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April 12, 2012 / dlitgroup

Learning Analytics: The LMS Perspective

With respect to learning analytics, the community is still working its way through some fundamental questions. Perhaps the most fundamental of all is how best to define or describe learning analytics, and what sets it apart from other varieties of analytics. Other key questions include: which data are the most important for learning analytics? What kinds of analysis of that data is most relevant and revealing for the student success that learning analytics is designed to promote? And what will learning analytics look like 2 or 3 years from now? This session will be an interview-style conversation that explores these key questions (as well as others) with panel of three experts, one a consultant and the other two from leading LMS vendors. Participants will 1) understand what is unique about the role and function of learning analytics; 2) have more complete understanding of the data and analysis that is characteristic of learning analytics; and 3) know what the likely major developments will be in this field.

James Chalex
Dir. Product Management
Blackboard Inc.
Director, Innovations and Analytics
Desire2Learn Incorporated
President
Strategic Initiatives, Inc

ELI Panel on Learning Analytics and the LMS

Notes:
  • “Analytics” – a methodical collection of data and the analysis of data for the purpose of improving efficiency.  “Academic Analytics” – How data can make the institution more efficient.   “Learning Analytics” – Focusing on the individual learner and that learners context and those that are supporting that learner and the information around the context of learning to improve the students’ learning outcomes.  Behavior patterns and use of the course management system.
  • Lenses, telescopes, and microscopes allow us to see more clearly and better.
  • Empherical science – looking at learning as a science.
  • Machine intelligence – heuristic to data mining.
  • Statistical reasoning – the logic of uncertainty.
  • Learning analytics provide tools and data to the people out in the field vs academic analytics being more for administration.
  • What constitutes student success?  Student retention, completion, and graduation.  Institutional level and course level and class level outcomes.
  • The presentation of the data in analytics is key.  The information needs to be readily available and within the context they expect it.  It needs to be readable and actionable and interactive and visual as possible.  Visualization is key!
Data is also more valuable when compared with peers to change one’s awareness, motivation, and eventually behavior. (John Fritz, UMBC)
  • Less data more insights… is key.  We need perhaps less data but more meaningful data.
  • The future?  Benchmarking across institutions within institutions…  Narrative visualizations that generate and consolidate data that is action oriented.  Authentic assessment and content evaluation and metrics.
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