Supporting you with your student’s first assessment

Students who perform well in their first assessment are successful throughout their studies

Findings from a year long learning analytics study with students in Brighton Business school supports this statement. Other research agrees:

1 The probability of failing the course if a student hasn’t submitted assessment 1 is almost 90%, making assessment 1 a strong predictor of future failure.

Some focused investigation into the role of the first Tutor Marked Assignment in predicting the final outcome, found that failing the first assessment had a significant negative impact. Therefore, the key to improving retention is in identifying those students who are at risk of either submitting but failing, or not submitting this first assessment.

The elearning team want to help schools ensure that first assessments for our level 4 students are a success and that the technology doesn’t form a barrier to our students. We will be running a number of events to help ensure that your modules are ready to go and that you have the correct resources available to support your students.

Resources for staff:

Information Services support: Electronic Management of Assessment

CLT Assessment resources : Assessment and Feedback

Drop in sessions will be offered on each campus giving you an opportunity to check your modules are “assessment-ready” and the right support is in place for your students.

Resources for students:

Next steps in #StartConnected: The next six steps students should complete before their first assignment

Student Services: Study Skills Workshops

Student drop-ins will be provided as part of #StartConnected

Contact your school Learning Technologies Adviser to discuss what you can do to prepare your students’ for their first assignment.


  1. HLOSTA, M., ZDRAHAL, Z. & ZENDULKA, J. 2017. Ouroboros: early identification of at-risk students without models 
    based on legacy data.  Proceedings of the Seventh International Learning Analytics & Knowledge Conference.
    Vancouver, British Columbia, Canada: ACM
  2. Annika Wolff Zdenek Zdrahal Drahomira Herrmannova Jakub Kuzilek Martin Hlosta. 2013. Developing predictive
    models for early detection of at-risk students on distance learning modules. Knowledge Media Institute, The OU

Recent Comments



Written by:

Katie is fascinated by the intersection of technology, games and learning and a core member of the ALT special interest group for Games and Learning. Dr Katie Piatt is the eLearning Services Manager at the University of Brighton.

Be First to Comment

Leave a Reply

Your email address will not be published. Required fields are marked *