The document discusses how learning analytics may transform higher education. It outlines the key elements of learning analytics including actors like ICT units and learning & teaching units, as well as culture. It also presents scenarios for how learning analytics could be applied, such as generating weekly student engagement reports or using analytics to remodel degree programs. The adoption of learning analytics faces challenges like integrating stakeholders, data sources, analysis techniques, and actions, while also driving cultural change across institutions.

1. ChrisMooreFlickr
Will Learning Analytics Transform
Higher Education?
Digital Education Show
Kuala Lumpur, 15 June 2015
Dr Abelardo Pardo (@abelardopardo)
Faculty of Engineering and IT
slideshare.net/abelardo_pardo

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Elements
Actors
Culture
Scenarios

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Elements

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Stakeholders
Data
Sources
Analysis
Actions

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Stakeholders
Policy
Makers
Institutional
Management
Instructors
Students
Parents

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http://www.learninganalytics.net/?p=131(lastvisitedSep2013)
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, 48(5)

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LMS interactions, Student Information System,
Recruiting, Demographics, Previous grades, Space
interaction, Social engagement

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• Statistical prediction
• Clustering and profiling
• Relationship mining
• Link prediction in
networks
• Text analysis

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• Solve academic
problems
• Suggest courses
• Financial options
• Learning strategy
• Adjust degrees
• Self awareness
• Guide investment

11. LA Matrix
Context
Question to be answered
Stakeholders Data Source Analysis Action
Policy maker
Management
Instructor
Students
Parents
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EvanBlaserflickr.com
Elements
Actors

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ICT Units
Enterprise data
warehousing
Security and Privacy
Student Oriented
Vision

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Student Services
Data producers/Consumers

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Learning & Teaching Units
Data-guided Course Redesign

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Leadership conveys
data-intensive culture

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EvanBlaserflickr.com
Elements
Actors
Culture

18. Multidisciplinary Work
Powell, S., & MacNeil, S. (2012). Analytics Series Institutional Readiness for Analytics (Vol. 1, pp. 1–11). JISC
Center for Educational Technology & Interoperability Standards, Analytics Series, 1(8).
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Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the Quality and Productivity of the Higher Education Sector. Policy and
Strategy for Systems-Level Deployment of Learning Analytics (pp. 1–35). Society for Learning Analytics Research for the Australian
Office for Learning and Teaching.

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Slow speed of adoption. Yet to go beyond
basic reporting
Bichsel, J. (2012). Analytics in Higher Education Benefits, Barriers, Progress, and Recommendations, EDUCAUSE Center for
Applied Research (pp. 1–31).

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Elements
Actors
Culture
Scenarios

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Scenario 1
• Weekly engagement
report
• Personalized
feedback
• Strategic advice for
semester
• Advice for the year

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Scenario 2
• Instructor observes
summary
• Sends personalized
feedback
• Anomalies detected
• Quick turnaround

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Scenario 3
• Degrees are
remodeled
• Investment in
infrastructure
• Overall student
experience
• Better service

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Multiple factors:
stakeholders, data
sources, analysis, actions
Multiple actors: ICT,
L&TU, Student Svcs,
Data Wranglers
Leadership has to convey
cultural change
Model matures slowly

26. ChrisMooreFlickr
Will Learning Analytics Transform
Higher Education?
Digital Education Show
Kuala Lumpur, 15 June 2015
Dr Abelardo Pardo (@abelardopardo)
Faculty of Engineering and IT
slideshare.net/abelardo_pardo