32
Learning Analytics: More Than Data-Driven Decisions Steven Lonn Research Fellow USE Lab, Digital Media Commons www.umich.edu/~uselab 1

Learning Analytics: More Than Data-Driven Decisions

Embed Size (px)

DESCRIPTION

An overview of learning analytics as well as recent examples from higher education and current projects underway at the University of Michigan.From The Horizon Report, 2011:"Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses across the country, and leverages the vast amount of data produced by students in day-to-day academic activities. While learning analytics has already been used in admissions and fund-raising efforts on several campuses, “academic analytics” is just beginning to take shape."

Citation preview

Page 1: Learning Analytics: More Than Data-Driven Decisions

Learning Analytics:More Than Data-Driven Decisions

Steven LonnResearch Fellow

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

1

Page 2: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Acknowledgements

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

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

2

Page 3: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

What is Analytics?

3

+ +

Page 4: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Analytics in Our Lives

4

Page 5: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab5

Analytics in Our Lives

Page 6: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Analytics in Our Work

6

Page 7: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Analytics in Our Work

6

Page 8: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Analytics in Our Work

6

What does one DO with all this d

ata?

Page 9: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Data Collected at . .

7

What kind of data is already available those

“in the know?”

Page 10: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

• High school GPA• SAT & ACT• Parental education• First generation college student?• Socio-economic status• Admission “rank”• AP tests & scores

8

Admissions

Data Collected at . .

Page 11: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

• Gender• Ethnicity• Age• Michigan residency• Country of origin & citizenship• Athlete?

9

Demographics

Data Collected at . .

Page 12: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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

10

Academic Record

Data Collected at . .

Page 13: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

• CTools (courses, projects, etc.)• Library (Mirlyn, website, electronic journals)• Wolverine Access• Other UM tools (LectureTools, SiteMaker,

UM.Lessons, MFile, Webmail, etc.)

11

Other Places Data is Gathered...

Data Collected at . .

Page 14: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Current Use of Data...

12

Page 15: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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)

13

Page 16: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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)

14

-- Goldstein & Katz, 2005

Page 17: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

!"#$%&"#'()#*+,""#'-#.//#&(0&1&02,+#"$20)($"3

Page 18: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Signals

• Purdue University

• System developed in 2007

• Use of analytics for:

– improving retention

– identifying students “at risk” of academic failure

16

Page 19: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Signals

• NBC Nightly News Clip: http://www.msnbc.msn.com/id/21134540/vp/32634348

• Aired August 31, 2009

17

Page 20: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Signals• 6-10% improvement in retention• 58% of students using report seeking help b/c of

Signals use

• Controlled by the instructor• Course-by-course• Does not show students direct comparison with

their peers

19

Page 21: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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

20

Page 22: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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

20

Page 23: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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

20

Page 24: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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

20

• Student-controlled

• Designed to promote student agency & self-regulation

• Low impact for the instructor

Page 25: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Projects

• ITS UM-Data Warehouse– One place where all data can be aggregated and reported

out.– Currently includes:

• Student Dataset• eResearch

• Financial• Human Resources• Payroll

• Physical Resources

21

Page 26: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Projects

• M-STEM Academy & USE Lab– 50 Engineering students per cohort– Use CTools data to better inform

mentor team• When do they need mentoring /

direction to resources?

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

– How does behavior change?

22

Page 27: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Projects

• M-STEM Academy & USE Lab– 50 Engineering students per cohort– Use CTools data to better inform

mentor team• When do they need mentoring /

direction to resources?

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

– How does behavior change?

22

!"!!#$

%!"!!#$

&!"!!#$

'!"!!#$

(!"!!#$

)!"!!#$

*!"!!#$

+!"!!#$

,!"!!#$

-!"!!#$

%!!"!!#$

./0$-$ ./0$%*$ 123$&$ 123$-$ 123$%*$

!"#$"%

&'(")!*+%&,)

-'&")

../0)123)/*45+%"6)788)

4567/85$9:;35$

<=2>>$?@/32A/$

Page 28: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Projects

23

Social Network Analysis

Page 29: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Projects

• Tim McKay– Arthur F. Thurnau

Professor of Physics

• Taught into Physics courses for years

• Director: LS&A Honors Program

• Used LS&A ART tool to track student progress.

24

Page 30: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Projects

• Studied nearly 50,000 students over 12 years

• Can predict final grades within 0.5 grade dispersion

• Next project: use an e-coach programmed with analytics data to motivate ALL students

26

Page 31: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

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?

26

Page 32: Learning Analytics: More Than Data-Driven Decisions

USE LabUniversity of Michigan

http://umich.edu/~uselab

Further Reading• Campbell, J., Deblois, P., & Oblinger, D. (2007). Academic analytics: A new tool for a new era.

EDUCAUSE Review, 42(4), 40−57.

• Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers. The Internet and Higher Education, 14(2), 89-97. doi:10.1016/j.iheduc.2010.07.007

• Goldstein, P., & Katz, R. (2005). Academic analytics: The uses of management information and technology in higher education — Key findings (key findings) (pp. 1–12). Educause Center for Applied Research. http://www. educause.edu/ECAR/AcademicAnalyticsTheUsesofMana/156526

• Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588−599. doi:10.1016/j.compedu.2009.09.008.

• Morris, L. V., Finnegan, C., & Wu, S. (2005). Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education, 8(3), 221−231. doi:10.1016/j.iheduc.2005.06.009.

27!"#$#%&'((%)%*+'((,-./012#3-