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Jisc learninganalytics hepsa-workshop 2018

Jisc analytics slides HESPA workshop Nov 2018

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Jisc learninganalytics hepsa-workshop 2018

  1. 1. Paul Bailey, Senior Codesign Manager, Research and Development Jisc learning analytics
  2. 2. Sources of data…. Jisc learning analytics service
  3. 3. Learning Analytics Service VLE data + Student record system + Attendance data + Library data Buildings data + Learning space data + Location data Teaching quality data + Assessment data + Curriculum design data Content data + Learning pathways data Better retention and attainment Retention and attainment A more efficient campus Improved teaching & curricula Personalised and adaptive learning Efficient campus Improving teaching & curricula Now Learning analytics Institutional analytics Educational analytics Cognitive Analytics and AI Future
  4. 4. Data Collection Data Storage and Analysis Presentation and Action Jisc Learning Analytics open architecture: core Alert and Intervention system Other Staff Dashboards Consent Service (tbc) Student App: Study Goal Jisc Learning Analytics Predictor Learning Data Hub Student Records VLE Library Staff dashboards in Data Explorer Self Declared Data Attendance, Presence, Equipment use etc…. Data Aggregator UDD Transformation Toolkit Plugins and/or Universal xAPI Translator
  5. 5. Intelligent Campus Learning Analytics Service
  6. 6. Health and well-being • Can we use activity data to support health and well-being? • Timely interventions identify students earlier • Patterns of behaviour • Improved student support processes • Developing coping strategies • Additional data • Student sentiment analysis • Long term data study • Sensitive data • Build AI models to predict at risk students, also beyond graduation Learning Analytics Service
  7. 7. Student Success • Behavioural patterns that lead to success (attendance, engagement, attainment, submission date/time of assignments) • Predictive models that look at success i.e. first or 2:1 – that will model the behaviours • Grouping of behaviours that lead to success (e.g. accessing a wider range of resources, time on task, linking intended with actual behaviours) Learning Analytics Service
  8. 8. Employability • Analyse data to find indicators that lead to employability Baseline data on employability Activity data e.g. • Careers entry profiles • Careers engagement activity • Employability skills in modules • Work experience Learning Analytics Service HEPI Employability: Degrees of Value
  9. 9. Contacts Paul Bailey Further Information: Join: Learning Analytics Service
  10. 10. Panel Session Questions – panel How are planners engaged in what you do? How important is it for you to work with planners? What skills sets do you need to make effective use of this data? To the planners How many of you inform learning and teaching through your work? Who do you connect with in institutions? What skills sets do you feel are required and do L&T staff have them? Learning Analytics Service
  11. 11. User Stories Template Title: As a I want so that I
  12. 12. Activity What challenges may you need to overcome? What additional data may be required? What else maybe required to realise these opportunities? What will it look like? Learning Analytics Service Questions