will learning analytics transform higher education?
TRANSCRIPT
Chris
Moore
Flickr
Will Learning Analytics TransformHigher Education?
Digital Education ShowKuala Lumpur, 15 June 2015
Dr Abelardo Pardo (@abelardopardo)Faculty of Engineering and IT
slideshare.net/abelardo_pardo
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JeanE
tienneM
inh-Duy
Poirrierflickr.com
Abelardo Pardo Will Learning Analytics Transform Higher Education? 3
Evan
Blaserflickr.com
Elements
Actors
Culture
Scenarios
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Evan
Blaserflickr.com
Elements
Abelardo Pardo Will Learning Analytics Transform Higher Education? 5
Thomas
LethO
lsenflickr.com
Stakeholders
DataSources
Analysis
Actions
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Martin
Fischflickr.com
Stakeholders
PolicyMakers
InstitutionalManagement
Instructors
Students
Parents
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http://ww
w.learninganalytics.net/?p=131
(lastvisitedS
ep2013)
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, 48(5)
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puthoOrP
hotographyflickr.com
LMS interactions, Student Information System,Recruiting, Demographics, Previous grades, Spaceinteraction, Social engagement
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zzpzaflickr.com
• Statistical prediction• Clustering and profiling• Relationship mining• Link prediction in
networks• Text analysis
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GotC
reditflickr.com
• Solve academicproblems
• Suggest courses• Financial options• Learning strategy• Adjust degrees• Self awareness• Guide investment
LA MatrixContext
Question to be answered
Stakeholders Data Source Analysis ActionPolicy makerManagement
InstructorStudentsParents
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Evan
Blaserflickr.com
Elements
Actors
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johnthrmFlickr
ICT Units
Enterprise datawarehousing
Security and Privacy
Student OrientedVision
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Matheson
LearningC
omm
onsflickr.com
Student Services
Data producers/Consumers
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Sanadreas
flickr.com
Learning & Teaching Units
Data-guided Course Redesign
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Richard
McD
onald-Proctorflickr.com
Leadership conveysdata-intensive culture
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Evan
Blaserflickr.com
Elements
Actors
Culture
Multidisciplinary Work
Powell, S., & MacNeil, S. (2012). Analytics Series Institutional Readiness for Analytics (Vol. 1, pp. 1–11). JISCCenter 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 andStrategy for Systems-Level Deployment of Learning Analytics (pp. 1–35). Society for Learning Analytics Research for the AustralianOffice for Learning and Teaching.
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Ed
Coyle
flickr.com
Slow speed of adoption. Yet to go beyondbasic reporting
Bichsel, J. (2012). Analytics in Higher Education Benefits, Barriers, Progress, and Recommendations, EDUCAUSE Center forApplied Research (pp. 1–31).
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Evan
Blaserflickr.com
Elements
Actors
Culture
Scenarios
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University
ofDenverflickr.com
Scenario 1
• Weekly engagementreport
• Personalizedfeedback
• Strategic advice forsemester
• Advice for the year
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PN
NL
Pacific
Northw
estflickr.com
Scenario 2
• Instructor observessummary
• Sends personalizedfeedback
• Anomalies detected• Quick turnaround
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Betta
Design
flickr.com
Scenario 3
• Degrees areremodeled
• Investment ininfrastructure
• Overall studentexperience
• Better service
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Gerard
Stolk
flickr.com
Multiple factors:stakeholders, datasources, analysis, actions
Multiple actors: ICT,L&TU, Student Svcs,Data Wranglers
Leadership has to conveycultural change
Model matures slowly
Chris
Moore
Flickr
Will Learning Analytics TransformHigher Education?
Digital Education ShowKuala Lumpur, 15 June 2015
Dr Abelardo Pardo (@abelardopardo)Faculty of Engineering and IT
slideshare.net/abelardo_pardo