in this article that is meant to capture and also support further development of a learner’s reflective sensemaking.
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MDPI and ACS Style
Vovides, Y.; Inman, S. Elusive Learning—Using Learning Analytics to Support Reflective Sensemaking of Ill-Structured Ethical Problems: A Learner-Managed Dashboard Solution. Future Internet 2016, 8, 26.
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