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Analytics:More Than Data-Driven Decisions

Steven LonnResearch Specialist

USE Lab, Digital Media Commonswww.umich.edu/~uselab

2

http://www.umich.edu/~uselab

http://www.umich.edu/~uselab

USE Labhttp://umich.edu/~uselab

Digital Media CommonsUniversity of Michigan

Acknowledgements

• USE Lab:– Stephanie D. Teasley– Andrew Krumm– R. Joseph Waddington

• John Campbell• John Fritz• Tim McKay• David Wiley

3

http://umich.edu/~uselab









What is Analytics?

4

+ +










Analytics in Our Lives

5










6

Analytics in Our Lives










Analytics in OAE!

7










Analytics in OAE!

7










Analytics in OAE!

7










Analytics in Our Work

8











8











8

What does one DO with all this d

ata?










Data Collected at . .

9

What kind of data is already available those

“in the know?”










10

Admissions











11

Demographics











• Cumulative GPA • Specific course grades• Major / minor• Number of Michigan credits• Number of transfer credits• Credits / grades in subsets (e.g., math courses)

12

Academic Record











13

Other Places Data is Gathered...











Current Use of Data...

14










What if...

15

!

• 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)










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)

16

-- Goldstein & Katz, 2005










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Signals

• Purdue University

• System developed in 2007

• Use of analytics for:

– improving retention

– identifying students “at risk” of academic failure

18










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Signals








http://www.msnbc.msn.com/id/3032619/vp/32634348%2332634348






“Check My Activity” Tool• University of Maryland, Baltimore County

20




20




20

• Student-controlled

• Designed to promote student agency & self-regulation

• Low impact for the instructor



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?

21










Our Project

22

• M-STEM Academy– 50 Engineering students

per cohort– Use Sakai data to better

inform mentor team










Our Project

22

• M-STEM Academy– 50 Engineering students

per cohort– Use Sakai data to better

inform mentor team

•When do students need mentoring / direction to resources?

•How do mentors & students make use of this data?

•How does behavior change?










23










24










comp

25










Graphing!

26










27

Advanced Graphing










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

28








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Education

Analytics Collaboration Session at Sakai 2011