lrt talks 20161103 jisc learning analytics
TRANSCRIPT
![Page 1: LRT Talks 20161103 JISC Learning Analytics](https://reader035.vdocuments.net/reader035/viewer/2022062503/5881ed821a28ab36088b76df/html5/thumbnails/1.jpg)
Using Data to Improve the Student Learning ExperienceProf Mark Stubbs | @thestubbs
![Page 2: LRT Talks 20161103 JISC Learning Analytics](https://reader035.vdocuments.net/reader035/viewer/2022062503/5881ed821a28ab36088b76df/html5/thumbnails/2.jpg)
Case Study: Integrated Response to Improving Student Experience
NSS Analysis, Focus Groups, Surveys
Use of Data #1
Satisfaction means targeting
‘hygiene factors’
![Page 3: LRT Talks 20161103 JISC Learning Analytics](https://reader035.vdocuments.net/reader035/viewer/2022062503/5881ed821a28ab36088b76df/html5/thumbnails/3.jpg)
University-wide Change Programme
PHASE 1
Coordinated
Step-change
Improvement
PHASE 2 Systematic
Continuous
Improvement
Listen to Student VoiceBe Bold & Joined-up
Balance Consistency & OwnershipAddress Dissatisfaction
Embed Student VoiceEmpower Programme & Module Leads
with Actionable Learner AnalyticsEmpower Students & Personal Tutors
with Actionable Learning Analytics
Refresh CurriculumRefresh Admin ProcessesWrap University Around Learner
![Page 4: LRT Talks 20161103 JISC Learning Analytics](https://reader035.vdocuments.net/reader035/viewer/2022062503/5881ed821a28ab36088b76df/html5/thumbnails/4.jpg)
Wrapping the University around the Learner
![Page 5: LRT Talks 20161103 JISC Learning Analytics](https://reader035.vdocuments.net/reader035/viewer/2022062503/5881ed821a28ab36088b76df/html5/thumbnails/5.jpg)
Delivering Intended Benefit of Improved Satisfaction
![Page 6: LRT Talks 20161103 JISC Learning Analytics](https://reader035.vdocuments.net/reader035/viewer/2022062503/5881ed821a28ab36088b76df/html5/thumbnails/6.jpg)
Current Project(s)• University-wide Data Warehouse
powering Dashboards for Personal Tutors, Heads and Programme & Module Leaders with data from• Student Records, Moodle,
Submissions, Attendance, Surveys• Embedding in standard CMI & SEM
processes• Challenges:
• Data Quality & Ownership• Verifying Accuracy of Novel
Insights• Meeting Demand with Scarce
Expertise• ‘Grounded Theory’ analysis of
comments to target improvements in:• Technology-Enhanced Learning• Assessment
Future• Opening R&A and Staff lines of
enquiry• Student App
Microsoft BI Stack | Data from Sept 201130 Fact Tables (biggies 40-50M rows) | 42 Dimensions
Approx. 700K rows added by weekly ETL
Use of Data #2
Driving Continuous Improvement
of Student Outcomes