Measures of Central Tendency: Mean, Median and Mode
Learning analytics in higher education: Promising practices and lessons learned
1. LEARNING ANALYTICS IN HIGHER EDUCATION
PROMISING PRACTICES AND LESSONS LEARNED
Bodong Chen,
University of Minnesota
October 27, 2016, Manila, Philippines
@bod0ng
2. BODONG CHEN
Assistant Professor in Learning Technologies
University of Minnesota-Twin Cities
Research interests: online learning, learning analytics, CSCL,
knowledge building
4. ... my main concern is the well being of the
plant materials ... And because of the diversity
of plants that we grow, we have to have a
wide range of niches to put those plants into.
Some need it to be a little cooler. Some want it
a little warmer. Some want to be drier. Some
want to be wetter. Our job here is to work with
Mother Nature and to try to provide the
conditions optimal for growth.
Source
9. LEARNING ANALYTICS IS
“The measurement, collection, analysis, and
reporting of data about learners and their
contexts”
WHAT
Long, P., & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause
Review, 46(5), 30–32.
10. LEARNING ANALYTICS IS
“The measurement, collection, analysis, and
reporting of data about learners and their
contexts
for understanding and optimising learning
and the environments in which learning
occurs”
WHY
(Long & Siemens, 2011)
11.
12. CAN ANY OF THESE PLAYERS AFFORD
NOT USING DATA?
14. Buckingham Shum, S. (2012). . UNESCO Institute for
Information Technologies in Education.
UNESCO Policy Brief: Learning Analytics
15. AGENDA
A study of Australian universities
University of Minnesota
My Classrooms
Cross-cutting factors
16. PART 1: A SNAPSHOT OF AUSSIE UNIVERSITIES
Colvin, C., Rogers, T., Wade, A., Dawson, S., Gasevic, D., Buckingham Shum, S., … Fisher, J. (2015).
. Australian Department of Education.
Student retention and learning analytics: A snapshot of Australian practices and a framework for
advancement
17. AN INTERVIEW STUDY
RESEARCH QUESTION
How senior institutional leaders perceived learning analytics
including the drivers, affordances and constraints?
PARTICIPANTS
Senior institutional leaders
(Deputy Vice Chancellors)
ANALYSES
Qualitative Coding + Cluster Analysis
23. UMN LEARNING ANALYTICS
DATA
Student Information System
Learning Management Systems
Student Advising Systems
A COMMON DATA LAYER
ANALYTICS
Dashboards
Predictive engines
25. BROADER DISCUSSIONS
Ethics: ethical use of data
Students: interacting with students
Quality: data quality
Leadership: the U's leadership structure
Research: esp. related to Unizin's deidenti ed data
Credit: UMN LA Team
35. 1. LEARNING ANALYTICS NOT NEUTRAL
Data are not neutral
Our analytics are our pedagogy
(Knight et al., 2014)
Interventionist by nature
educational visions and values
Replicate - Amplify - Transform
(Hughes, Thomas, & Scharber, 2006)