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Learning Analytics: More Than Data-Driven Decisions
 

Learning Analytics: More Than Data-Driven Decisions

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An overview of learning analytics as well as recent examples from higher education and current projects underway at the University of Michigan....

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."

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    Learning Analytics: More Than Data-Driven Decisions Learning Analytics: More Than Data-Driven Decisions Presentation Transcript

    • Learning Analytics:More Than Data-Driven Decisions Steven Lonn Research Fellow USE Lab, Digital Media Commons www.umich.edu/~uselab 1
    • Acknowledgements • USE Lab: • John Campbell – Stephanie D. Teasley • John Fritz – Andrew Krumm • Tim McKay – R. Joseph Waddington • David Wiley USE Lab University of Michigan 2http://umich.edu/~uselab
    • What is Analytics? + + USE Lab University of Michigan 3http://umich.edu/~uselab
    • Analytics in Our Lives USE Lab University of Michigan 4http://umich.edu/~uselab
    • Analytics in Our Lives USE Lab University of Michigan 5http://umich.edu/~uselab
    • Analytics in Our Work USE Lab University of Michigan 6http://umich.edu/~uselab
    • Analytics in Our Work USE Lab University of Michigan 6http://umich.edu/~uselab
    • Analytics in Our Work at a? hi sd ll t it ha w DO ne oeso hatd W USE Lab University of Michigan 6http://umich.edu/~uselab
    • Data Collected at . . What kind of data is already available those “in the know?” USE Lab University of Michigan 7http://umich.edu/~uselab
    • Data Collected at . . Admissions • High school GPA • SAT & ACT • Parental education • First generation college student? • Socio-economic status • Admission “rank” • AP tests & scores USE Lab University of Michigan 8http://umich.edu/~uselab
    • Data Collected at . . Demographics • Gender • Ethnicity • Age • Michigan residency • Country of origin & citizenship • Athlete? USE Lab University of Michigan 9http://umich.edu/~uselab
    • 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 University of Michigan 10http://umich.edu/~uselab
    • Data Collected at . . Other Places Data is Gathered... • CTools (courses, projects, etc.) • Library (Mirlyn, website, electronic journals) • Wolverine Access • Other UM tools (LectureTools, SiteMaker, UM.Lessons, MFile, Webmail, etc.) USE Lab University of Michigan 11http://umich.edu/~uselab
    • Current Use of Data... USE Lab University of Michigan 12http://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) USE Lab University of Michigan 13http://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) -- Goldstein & Katz, 2005 USE Lab University of Michigan 14http://umich.edu/~uselab
    • !"#$%&"#()#*+,""#-#.//#&(0&1&02,+#"$20)($"3 USE Lab University of Michiganhttp://umich.edu/~uselab
    • Signals • Purdue University • System developed in 2007 • Use of analytics for: – improving retention – identifying students “at risk” of academic failure USE Lab University of Michigan 16http://umich.edu/~uselab
    • Signals • NBC Nightly News Clip: http://www.msnbc.msn.com/id/21134540/vp/32634348 • Aired August 31, 2009 USE Lab University of Michigan 17http://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 USE Lab University of Michigan 19http://umich.edu/~uselab
    • “Check My Activity” Tool • University of Maryland, Baltimore County USE Lab University of Michigan 20http://umich.edu/~uselab
    • “Check My Activity” Tool • University of Maryland, Baltimore County USE Lab University of Michigan 20http://umich.edu/~uselab
    • “Check My Activity” Tool • University of Maryland, Baltimore County USE Lab University of Michigan 20http://umich.edu/~uselab
    • “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 University of Michigan 20http://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 USE Lab University of Michigan 21http://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? USE Lab University of Michigan 22http://umich.edu/~uselab
    • Projects• M-STEM Academy & USE Lab – 50 Engineering students per cohort ../0)123)/*45+%"6)788) %!!"!!#$ – Use CTools data to better inform -!"!!#$ mentor team ,!"!!#$ +!"!!#$ • When *!"!!#$ they need mentoring / do !"#$"%&(")!*+%&,) direction to resources? )!"!!#$ 4567/85$9:;35$ – How do mentors & students make (!"!!#$ !"!!#$ <=2>>$?@/32A/$ use of this data? &!"!!#$ – How does behavior change? %!"!!#$ !"!!#$ ./0$-$ ./0$%*$ 123$&$ 123$-$ 123$%*$ -&") USE Lab University of Michigan 22http://umich.edu/~uselab
    • Projects Social Network Analysis USE Lab University of Michigan 23http://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. USE Lab University of Michigan 24http://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 USE Lab University of Michigan 26http://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? USE Lab University of Michigan 26http://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. USE Lab University of Michigan !"#$#%&((%)%*+((,-./012#3- 27http://umich.edu/~uselab