Introduction to the Center for Learning Analytics Research

Introduction to the Center for Learning Analytics Research

Welcome to the emerging field of learning analytics and the University of Phoenix’s newest research center, the Center for Learning Analytics Research

My name is Dr. Scott Burrus and student success is my passion. 

For over twenty years I have been involved with applied social and education policy research. As a scholar-practitioner by practice, my work over the years has focused on developing programs and evaluating their effectiveness. Several years ago I turned my attention to higher education as I saw an opportunity to extend my policy and program evaluation work toward an even greater focus on continuous improvement. Since then, my research has focused on teaching and learning, in particular within online environments. I believe in adult online education and have witnessed firsthand how this model facilitates individual, organizational, and community level change.

Notably, in 2014 my co-authors and I received the Effective Practice Award from the Online Learning Consortium thanks to a paper we wrote on pedagogy we developed, implemented, and tested at an online university.  Another interesting and impactful project was on the relationship between course length and academic achievement outcomes.  This study found that course length did not influence academic achievement, but did relate to improved time to completion, and helped to dispel myths that faculty may hold that shorter courses reflect lower academic quality.  These published findings have helped inform curricular change.  This year we published a paper on learning contracts in online doctoral education and found that students under learning contracts were more likely to complete their program and the contract increased motivation to graduate. 

My underlying focus is on continuous improvement using learning analytics and other data to help inform and set a research agenda and a strategic or practical agenda that informs pedagogy, faculty engagement and student success.  For institutions like the University of Phoenix that posses “big data,” learning analytics provides an untapped opportunity to not only understand how students learn and what comprises effective teaching (no small undertaking), but to also increase our ability to deliver personalized, practical, and applied instruction resulting in meaningful learning outcome achievement.  

Learning analytics also provide tools by which universities may identify why students struggle and which interventions result in success for particular students.  Learning analytics can be applied to every level of the university, and is relevant to marketing, admissions, academic advising, faculty, finance, leaders, and students. This means that members across the university should be using data to understand student success.  My long-term vision is to see all constituencies become student success scientists utilizing data to inform student success. 

This vision is ambitious and requires considerable collaboration across several university constituencies. Ultimately, the goal of learning analytics is to inform improvements in the learning process and to advance educational practice and scholarship. 

Sound exciting? Please join us! 

Comments

Scott Burrus's picture Scott Burrus | January 11, 2016 12:12 pm MST

Learning Analytics is an exciting new field and particularly relevant for EdD students focused on Curriculum and Instruction and Educational Technology!

John Hahn's picture John Hahn | March 11, 2016 11:15 pm MST

I work in Personalized Learning and Analytics for Pearson in Higher Education and want to thank you for your blog post.  We at Pearson are committed to improving peoples lives through learning.  I live by the axiom if you can measure it you can improve it so measuring learning is part of my everyday.  Please accept my late congratulations and hope to learn more in the days ahead.

Elizabeth Young's picture Elizabeth Young | August 5, 2016 8:58 am MST

Welcome, Scott.  You are setting the tone for a highly energetic group of researchers.  I was very impressed and encouraged by our teleconference.  Your support and advice for my team's research will be invaluable. 

Thank you for being you!!

Respectfully, Dr. Liz Young

Louis Daily's picture Louis Daily | November 21, 2016 1:09 pm MST

Scott,

I have talked with your briefly at a couple of meetings.  I am very interested in analytics.  I am trying to determine which to focus on:  R?  Python?  Rapidminer?  I know you have told me that you use SPSS.  As a faculty, I have access to SPSS, but I think most of the clustering techniques are in the SPSS modeler.  Do we have access to the Modeler?  Or should I just focus on what is available in the base SPSS?  Any direction you can give will be greatly appreciated.

 

thanks!

Lou Daily

 

Scott Burrus's picture Scott Burrus | November 21, 2016 1:59 pm MST

Hi Louis - I do have access to Modeler.  I'm not sure if the free license from UOP offers this or not. If it does, then I would use that.  

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Scott Burrus

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