Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insight Europe 2016
Transcript of Using Analytics to Transform the Library Agenda - Linda Corrin | Talis Insight Europe 2016
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Using analytics to transform the library agenda
Dr Linda Corrin@lindacorrin
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DEFINITIONthe 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
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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.
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PossibilitiesLearning 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.
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Hot off the press - JISC Report As a tool for quality assurance and
quality improvement
As a tool for boosting retention rates
As a tool for assessing and acting upon differential outcomes among the student population
As an enabler for the development and introduction of adaptive learning
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Libraries and Student SuccessPositive 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.
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LA Implementation in Australia1. Conceptualisation
2. Capacity & culture
3. Leadership
4. Rapid innovation cycle
5. Ethics
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What do libraries want/need to know?
How can learning analytics help answer these questions?
QUESTION:
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1. Performance
2. Effort
3. Prior academic history
4. Student characteristics
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Teachers & Students
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Teachers & Students
0101010101010101010101010
?01010101010101010110101001010101010101010101010100101010101010101010101010
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Teachers & Students
0101010101010101010101010
?01010101010101010110101001010101010101010101010100101010101010101010101010
FEEDBACK
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Teachers
1. Focus groups 9 focus groups University of Melbourne
2. Interviews 12 individual interviews 3 Australian universities
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Focus Groups
Student performance
Student engagement ‘At risk’ studentsAttendanceAccess to learning resourcesParticipation in communicationPerformance 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).
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Focus Groups
Student performance
Student engagement += ? (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).
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Focus Groups
Student performance
The learning experience
Quality of teaching and curriculum
Administrative functions associated with L&T
Student engagement
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).
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InterviewsInterviews 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).
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Loop
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Loop
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Loop
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Loop
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Learning Design
“Learning design provides a semantic
structure for analytics” Mor, Ferguson & Wasson, 2015
“a documentation of pedagogical intent”
Lockyer, Heathcote & Dawson, 2013
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Learning Analytics for Learning Design Conceptual Framework
Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gasevic, D., Mulder, R., Williams, D., Dawson, S., Lockyer, L. (2016). A conceptual framework linking learning design with learning analytics. In Proceedings of the 6th International Conference on Learning Analytics and Knowledge. New York: ACM.
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Discussion Activities
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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.
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GIVING THE DATA TO STUDENTS…
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Students
How?
What?
When?Privacy & Ethics
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Vendor Solutions
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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
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Student Dashboards
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Nottingham Trent - Student Dashboard
Source: https://www.ntu.ac.uk/current_students/document_uploads/179129.pdf
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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/
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JISC Student Learning Analytics App
Source: Sclater, N. (2015) Student app for learning analytics: functionality and wireframes. http://analytics.jiscinvolve.org/wp/2015/08/21/student-app-for-learning-analytics-functionality-and-wireframes/
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Situation
Theory/Design
Question
Data
Representation
Timing
Planning for Learning AnalyticsWhat is the problem/issue you want to address?
What learning theory or design informs the situation?
What is the specific question(s) you want to answer about the situation?
What data do you need to answer the question?
How can this data be represented in a way it will be meaningful?
When would it be best to receive this information?
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Situation Theory Question Data Representation Timing
Situation Theory Question Data Representation Timing
Planning for Libraries
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melbourne-cshe.unimelb.edu.au© Melbourne Centre for the Study of Higher Education, The University of Melbourne
2016
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