1. Learning Analytics @ UEL: An update on developments Gary Tindell Information Improvement Manager Feb 3rd 2016

2. Student Engagement Journey Phase 1: Student Attendance (App developed and in constant use) Phase 2: Student Engagement for Personal Tutors (deployed spring 2015) Phase 3: Developing a Student Engagement metric (Model developed) Phase 4: Research informing development (Preliminary analysis into correlations between student engagement and assessment) Phase 5: Development of student-facing app (Designing student-friendly data visualisations)

3. Phase 1: Attendance Monitoring Attendance reporting project started in 2011 with card readers installed in early 2012 with new timetabling software. Collects data on student attendance via swipe card system and can be broken down by school, module, event and by the student. Developed attendance reporting app using QlikView (BI tool) in early 2013 and deployed in spring 2013.

4. Phase 1: Use of Student Attendance Application App used to identify students whose attendance falls below the 75% threshold level and enable us to put in place, effective interventions via Student Retention Team. Been able to correlate student attendance with module performance and other characteristics.

5. LB Newham Example Demo 2013 Semester A: Average absence rate of 24%. Those with absences above the average rate are more likely to fail their modules

6. Phase 2: Student Engagement for Personal Tutors Although attendance is probably the single most important predictor of student success, there are other indicators that are also linked to student progression & achievement. Developed and deployed a Student Engagement app for use by Personal Tutors that incorporates student attendance, Moodle activity, use of library facilities, e- book activity, coursework submission, assessment profile, etc.

8. Phase 3: Student Engagement Metric Developed an app that integrates all this data and calculates a level of student engagement based on a weighting system for identifying those students who are most at risk of leaving Provide us with an early warning system of students who are risk of leaving Ultimately, want to provide students with an indication of their level of student engagement compared to their cohort

9. Data Extracts: Library Access & book loans Samsung tablet Kortext data Coursework grades and marks Attendance Athens online journals Engagement

10. Phase 4: Research informing development Took a deliberate break from developing student engagement apps to examine the correlations between measures of student engagement and student performance Intention to use the outcomes of this analytic activity to inform the weightings we apply to our student engagement app Not surprisingly, initial analysis found highest correlations for attendance and average module marks Subsequent multiple regression modelling suggests all engagement measures are significant

11. Phase 4: Research informing development Undertook a scenario testing exercise where we examined 18 different sets of weightings to determine a metric dependant on year and emphasis Based on this exercise, we’ve developed a quadrant based model that identifies ‘low engagers’ from ‘high engagers’ and provides an indication of student performance One of the key measures is previous student performance. Do you treat that as a student engagement measure? Recently developed a scenario testing app that is being evaluated and refined

12. Phase 5: Developing a student-facing application Currently in progress, developed and evaluating 3 different sets of visualisations of student engagement: 1)Compares a individual student engagement level with UEL, School & Course 2)Provides a student with an indication of where they are located in terms of student engagement 3)Provides an indication of the ‘distance to travel’ for a student to progress to another quadrant

13. Next Steps/Ongoing work Continue refining the Student Engagement Metric app by adding additional measures Established a small research group and continue refining our statistical model Deploy the student facing app to a student cohort and evaluate their feedback Can we answer the following questions: 1)Can we accurately predict student performance based on a metric? 2)Can providing feedback to students on levels of engagement really change study patterns?

14. Questions?

15. Contact Details Gary Tindell Information Improvement Manager IT Services University of East London Email: g.c.tindell@uel.ac.ukg.c.tindell@uel.ac.uk Tel: 0208 223 2065