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  • 1. Early Warning System for Identifying students at risk of failing
    Roger Brown
    Center for Educational Technology
    University of Cape Town
    roger.brown@uct.ac.za
  • 2. Overview
    PART 1
    Why we started looking at EWS
    The functional requirements of the system
    Institutional fit
    Matching Vula (Sakai) affordances to the functional requirements
    Developments that would allow Sakai to act as an EWS (well some of them anyway!)
    How UCT is moving forward
    PART 2 - Discussion
    Activity and course grade
    Your ideas
    12th Sakai Conference – Los Angeles, California – June 14-16
    2
  • 3. Part1: Introduction
    In 2009 Senate Re-admission Review Committee recommended that greater attention needed to be given to Faculty EWS.
    The SRRC report recommended that:
    the SRRC monitors data from the Faculty EWS with the aim of assessing and reporting on the impact mid-year exclusions have on throughput rates, and
    Investigate the EWS issue to assess the most effective approach to adopt a single system thereby providing consistency across faculties.
  • 4. EWS – Functional requirements
    The ability to record a standard number of “in course”results/performance/grade
    The ability to create a current class list of all valid registered students in a course, populate this list with “in course results”, and load these to the students’ PeopleSoft record.
    Specification of an “at risk” threshold value for these results
    Recording of comments against a student
    Generation of communication to identified students 
    Retentions of a permanent record of these communications
    Specified reporting of student performance
    For a student within a course across all courses
    For a specified cohort of students
    Access and authorisations to entry grades, viewing of grades and running of reports, course conveners and/or mentors and/or other “intervention” managers
  • 5. EWS – Institutional criteria
    Technical Implementation
    Technical Integration
    Usability and User Support Requirements
    Technical Support
    Security and Authorisations
    Student Access
    Overall Reporting Capacity
    Cost (of licensing, implementation and support)
    Vendor and Product Sustainability
  • 6. EWS Functional requirements and Vula (Sakai 2.7) - Integration
    PeopleSoft
    HEDA - Higher Education Data Analyzer
    Demographic and K12 data (currently used by IPD)
    IDvault
  • 7. Vula: Affordances vs Requirements
    Groups: Easily created and populated
    Configurable Roles:
    Access
    and authority
    Gradebook
  • 8. Vula: Affordances vs Requirements
    Communication:
    Email
    Internal message
    SMS
    • Secure
    • 9. Familiar
    • 10. Widely accepted by admin/academic staff (2474 staff used Vula in 2010)
  • Vula: Affordances vs Requirements -Gradebook
    1. Import marks from excel
    3. Integrates with Vula testing tools
    2. Weighting and Categories
  • 11. Assessing students at risk?
    Back to My Workspace
    Out of Gradebook
  • 12. Vula: Limitations (currently)
    “At Risk” assessment would need to be done outside Vula
    Not all lecturers/course convenors use Vula
    Vula is not the authoritative source of marks
    Vula does not “push” data to PS
    Staffing
  • 13. Vula: R&D for EWS application
    Automated Gradebook export to ETL platform or preferably an internal logic
    Internal or external algorithm development for risk analysis
    Auto grouping based on risk analysis
    Reporting communications, display, etc
    Predictive logic based on previous student performance
  • 14. How UCT is moving forward
    The task team recommended in phase 1
    EWS to utilise the functionality of PeopleSoft (some developments required)
    Improve the integration of Sakai and PS
    gradebook export to PS (it’s easier to get grades into GB than into PS )
    Samigo& Asn => GB => PS
    12th Sakai Conference – Los Angeles, California – June 14-16
    13
  • 15. Part 2: Vula: Final grade vs all events (PSY1001W)
  • 16. Your Ideas
    12th Sakai Conference – Los Angeles, California – June 14-16
    15
    Predictive modelling
    GB in students’ “My workspace”
    ?
    Using ETL
    Communicating “failure”
    Learning analytics
    A “read only” SU role in Sakai
    Data mining – automating and exposing
    More than grades only? – is attendance a predictor?