Learning Analytics: Distorting, Enhancing, or Wrecking Education?
Primary Presenter: Janet Corral
Co-Presenter: Helen Macfarlane
Organization: University of Colorado Denver Anschutz
Role: Faculty
Track: Research Presentation
Level: Cutting Edge
Abstract: Learning analytics is an emerging field, where complex data sets are analyzed into various graphical outputs to assess learning. The ease of using learning analytics software betrays the superficiality of these measures, which is a concern as such approaches are applied to predicting student performance. Through exploring examples in practice and software, the promises and pitfalls will be explored towards charting a course ahead for ethical and responsible use of learning analytics in education.
Bio: Dr. Corral is a faculty member in the Academy of Medical Educators in the School of Medicine at the UC Anschutz campus. Her research focuses on designing and evaluating learning in complex environments. She has presented nationally and internationally at conferences and published in lead academic journals in education and medicine. Her most recent book chapter, "Large Group Teaching", was an invited chapter for the Oxford Textbook of Medical Education. She is president-elect of the western regional group for Computing Resources in Medical Education of the American Association of Medical Colleges.
Description: Presentation Objectives: 1. Introduce and define Learning Analytics 2. Demonstrate the range of analytics examples from medical schools, economics, genetics, business, and online education. 3. Critically appraise the role and contribution of data analytics to and in post-secondary education. Presentation Outline: Learning analytics holds great potential to simplify the understanding of complex data sets and their relationships to student learning. Learning analytics will be defined and contrasted with similar movements of academic analytics and customer retention management. A review of examples of analytics in education and research, as well as from the broader fields of economics, genetics, and business, will help provide practical case studies of what works, what scares us, and areas of future opportunity. The presenters will critically appraise the many tools (free and for cost) are available, relating such tools to where they both support and misalign the power of analytics for understanding students' learning. Careful definitions of metrics, and critical appraisal of predictive algorithms for reliability and validity, are necessary. The authors provide a path forward for the use of analytics in education and research. Expected outcomes: Attendees will be able to: 1. Define learning analytics; contrast and compare with academic analytics, customer retention management. 2. Identify and apply learning analytics software appropriate to needs 3. Assess a framework for moving forward with learning analytics.