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Supporting Higher Education to Integrate Learning Analytics_EUNIS20171107
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Transcript of Supporting Higher Education to Integrate Learning Analytics_EUNIS20171107
Supporting Higher Education to Integrate Learning Analytics
http://sheilaproject.eu/
Yi-Shan TsaiUniversity of [email protected]
@yi_shan_tsai
EUNIS Workshop7th November 2017
Teamhttp://sheilaproject.eu/
Supporting Higher Education to Integrate Learning Analytics
• The state of the art
• Direct engagement with key stakeholders
• A comprehensive policy framework
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Slide credit: Dragan Gašević (2017) Let’s get there! Towards policy for adoption of learning analytics. LSAC, Amsterdam, The Netherlands.
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The state of the artChallenges, adoption and strategy
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Adoption challenges
1. Leadership for strategic implementation & monitoring
2. Equal engagement with stakeholders
3. Pedagogy-based approaches to removing learning barriers
4. Training to cultivate data literacy among primary stakeholders
5. Evidence of impact
6. Context-based policies to address privacy & ethics issues and other challenges
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education –challenges and policies: a review of eight learning analytics policies. InProceedings of the Seventh International Learning Analytics & Knowledge Conference(pp. 233-242).
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Essential features of a LA policy…
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CHALLENGES
Experts’ perspectives
LA adoption in Europe
• Institutional interviews: 16 countries, 51 HEIs, 64 interviews, 78 participants
N O P L A N S
I N P R E P A R A T I O N
I M P L E M E N T E D 9 7 5
12
18
The adoption of learning analytics (interviews)
Institution-wide Partial/ Pilots Data exploration/cleaning
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LA adoption in Europe
• Institutional survey: 22 countries
N O P L A N S
I N P R E P A R A T I O N
I M P L E M E N T E D 2 13
15
16
The adoption of LA
Institution-wide Small scale N/A
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LA strategy
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No defined strategy
LA
Digitalisation strategiesTeaching & learning strategies
Immature plans for
monitoring & evaluation
StakeholdersInterests and concerns
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Interests – senior managers
• To improve student learning performance (16%)
• To improve student satisfaction (13%)
• To improve teaching excellence (13%)
• To improve student retention (11%)
• To explore what learning analytics can do for our institution/ staff/ students (10%)
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LA
Learner
driver
Teaching
driver
Institutional
driver
Interests – teaching staff
• An overview of student attendance, submission of assignments, access to coursework and resources, and performance.
• Inform course design.
• Manage a big class.
• Know ‘why’ students struggle.
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Interests – students
Personalised approach
• Inform teaching support and curriculum design.
• Support a widening access policy.
• Support students at all achievement levels to improve learning.
• Assist with transitions from pre-tertiary education to higher education, and from higher education to employment.
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Concerns – senior managers
• No one-size-fits-all solutions
• Pressure to adopt LA
• How can the institution as a whole benefit from LA?
• The strictness of existing data protection regulations makes adoption more difficult.
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Concerns– teaching staff
• Workload
• Judging staff performance
• Not all learning is digital
• No one size-fits-all solution
• Correlation does not suggest causation
• Surveillance on students
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Concerns– students
• Data collection is unnecessarily personal
• Data producing stereotypes and biases
• Limitations in quantifying learning
• Worries about human contacts and teaching professionalism being replaced by machines
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Concerns– students
Legitimate or illegitimate?
• Purpose
• Anonymity
• Access
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Privacy paradox
Transparency
Effective communication
LA policy frameworkFrom ROMA to SHEILA
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ROMA (Rapid Outcome Mapping Approach)
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperative and the sociotechnical challenge: Policy for complex systems. Research & Practice in Assessment, 9(Winter 2014), 17-28.
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SHEILA framework
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• Become an associate partner of the SHEILA project?
• Visit: http://sheilaproject.eu/
Yi-Shan [email protected]
@yi_shan_tsai
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