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Learning Analytics and libraries

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Dr Linda Corrin, University of Melbourne, discusses all things learning analytics. One of the important take-aways from this presentation is to define the question(s) before you start collecting data.

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Learning Analytics and libraries

  1. 1. Using analytics to transform the library agenda Dr Linda Corrin @lindacorrin
  2. 2. DEFINITION the 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 Society for Learning Analytics Research
  3. 3. Long P. & Siemens G. (2011) Penetrating the fog: analytics in learning and education. EDUCAUSE Review 46, 31–40. Available at: http://www.educause.edu/ero/article/penetrating-fog-analytics- learning-and-education Micro Meso Macro Buckingham Shum, S., Knight, S., & Littleton, K. (2012). Learning analytics. In UNESCO Institute for Information Technologies in Education. Policy Brief.
  4. 4. Possibilities Learning analytics  Personalised learning  Understanding the learning process  Information about the students’ context  Pedagogical and assessment improvements  Understanding student motivation and attitude Academic analytics  IT service provision  Curriculum mapping  Review of teaching structures  Student support services  Student retention Drachsler, H., & Greller, W. (2012). The pulse of learning analytics. Understandings and expectations from the stakeholders. In S. Buckingham Shum, D. Gasevic, & R. Ferguson (Eds.), 2nd International Conference Learning Analytics & Knowledge (pp. 120-129). April, 29-May, 02, 2012, Vancouver, BC, Canada.
  5. 5. Libraries and Student Success Positive impact on grades a Positive impact on retention b Positive impact on grades and retention c a. Jantti, M., & Cox, B. (2013). Measuring the value of library resources and student academic performance through relational datasets. Evidence Based Library and Information Practice, 8(2), 163-171. b. Haddow, G. (2013). Academic library use and student retention: A quantitative analysis. Library & Information Science Research, 35(2), 127-136. c. Soria, K. M., Fransen, J., & Nackerud, S. (2014). Stacks, serials, search engines, and students' success: First-year undergraduate students' library use, academic achievement, and retention. The Journal of Academic Librarianship, 40(1), 84-91.
  6. 6. LA Implementation in Australia 1. Conceptualisation 2. Capacity & culture 3. Leadership 4. Rapid innovation cycle 5. Ethics
  7. 7. What do libraries want/need to know? How can learning analytics help answer these questions? QUESTION:
  8. 8. Image source: https://edtechdigest.wordpress.com/2012/05/10/learning-analytics-the-future-is-now/
  9. 9. 1. Performance 2. Effort 3. Prior academic history 4. Student characteristics
  10. 10. Current Research 0101010101010101010101010 ?
  11. 11. Current Research 0101010101010101010101010 ? 010101010101010101101010 0101010101010101010101010 0101010101010101010101010
  12. 12. Focus Groups Student engagement The learning experience Quality of teaching and curriculum Administrative functions associated with L&T Student performance ‘At risk’ students Attendance Access to learning resources Participation in communication Performance in assessment Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers. In H. Carter, M. Gosper & J. Hedberg (Eds.), Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).
  13. 13. Focus Groups Student engagement The learning experience Quality of teaching and curriculum Administrative functions associated with L&T Student performance + = ? (ideal student) Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers. In H. Carter, M. Gosper & J. Hedberg (Eds.), Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).
  14. 14. Focus Groups Student engagement The learning experience Quality of teaching and curriculum Administrative functions associated with L&T Student performance Feedback Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers. In H. Carter, M. Gosper & J. Hedberg (Eds.), Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).
  15. 15. Focus Groups Student engagement The learning experience Quality of teaching and curriculum Administrative functions associated with learning & teaching Student performance  Greater understanding of how students develop knowledge  Track prior knowledge and it’s development through learning activities  Data?
  16. 16. Focus Groups Student engagement The learning experience Quality of teaching and curriculum Administrative functions associated with learning & teaching Student performance  Automated textual analysis of messages sent to student support services  Assessment (formative and summative) to identify areas for review  Access to support resources
  17. 17. Focus Groups Student engagement The learning experience Quality of teaching and curriculum Administrative functions associated with learning & teaching Student performance  Assessment of consistency of student placements  Enrolment and profiling of tutorial groups  Tracking safety requirements for field trips  Guidance for students on future subject selection
  18. 18. Interviews Interviews with 12 teaching academics (UoM, Macquarie, UniSA) 1. Fairly basic analytics requests 2. Focus on engagement analytics 3. Limited use of technological tools (blended) 4. Concerns over ability to interpret data Kennedy, G., Corrin, L., Lockyer, L., Dawson, S., Williams, D., Mulder, R., Khamis, S., & Copeland, S. (2014). Completing the loop: returning learning analytics to teachers. In B. Hegarty, J. McDonald, & S.-K. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 436-440).
  19. 19. Loop
  20. 20. Loop
  21. 21. Loop
  22. 22. Learning Design “Learning design provides a semantic structure for analytics” Mor, Ferguson & Wasson, 2015 “a documentation of pedagogical intent” Lockyer, Heathcote & Dawson, 2013
  23. 23. Interaction with resources Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2-3), 107-124.
  24. 24. GIVING THE DATA TO STUDENTS…
  25. 25. Student Perspectives “I just log into the [LMS] to download learning materials and print them. I do not think my online learning behaviours such as log-ins would reflect my general efforts for learning and learning outcomes” Park, Y., & Jo, I. H. (2015). Development of the Learning Analytics Dashboard to Support Students' Learning Performance. Journal of Universal Computer Science, 21(1), 110-133.  Plan learning schedule  Manage learning processes  Set learning goals  Get an objective and accurate perspective  Do not want such data to impact final score and grade
  26. 26. Student Dashboards Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In B. Hegarty, J. McDonald, & S.-K. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 629-633).
  27. 27. JISC Student Learning Analytics App Source: Sclater, N. (2015) What do students want from a learning analytics app?. http://analytics.jiscinvolve.org/wp/2015/04/29/what-do-students-want-from-a-learning-analytics-app/
  28. 28. Situation Theory Question Data Representation Timing Situation Theory Question Data Representation Timing Planning for Libraries
  29. 29. melbourne-cshe.unimelb.edu.au © Melbourne Centre for the Study of Higher Education, The University of Melbourne 2016

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