Your SlideShare is downloading. ×
Sakai la-ewsv2
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Sakai la-ewsv2


Published on

EWS using Sakai

EWS using Sakai

Published in: Education

  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. Early Warning System for Identifying students at risk of failing
    Roger Brown
    Center for Educational Technology
    University of Cape Town
  • 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
  • 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
    HEDA - Higher Education Data Analyzer
    Demographic and K12 data (currently used by IPD)
  • 7. Vula: Affordances vs Requirements
    Groups: Easily created and populated
    Configurable Roles:
    and authority
  • 8. Vula: Affordances vs Requirements
    Internal message
    • 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
  • 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
  • 15. Part 2: Vula: Final grade vs all events (PSY1001W)
  • 16. Your Ideas
    12th Sakai Conference – Los Angeles, California – June 14-16
    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?