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Learning analytics for assessment and feedback

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The concept of learning analytics is gaining traction in education as an approach to using learner data to gain insights into different trends and patterns but also to inform timely and appropriate …

The concept of learning analytics is gaining traction in education as an approach to using learner data to gain insights into different trends and patterns but also to inform timely and appropriate support interventions. This webinar will explore a number of different approaches to integrating learning analytics into the context of assessment and feedback design; from overall assessment patterns and VLE usage in an institution, to creating student facing workshops, to developing principles for dashboards.
The presentations will feature current thinking and approaches from teams from the following projects in the Jisc Assessment and Feedback programme:
TRAFFIC, MMU ( speaker Rachel Forsyth)
EBEAM, University of Hudersfield, (speaker Cath Ellis)
iTeam, University of Hertfordshire (speaker Julie Vuolo)
There will be opportunities for questions and discussion throughout the session.

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    • 1. 15/05/2013 Venue Name: Go to View menu > Header and Footer to change slide 1Learning Analytics for Assessmentand FeedbackPresentersSheila MacNeill, Jisc CetisRachel Forsyth, Manchester Metropolitan UniversityCath Ellis, University of HuddersfieldJulie Vuolo, University of Hertfordshire15 May 2013
    • 2. Which best describes your role?A. Supporting/managing e-learning or technology-enhanced learningB. Staff and educational developmentC. Lecturer/course leaderD. Professional services eg library, careers, student supportE. Other – please use chat box to give detail15/05/2013 Developing Digital Literacies slide 2
    • 3. 15/05/2013 slide 3TRAFFIC project
    • 4. 15/05/2013 slide 4MMU TRAFFIC – the context• Big institutional curriculum change• One of aims: to address over-assessment– Max 2 summative elements per 30 credit module– Rolling out year-by-year to all UGs from Sep 2011
    • 5. Analytics and systems development15/05/2013 slide 5
    • 6. Analytics and systems development15/05/2013 slide 6Personalised submission sheetStudent ID (+ Unit code)
    • 7. Analytics and systems development15/05/2013 slide 7
    • 8. Analytics and systems development15/05/2013 slide 80500001000001500002000002500001 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 2 4 6 8 10 12 14 162012 2013+ = 8.3M hits(since Jan 1, 2012)
    • 9. Analytics and systems development15/05/2013 slide 9
    • 10. Analytics and academic development15/05/2013 slide 10
    • 11. Analytics and academic development15/05/2013 slide 11
    • 12. Analytics and academic development15/05/2013 slide 12
    • 13. Analytics and academic development15/05/2013 slide 13
    • 14. Analytics and enhancement15/05/2013 slide 1430%40%50%60%70%80%90%100%The teaching on mycourseAcademic support Learning resources Overall Satisfaction30%40%50%60%70%80%90%100%Theteaching onmy courseAssessmentand feedbackAcademicsupportOrganisationandmanagementLearningresourcesPersonaldevelopmentOverallSatisfactionI am satisfiedwith theStudentsUnion(Associationor Guild) atmy institution2011MMU resultSimilar institution results2012
    • 15. Analytics and enhancement15/05/2013 slide 152011/12 (n=8,709) 2012/13 (n=8,646)FBSATISFCNHAND-INS BUNCHINGFBSATISFCNHAND-INS BUNCHINGL4 3.75 5.61 1.293.79(+1%)5.59(0%)1.25 (-3%)L5 3.70 10.47 1.383.77(+2%)5.78 (-45%)1.25 (-9%)L6 3.72 7.81 1.303.8(+2%)7.86(+1%)1.26 (-3%)Predictors of overallsatisfaction
    • 16. 15/05/2013 slide 16EBEAM
    • 17. 15/05/2013 slide 17Learning AnalyticsDepartment LevelCath EllisUniversity of Huddersfield
    • 18. Social networksRetention
    • 19. InstitutionalisedOperationalisation
    • 20. Social networksRetentionInstitutionalisedOperationalisation
    • 21. AssessmentAll students are assessed and all tutorsmark student workUbiquitousRoutine NormativeWidelyunderstoodExpectedEveryone‘gets’ itImportantIt’s whatstudents payforIt’s whattutors arepaid to doFocussed onachievementnot justretention
    • 22. Academic AnalyticsBusinessintelligencein HELearning AnalyticsFocused onretentionand (to alesserextent)achievementAssessment AnalyticsAssessment data used within a learning analyticsstrategy
    • 23. DegreeClassificationGPA atprogressionModuleResultsAssessmenttask resultsGranularity
    • 24. DegreeClassificationGPA atprogressionModule ResultsAssessmenttask resultsGranularityCommonerrorsAssessmentcriteria
    • 25. IpsativeIndividualimprovementover timeImprovementbetweencohorts
    • 26. Department levelCoordinated approachShared comments setsLinked to other supportmaterialsSame ‘song sheet’Pooled dataStudentModuleCourseFed through to dashboardStudent facingTutor facingInstitution/PSRB facing
    • 27. 15/05/2013 slide 29ITEAM
    • 28. 15/05/2013 slide 30
    • 29. 15/05/2013 slide 31Course monitoring and the Student Dashboard• A development which pulls together informationabout VLE (module level) engagement andassessment performance and displays it in astudent and staff view within the VLE• To help students with the self-regulation of theirstudies.• To help staff identify where support for individualsor groups of students might be needed
    • 30. 15/05/2013 slide 32FilterstudentsSet threshold
    • 31. 15/05/2013 slide 33
    • 32. 15/05/2013 slide 34Student input• ..wanted to know where they were in relation to others as thiswould be a motivator to do better• ..felt it would not de-motivate them to know they were at thelower end of the league table.• ..said the currency of data is important if dashboard is to bemeaningful to them• ..would like a predictor or calculator for working out finalclassification• ..wanted re-assurance that their information would only beseen by ‘those needing it’• ..were interested in what else could go in there e.g. bookloans, module pass/fail rates
    • 33. 15/05/2013 slide 35Design considerationsGeneral, Access, Display, Content, SupportAccess• Levels of staff access need to be agreed & processes developed togate-keep access• It should be clear to staff and students what levels of access exist• Entry to the SPD should be quick and easy e.g. icon click onhomepage• Effective filters should be employed to allow staff to accessrelevant information quickly• Links to other University information systems should beembedded in the SPD e.g. University Policies and Regulations onclassification
    • 34. 15/05/2013 slide 36Design considerationsDisplay• Page content should be signposted clearly to aid navigation withinthe dashboard• Data should be presented in a simple, unambiguous manner forclarity of meaning and to promote engagement• Colour schemes should be simple and consistent to ensure clarity ofpresentation and to promote engagementSupport• There must be education for student and teachers about using theSPD• Education should target staff and students and include how toaccess and functionality, limitations and data interpretation.
    • 35. Average Grade for Module Hits
    • 36. PotentialWhat other data would be useful to influencelearning and teaching?