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Using analytics to transform the library agenda

Dr Linda Corrin@lindacorrin

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

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.

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.

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

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.

LA Implementation in Australia1. Conceptualisation

2. Capacity & culture

3. Leadership

4. Rapid innovation cycle

5. Ethics

What do libraries want/need to know?

How can learning analytics help answer these questions?

QUESTION:

1. Performance

2. Effort

3. Prior academic history

4. Student characteristics

Teachers & Students

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Teachers & Students

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Teachers & Students

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FEEDBACK

Teachers

1. Focus groups 9 focus groups University of Melbourne

2. Interviews 12 individual interviews 3 Australian universities

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).

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).

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).

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).

Loop

Loop

Loop

Loop

Learning Design

“Learning design provides a semantic

structure for analytics” Mor, Ferguson & Wasson, 2015

“a documentation of pedagogical intent”

Lockyer, Heathcote & Dawson, 2013

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.

Discussion Activities

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.

GIVING THE DATA TO STUDENTS…

Students

How?

What?

When?Privacy & Ethics

Vendor Solutions

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

Student Dashboards

Nottingham Trent - Student Dashboard

Source: https://www.ntu.ac.uk/current_students/document_uploads/179129.pdf

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/

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/

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?

Situation Theory Question Data Representation Timing

Situation Theory Question Data Representation Timing

Planning for Libraries

melbourne-cshe.unimelb.edu.au© Melbourne Centre for the Study of Higher Education, The University of Melbourne

2016

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