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Curriculum analytics: Using data from student learning analytics

by Paul Bailey

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Curriculum analytics: Using data from student learning analytics

  1. 1. Curriculum analytics: Using data from student learning analytics December 2018 Paul Bailey, Research and Development, Jisc Niall Sclater, Consultant
  2. 2. Jisc Curriculum Analytics2
  3. 3. What is curriculum analytics? “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” SoLAR – Society for Learning Analytics Research
  4. 4. Towards Education 4.0 (Davies Model revisited) Jisc Futures4
  5. 5. User stories Jisc Curriculum Analytics5 Outputs from workshop Nov 2018 •63 user stories •Institutional goals •Improving benchmarks (e.g. NSS, TEF) •Quality assurance •Resource usage •Course/module review •Data-informed course and module design •Course reporting dashboard •Improving assessment •Online learning optimisation •Feedback driven course design
  6. 6. Jisc Curriculum Analytics6
  7. 7. What data do we get from learning analytics? What additional data do we need?
  8. 8. Building on the LA Architecture Jisc Curriculum Analytics8 Data Collection Data Storage and Analysis Presentation and Action Learning Data Hub Student Records Engagement Data Staff dashboards in Data Explorer/Curriculum design tools Course and Module Information Lecture Activity Learning Outcomes Student feedback Data Processing Tools Data Estate Institutional BI Tools
  9. 9. Jisc Curriculum Analytics9 Course Course Instance Module Module Instance Sessions i.e. Lectures, labs, etc.Assessment Assessment Instance Learning outcomes Learning Design Activities Additional course information Additional module information Feedback data Existing UDD New Data Calculations Lecture capture data Derived course and module data Student activity data metrics Student Activity Data – VLE, attendance, …
  10. 10. What data do we need? Jisc Curriculum Analytics10 Information Sources Issues Student information Student record systems Sensitivity of data Student course information Student record system Structure and quality Course and module information Curriculum management system, KIS data, course handbook Not fit for purpose Behaviour data Learning activity software, attendance data Open APIs, aggregation, patchy, social apps Assessment data Student records system, VLE Timely data, centrally available Learning design data Lectures and module handbooks Rarely available, no culture, staff resistance Feedback data Course evaluation survey tools Electronic? Identifiable? Frequency of feedback
  11. 11. Next steps for curriculum analytics Jisc Curriculum Analytics11 •Curriculum data gathering pilot (April - Sept 2019) •~12 pilot institutions with access to learning analytics data •Identify more detail use cases for curriculum analytics •Create metrics that can give meaningful insights •Define a minimum viable data set and how to gather it •Explore changes in practice that will facilitate curriculum analytics
  12. 12. Overview of Learning Design and Curriculum Objects
  13. 13. Emerging types of analytics in education Jisc Learning Analytics Network13 Educational analytics Learning Curriculum Welfare Intelligent campus Employability & apprenticeship Institutional
  14. 14. What makes curriculum analytics different?
  15. 15. Potential application of curriculum analytics Jisc Learning Analytics Network15 Potential applications Identify modules which appear to result in better learning and/or greater student satisfaction Understand which aspects of the curriculum result in better learning and build this knowledge into future curriculum development Understand how sequencing of exams affects student performance
  16. 16. Morris, Finegan & Wu Jisc Curriculum Analytics16 Looked at VLE logs from 354 students Completers more engaged than those who withdrew 3 indicators significant: •No of discussion posts viewed •Number of content pages viewed •Time spent viewing forums Morris, L. V., Finnegan, C. & Wu, S.-S., 2005, Tracking student behavior, persistence, and achievement in online courses, The Internet and Higher Education, Volume 8, pp. 221-231.
  17. 17. 17 Tim Hardy, University of Maryland Baltimore County Introduced adapative release feature Analytics showed 20% improvement in student performance Students performed better in next courses too Fritz, J., 2013, Using Analytics at UMBC: Encouraging Student Responsibility and Identifying Effective Course Designs, Louisville, CO: EDUCAUSE Center for Applied Research, pp. 6-7.
  18. 18. Jisc Curriculum Analytics18 Quan Nguyen, Bart Rienties et al, OU
  19. 19. Multiple uses for data gathered for curriculum analytics Jisc Learning Analytics Network20 Examples Real-time adjustments to teaching by the lecturer during the lecture Subsequent enhancements to the curriculum to provide more explanatory material the next time the lecture is delivered Correlations with grade data to ascertain whether such teaching methods appear to be effective
  20. 20. Curriculum analytics is the use of data to help understand and enhance the curriculum.
  21. 21. Potential users of curriculum analytics Jisc Learning Analytics Network22 • to see which aspects of their modules are proving more effective than others Lecturers • and others responsible for overseeing and reporting on module and course performance Associate deans • and others who can identify and promote good practice in module development Learning technologists • who wish to assess the relative success of different schools or faculties and develop policy to ensure that good practice is embedded Senior management
  22. 22. “We should only provide learning content and activities where we have ways of measuring their impact on student learning.”
  23. 23. Lecture Module code Politics101 Number 7 [out of 10] Title Marxism Learning outcome(s) addressed Understand Karl Marx’s main theories Lecturer Dave Wilson Date & time 27/01/2019 10:00 Location Renfield 210 Jisc Learning Analytics Network24
  24. 24. Curriculum objects Jisc Learning Analytics Network25 Lecture Descriptive data Module code Lecture number Learning objective(s) addressed Lecturer Date & Time Location Individual usage data Student attended (yes/no) Stakeholder Lecturer Course director Aggregated usage data Number of attendees %age of cohort who attended Observation Attendance lower than expected Question Is low attendance because students are studying for exams? Intervention Stress importance of this lecture with next cohort Correlated data Attendance at this lecture + attendance at others for this module Analytics Trend in attendance over time Observation Attendance dropping more than expected as module progresses Question Are lectures compelling enough? Are there timetabling conflicts with other modules? Intervention Review student feedback and adapt curriculum accordingly
  25. 25. A curriculum object describes an aspect of the curriculum, the data and the analytics that can be used to enhance it
  26. 26. Activity: Writing Curriculum Objects
  27. 27. Activity Jisc Curriculum Analytics28 Outcome A prioritised list of curriculum objects to be developed •In groups – what could be a curriculum object – 10 mins •In pairs – chose one and activity writing curriculum objects – 2 mins •10 mins in groups to discuss •20 mins feeding back from groups (maybe some sort of collation activity) •10 mins prioritising/voting
  28. 28. Except where otherwise noted, this work is licensed under CC-BY-NC-ND Contact us Contact Paul Bailey Senior Co-Design Manager,