Learning Analytics: Realizing the Big Data Promise in the CSU

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The word “analytics” has become a buzzword in current educational technology conversations, applied to everything from analysis of student work to LMS usage reporting to institutional analysis of ERP data. Broadly speaking, Learner Analytics refers to the analysis of student data using statistical techniques to improve decision-making. In the context of educational technology, Learner Analytics promises to improve our understanding of effective (and ineffective) student learning and technology usage. What progress have we seen in realizing this promise? This session offers a discussion of the promise of Learner Analytics, current research findings and tools, and explores examples from CSU Chico and the CSU Office of the Chancellor.

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  • Learning Analytics: Realizing the Big Data Promise in the CSU

    1. 1. Learner AnalyticsRealizing the “Big Data” Promise in the CSU John Whitmer, CSU Office of the Chancellor & CSU Chico Download slides at: http://bit.ly/HqaHBF
    2. 2. Outline1. Big Data & Analytics Promise(s)2. National Examples of Tools & Systems3. Learner Analytics @ Chico State4. Q&A
    3. 3. 1. BIG DATA & ANALYTICS PROMISE(S)
    4. 4. Steve Lohr, NY Times, August 5, 2009
    5. 5. Draft DOE Reportreleased April 12http://1.usa.gov/GDFpnI
    6. 6. Economist. (2010, 11/4/2010). Augmented business: Smart systems will disrupt lots of industries, and perhaps the entire economy. The Economist.
    7. 7. Current GPA: 3.3 First in family to attend college SAT Score: 877 Hasn’t taken college- level math No declared majorSource: jisc_infonet @ Flickr.com 7Source: jisc_infonet @ Flickr.com
    8. 8. What’s different with Big Data?4 V’s:1. Volume2. Variety3. Velocity4. Variability (IBM & Brian Hopkins, Forrester) 8
    9. 9. Academic Analytics“Academic Analytics marries large data sets with statistical techniques and predictive modeling to improve decision making” (Campbell and Oblinger 2007, p. 3)
    10. 10. Academic Analytics1. Term adopted in 2005 ELI research report (Goldstein & Katz, 2005) – Response to widespread adoption ERP systems, desire to use data collected for improved decision making – 380 respondents; 65% planned to increase capacity in near future2. Call to move from transactional/operational reporting to what-if analysis, predictive modeling, and alerts3. LMS identified as potential domain for future growth 10
    11. 11. DD Screenshot
    12. 12. Learner Analytics:“ ... measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” (Siemens, 2011)
    13. 13. or said plainly: What are students doing? Does it matter?
    14. 14. Learner Analytics1. Analyze combinations of data including: – Frequency of ed tech usage (e.g. clickstream analysis) – Student learning “outputs” (e.g. quiz scores, text answers) – Student background characteristics (e.g. race/ethnicity) – Academic achievement (e.g. grades, retention, graduation)2. Current rsch: mostly data mining, not hypothesis-driven3. More complex than Academic Analytics, considering: – Immaturity of ed tech reporting functionality – Translation of usage into meaningful activity – No significant difference: not what technology used, it’s how it’s used, who uses it, and for what purpose
    15. 15. A few promises of analytics for faculty and students …1. Provide behavioral data to investigate student performance2. Inform faculty about students succeeding or at risk of failing a course3. Warn students that they are likely to fail a course – before it’s too late4. Help faculty evaluate the effectiveness of practices and course designs5. Customize content and learning activities (e.g. adaptive learning materials)
    16. 16. What’s the promise of analytics for academic technologists?1. Decision-making based on actual practices (not just perceptions) and student outcomes2. Support movement of A.T. into strategic role re: teaching and learning by: – demonstrating the link between technology and learning – distinguishing our role from a technology infrastructure provider
    17. 17. Our 2 biggest barriers Image Source: http://bit.ly/Hq9Cdg
    18. 18. Image Source: Utopian Inc http://bit.ly/Hq9sCq
    19. 19. Image Source: Privacy in the Cloud: http://bit.ly/HrF6zk
    20. 20. 2. NATIONAL EXAMPLES OF TOOLS & SYSTEMS
    21. 21. SIGNALSPurdue Signals Project http://www.itap.purdue.edu/studio/signals/
    22. 22. SNAPPSNAPP (Social Networks Adapting Pedagogical Practice) http://www.snappvis.org/
    23. 23. KHANKhan Academy http://www.khanacademy.org/
    24. 24. OLICM Open Learning Initiative http://oli.web.cmu.edu/openlearning/initiative/process
    25. 25. PARCHMENTParchment http://www.parchment.com/c/my-chances/
    26. 26. 3. LEARNER ANALYTICS @ CHICOSTATE 26
    27. 27. LMS Learner Analytics @ Chico StateCampus-wide – How are faculty & students using the LMS? – What meaningful activities are being conducted? – How does that usage vary by student background, by college, by department?Course level – What is the relationship between LMS actions, student background characteristics and student academic achievement? (6 million dollar question) – Intro to Religious Studies: redesigned in Academy eLearning, increased enrollment from 80 to 327 students first semesterUltimate goal: provide faculty and administrators with what-ifmodeling tools to identify promising practices and early alerts 27
    28. 28. 28Chart from Scott Kodai, Chico State
    29. 29. CSU Practice
    30. 30. INTRODUCTION TO RELIGIOUSSTUDIES (RELS 180)
    31. 31. CLOSING THOUGHTS 34
    32. 32. Call to Action1. Metrics reporting is the foundation for Analytics2. Don’t need to wait for student performance data; good metrics can inspire access to performance data3. You’re *not* behind the curve, this is a rapidly emerging area that we can (should) lead ...4. If there’s any ed tech software folks in the audience, please help us with better reporting!
    33. 33. Want more? Resources on Analytics Googledoc: http://bit.ly/HrG6Dm
    34. 34. Q&A and Contact InfoResources Googledoc: http://bit.ly/HrG6DmContact Info:• John Whitmer (jwhitmer@csuchico.edu)• Hillary C Kaplowitz (hillary.kaplowitz@csun.edu)• Berggren, Kate E (kate.berggren@csun.edu) Download presentation at: http://bit.ly/HqaHBF 37

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