Analytics Collaboration Session at Sakai 2011

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Analytics:More Than Data-Driven Decisions Steven Lonn Research Specialist USE Lab, Digital Media Commons www.umich.edu/~uselab 2

Acknowledgements • USE Lab: • John Campbell – Stephanie D. Teasley • John Fritz – Andrew Krumm • Tim McKay – R. Joseph Waddington • David Wiley USE Lab Digital Media Commons 3http://umich.edu/~uselab University of Michigan

What is Analytics? + + USE Lab Digital Media Commons 4http://umich.edu/~uselab University of Michigan

Analytics in Our Lives USE Lab Digital Media Commons 5http://umich.edu/~uselab University of Michigan

Analytics in Our Lives USE Lab Digital Media Commons 6http://umich.edu/~uselab University of Michigan

Analytics in OAE! USE Lab Digital Media Commons 7http://umich.edu/~uselab University of Michigan

Analytics in Our Work USE Lab Digital Media Commons 8http://umich.edu/~uselab University of Michigan

Analytics in Our Work at a? hi sd ll t it ha w DO ne oeso hatd W USE Lab Digital Media Commons 8http://umich.edu/~uselab University of Michigan

Data Collected at . . What kind of data is already available those “in the know?” USE Lab Digital Media Commons 9http://umich.edu/~uselab University of Michigan

Data Collected at . . Admissions USE Lab Digital Media Commons 10http://umich.edu/~uselab University of Michigan

Data Collected at . . Demographics USE Lab Digital Media Commons 11http://umich.edu/~uselab University of Michigan

Data Collected at . . Academic Record • Cumulative GPA • Speciﬁc course grades • Major / minor • Number of Michigan credits • Number of transfer credits • Credits / grades in subsets (e.g., math courses) USE Lab Digital Media Commons 12http://umich.edu/~uselab University of Michigan

Data Collected at . . Other Places Data is Gathered... USE Lab Digital Media Commons 13http://umich.edu/~uselab University of Michigan

Current Use of Data... USE Lab Digital Media Commons 14http://umich.edu/~uselab University of Michigan

What if...• Identify: – Who needs the most help – Most successful sequence of courses – Most / least successful portions of a course• Notify: – Instructors about their students – Students about their performance compared to peers – – Academic advisors about students “at risk” Staff about their resources (e.g., library use) ! USE Lab Digital Media Commons 15http://umich.edu/~uselab University of Michigan

Milestones• Stage 1: Extraction & reporting of transaction-level data• Stage 2: Analysis and monitoring of operational performance• Stage 3: What-if decision support (e.g., scenario building)• Stage 4: Predictive modeling & simulation• Stage 5: Automatic triggers of business processes (e.g., alerts) -- Goldstein & Katz, 2005 USE Lab Digital Media Commons 16http://umich.edu/~uselab University of Michigan

"#$%&#$()*$+,-##$(.$/00$)1213-,$#%31*)%#4 USE Lab Digital Media Commonshttp://umich.edu/~uselab University of Michigan

Signals • Purdue University • System developed in 2007 • Use of analytics for: – improving retention – identifying students “at risk” of academic failure USE Lab Digital Media Commons 18http://umich.edu/~uselab University of Michigan

Signals 567$58&%,9$5*:# ;383#%$<=>$?00@ !"#$%%&&&()*+,()*,-(%./%0102345%6#% 02307078902307078 USE Lab Digital Media Commons 19http://umich.edu/~uselab University of Michigan

“Check My Activity” Tool • University of Maryland, Baltimore County USE Lab Digital Media Commons 20http://umich.edu/~uselab University of Michigan

“Check My Activity” Tool • University of Maryland, Baltimore County • Student-controlled • Designed to promote student agency & self-regulation • Low impact for the instructor USE Lab Digital Media Commons 20http://umich.edu/~uselab University of Michigan

Issues to Ponder • Who is the audience? – Students, Instructors, Advisors, Deans, Staff, Others? • Who has the control? – Issues of burden? • Which views? • Privacy concerns? – Is their an institutional obligation? • Is Learning Analytics just a fad? • Others? USE Lab Digital Media Commons 21http://umich.edu/~uselab University of Michigan

Our Project • M-STEM Academy – 50 Engineering students per cohort – Use Sakai data to better inform mentor team USE Lab Digital Media Commons 22http://umich.edu/~uselab University of Michigan

Our Project • M-STEM Academy – 50 Engineering students • When do students need mentoring / direction per cohort toUse Sakai data to better – resources? • How domentor team& students make use of this inform mentors data? • How does behavior change? USE Lab Digital Media Commons 22http://umich.edu/~uselab University of Michigan

USE Lab Digital Media Commons 23http://umich.edu/~uselab University of Michigan

USE Lab Digital Media Commons 24http://umich.edu/~uselab University of Michigan

comp USE Lab Digital Media Commons 25http://umich.edu/~uselab University of Michigan

Graphing! USE Lab Digital Media Commons 26http://umich.edu/~uselab University of Michigan

Advanced Graphing USE Lab Digital Media Commons 27http://umich.edu/~uselab University of Michigan

Project Next Steps• What Sakai events are “meaningful” for predicting student success?• Presenting data displays to advisors and students in-term. – Is a behavioral change noted? To what effect? What kinds of outcomes are noted?• Can this approach scale? – Beginning with engineering college USE Lab Digital Media Commons 28http://umich.edu/~uselab University of Michigan

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Analytics Collaboration Session at Sakai 2011

by Steve Lonn

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Analytics Collaboration Session at Sakai 2011 Presentation Transcript

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