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Workshop presentation UL
The document discusses using data to support student success through the DESSI project. It notes that while data is valuable, what matters most is taking action based on the data. The document outlines challenges like achieving buy-in and data quality. It explains the process of using data to inform, transform understanding, take action, and review results. Key lessons are to stay student-centered, work with available data, and have a clear vision aligned with institutional priorities of enhancing student success when using learning analytics. The document prompts questions about objectives, principles, linking to priorities, specific intended changes, and potential data sources and actions to support students.
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Workshop presentation UL
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- 2. DESSI Project • Comesunder NF’s Student Success Strategic Priority • Data-informed decision-making identified as key enabler of student success • Working with >20 HEIs nationwide to support capacity to utilise data as a student support resource • Workshops • Professional Development • Guides and Resources (ORLA)
- 3. What we’ve learned •Data is an invaluable tool, not a silver bullet • It’s what you DO with it that counts • Stay student-centred and data- enabled • Work with what you have • Main Challenges: • Realising change • Merging data from multiple sources • Data quality • Achieving buy-in
- 4. How does theprocess work? 01 02 03 04 Data Info Action Review What data will we use? Extracting What will the information look like? Transforming What will we DO with the answers? Intervening How will we know if we’ve succeeded? Did it work?
- 5. Data v Information 99 81 12 105 0 20 40 60 80 100 120 123456123457 123458 123459 Activity Student Total
- 6. How does theprocess work? 01 02 03 04 Data Info Action Review What data will we use? Extracting What will the information look like? Transforming What will we DO with the answers? Intervening How will we know if we’ve succeeded? Did it work?
- 7. WHAT IS THE MOST IMPORTANT THINGTO GET RIGHT? “In order not to fail, it is necessary to have a clear vision of what you want to achieve with learning analytics, a vision that is closely aligned with institutional priorities” – Ferguson & Clow 2017
- 8. What is ourobjective? To develop our LA Capacity? • What is the best data to use? • What IT infrastructure do we need? • Success: How accurate is our model? 1 To enhance student success? • How are we going to act on the data? • How can we engage stakeholders? • Success: How many students have we helped? 2
- 9. WHAT ARE OUR PRINCIPLES? 1. Whatdoes student success mean? Is it synonymous with retention? 2. Can data do everything? What are its limitations? 3. ‘All models are wrong, some are useful’ George Box, 1976 – What are the implications of this?
- 10. HOW DOES THIS LINKIN WITH INSTITUTIONAL & NATIONAL PRIORITIES? • National Plan for Equity of Access to Higher Education 2015-2019 • HE System Performance Framework 2018-2020 • Expert Group on Future Skills Needs • Public Service ICT Strategy • Tracking Leadership Perspectives on Digital Capacity (National Forum, 2017) • Student Non-Completion on ICT Programmes (National Forum, 2015) • EU’s Digital Education Action Plan • Institutional?
- 11. WHAT (SPECIFIC) CHANGE AREWE TRYING TO BRING ABOUT?
- 12. WHAT IS OURQUESTION?
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- 14. Choosing Data Sources GDPR Relevant? Transparent? PredictiveModelling Historic data available Valuable/Insightful Operations Readily available Dynamic Institutional Ethos Prior data? Demographic? Attendance? Data Quality Complete Accurate Perceptions How does it look? Engagement is critical for success Data Types Quantitative (Attendance, VLE hits) Qualitative (Grades, Quizzes, CA) Descriptive (VLE activity)
- 15. WHAT DATA COULD WE USE? Some Potential DataSources • Hits • Resources • Quizzes • Forums • Registration • Grades • Fees • Frequency • Interactions • Resources • Issues • Frequency • Quizzes • Lecture capture • Access • Payments • Services • Satisfaction • ISSE • Lectures • Tutorials • Other Icons made by Freepik, Eucalyp, Smashicons & Dinosoftlabs from flatiron.com
- 16. WHAT ARE WEGOING TO DO WITH THE ANSWER? WHAT ACTIONS WILL WE TAKE?
- 17. HOW WILL WE ADDRESS GDPR? DataProtection by Design • Consent? • Legal Obligation? What are our grounds? How will we inform students?
- 18. WHAT ACTIONS DO WENEED TO TAKE?
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