Download to read offline
Uploaded byBlackboardEMEA
TLC2016 - Data for Students - A student-centred approach to analytics in Learn
The document discusses a student-centered approach to learning analytics, aiming to develop tools that are both effective for instructors and appealing to students. It highlights feedback from pilot courses which indicates mixed feelings among students regarding the usefulness and relevance of analytics data, particularly in relation to grades and engagement. The project emphasizes the importance of making analytics easy to interpret and ensuring that they integrate well with existing student support services.
More Related Content
Similar to TLC2016 - Data for Students - A student-centred approach to analytics in Learn
TLC2016 - Data for Students - A student-centred approach to analytics in Learn
- 1. Data for Students Astudent-centred approach to analytics in Learn
- 2. Ross Ward Introduction 2 Information Services Learning,Teaching & Web Services @rosswoss @UoE_LTW
- 3. Learn Introduction 3 5500 Courses peryear (and growing) Over 70% Course Coverage Primary platform for on-campus courses
- 4. Learning Analytics Introduction 4 Rapidly Developing& Evolving Powerful tools & reports Support & Advice Generally not student facing!
- 5. Big Data |Little Data 5 Big Data Little Data • Tutor Facing • Interventions • Course Evaluation • Student Retention • Student Facing • Self Monitoring • Personal Motivation • Student Attainment Focus of industry Largely ignored
- 6. Aim of ourproject • Awareness of learning analytics developments at national level – JISC | Code of Practice for learning analytics – Prof Dragan Gasevic • Student Retention not an aim or outcome • Student focussed & Student facing • Increasing Awareness – Existing tools and reports – Support and guidance – Keeping in-touch with academic ideas and practices • Develop a student-facing building block – What data is suitable? – What do students want? – Would students use it? 6
- 7. Tutor Facing Analytics 7 Whatare the expectations of our tutors? User Stories • Current practice • Awareness of tools • Risks and fears
- 8. Tutor Facing Analytics 8 Whatare tutors allowed to do? Learning Analytics Steering Group • University Policies • Legal Requirements • Ethical Considerations • JISC Code of Practice
- 9. Tutor Facing Analytics 9 Howcan we support our users? Self-enrol Support Course • 92% of approaches were available • “hand-cranked” data analysis common • Examples of usage • Highlight areas for consideration
- 10. That’s all goodand well, but… 10 What about the students?
- 11. Exposing data tostudents 11 What about the students? Some initial research Worked closely with Student Association • Not really that bothered • Mixed opinions about data privacy • Grades! Grades! Grades!
- 12. Requirements for theBuilding Block 12 What about the students? • Simple as possible • Easy to interpret • Anonymous • Manageable by Instructors • Opt-in by course Clicks & Grades
- 13. Click Data 13 • Heatmapof student activity • Personal activity only • “Quantified Self” • Data presented from data centre – not live • Course opt-in
- 14. Grade Data 14 • Tutorcontrols which grade columns are included • Simple to understand • Cannot be used on small cohort sizes
- 15. Pilot Courses 15 What aboutthe students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools Online Distance Prof Development Very large cohort No grade data Ad-hoc usage of click data (minimal) PG Foreign Language Little data to this point 3 Pilot Courses (first semester)
- 16. Feedback from course 16 Whatabout the students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools Click Count Focus Group & Survey 89%Easy to understand at first glance 69%Found the information useful 61%Would check information weekly 12%Would never check click count data
- 17. Feedback from course 17 Whatabout the students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools Click Count Focus Group & Survey allows me to plan my week see when I am most productive little relevance to my performance can track my activity in the course Visualise how often I am actually on the website I've used this to build a 'timetable' of when i should do work pretty pointless information I can't think of a way in which I could utilise the information. The information is set out in an easy to read way and tabulated well. The amount of clicks per day does not correlate with how much work you are doing It shows you how much (or little) you are doing and gives an incentive to maybe visit the learn page more often. I have not yet found a use for it. I just find it interesting.
- 18. Feedback from course 18 Whatabout the students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools Grade Averages Focus Group & Survey 97%Easy to understand at first glance 100%Found the information useful 78%Would check information weekly 4%Would never check click count data
- 19. Feedback from course 19 Whatabout the students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools Grade Averages Focus Group & Survey Allows me to see how well I'm doing compared to the rest of the class Shows you if other people are struggling with the course like you or not allows to compare own results to the average to give show what results I should be getting It gives me a good overview of how I'm doing and which topics I struggled with I find it very useful (and sometimes reassuring) to compare my grades with my peers It's a motivating tool if I do well or not compared to others. Column chart is better then a table especially for comparison Provides me with an estimation of how well I am coping with the course work. It gives me assurance that i am at the same level as the class average in the assessments and that i am doing ok in the course work.
- 20. Feedback from course 20 Whatabout the students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools Have you used this information to inform decisions on your study and work patterns? Focus Group & Survey Yes No 60.3% 39.7%
- 21. Feedback from course 21 Whatabout the students? UG Physics Large cohort Driven & competitive students Tutor engaged with students about tools TheTutors Perspective Focus Group & Survey
- 22. Summary 22 Analytics is nota “one size fits all” solution
- 23. Summary 23 Data must beeasy to interpret Fit in with existing support and guidance offered to students
- 24. Summary 24 Used at student’s owndiscretion
- 25.
- 26. Summary 26 Just one toolof many available
- 27. What next… • Coursesstill piloting the tool • Not currently developing building block further – Will be making it available on Oscelot over the summer • Keeping in-touch with other Student Facing developments – JISCApp of interest • Continue to engage with Student association • Continually shifting attitudes towards analytics 27
- 28.