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Learning Analytics: Realizing their Promise in the California State University

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Learning Analytics: Realizing their Promise in the California State University

  1. 1. Learner Analytics Realizing their Promise in the CSUJohn Whitmer, CSU Office of the Chancellor & CSU Chico Kate Berggren, CSU Northridge Hillary Kaplowitz, CSU Northridge Tom Norman, CSU DH Download slides at: http://bit.ly/HqaHBF
  2. 2. Outline1. Promise of Learner Analytics2. Tools & Systems in Practice3. CSU Case Studies: • Analytics at Work in the Classroom (Hillary) • GISMO & SQL Query Tools (Kate) • Vista in RELS 180 (John)4. Q & A
  3. 3. 1. PROMISE OF LEARNER ANALYTICS
  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. Source: 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. TOOLS & SYSTEMS IN PRACTICE
  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. CSU CASE STUDIES
  27. 27. ANALYTICS AT WORK IN THECLASSROOM (HILLARY) 27
  28. 28. How can data help teachers and students?Two stories about how data helped students and teachers work better together 28
  29. 29. 29
  30. 30. 30
  31. 31. “Hey Professor,I just looked at my assignments andrealized that my Chapter 11 summary didnot get submitted, which Im havingtrouble believing that I didnt submit it...especially because I see that I did it, and Ialways submit my assignments as soon as Ifinish them.” 31
  32. 32. Now the hard part…. Do I believe him? If I only I could check… 32
  33. 33. 33
  34. 34. 34
  35. 35. And it was all his idea…The student suggested that I check Moodle andif that didn’t work told me how to check theRevision History in GoogleDocs with step-by-step directions! 35
  36. 36. 36
  37. 37. Hybrid Course Weekly Structure 4. Post 3. Online questions1. Watch 2. Read 4. Class 5. Aplia chat and and takelectures textbook meets quiz tutoring practice quiz 37
  38. 38. “The quiz is unfair” 38
  39. 39. But the story was not that simple…» Reports on Moodle painted a different picture» Student was watching the lectures at 10:00 p.m.» Then immediately taking quiz 39
  40. 40. Enabled constructive feedback…1. Advised the student how the structure of the course was designed to enhance learning2. Student revised their study habits3. Improved grades and thanked the instructor! 40
  41. 41. What we can do with data now1. Use Reports in Moodle to verify student claims2. Review participant list to see last access time3. Empower students to review their own reports4. Analyze usage and advise students how to study better5. Review quiz results to find common misconceptions 41
  42. 42. And if we had better tools that are easier to use…1. Let our students see more details about how their habits affect their grades and encourage them to use them2. Give instructors access to more information and better tools to organize data so they can see patterns of access and time on task and how they relate to outcomes3. Have tools that red flag students with teacher set criteria4. Help streamline workflow for instructors by organizing student information – View all ungraded assignments 42
  43. 43. Could we help improve studentlearning outcomes if we knew the effect of… Coffee Friends Time Attendance Amount Mobile Textbook LMS LMS Access Activities 43
  44. 44. GISMO & SQL QUERY TOOL (KATE) 44
  45. 45. GISMO – Course Block 45
  46. 46. GISMO – Access Overview 46
  47. 47. GISMO – Access by Student 47
  48. 48. GISMO – Quiz Overview 48
  49. 49. SQL Query Tool 49
  50. 50. List of Contributed Queries 50
  51. 51. Query Example 51
  52. 52. Query Results:Most Active Courses 52
  53. 53. Query: Most Popular Activities 53
  54. 54. Query: Systemwide use ofActivities and Resources 54
  55. 55. Query:Forum Use Count by Type 55
  56. 56. Learner Analytics Thomas J. NormanCalifornia State University Dominguez Hills
  57. 57. eBook
  58. 58. A New Way of Reading
  59. 59. From Textbooks to Apps
  60. 60. Assignments
  61. 61. Grading To Do List
  62. 62. Real Time Metrics
  63. 63. Warnings
  64. 64. LearnSmart Progress
  65. 65. Analysis by AACSB Categories
  66. 66. Bloom’s Taxonomy
  67. 67. Performance by Learning Objective/Difficulty
  68. 68. Ideas? Questions?• tnorman@csudh.edu• tom@professornorman.com• 310-243-2146
  69. 69. CSU CHICO VISTA ANALYTICS 72
  70. 70. 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 73
  71. 71. 74Chart from Scott Kodai, Chico State
  72. 72. CSU Practice
  73. 73. INTRODUCTION TO RELIGIOUSSTUDIES (RELS 180)
  74. 74. CLOSING THOUGHTS 80
  75. 75. 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!
  76. 76. Want more? Resources on Analytics Googledoc: http://bit.ly/HrG6Dm
  77. 77. 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 83

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