Maximising the teaching and assessment opportunities for higher education students - data driven decision making for quality assurance purposes

Simon B. Bedford, Rodney Vickers, Emma Purdy, Jan Sullivan


Background: It has become increasingly important to collect institutional data to measure and evaluate teaching and assessment improvements and to evidence quality assurance for both internal policy obligations and external review (TEQSA). However, how this data is presented, reported and targeted to individuals at various levels is of equal importance in ensuring that the correct decisions are made to maximise the student learning experience.

Aims: The primary aim of this work was to see how best to provide analytic data on subjects and courses at the University of Wollongong to staff and committees for monitoring and quality assurance improvement. This presentation aims to explain how effective this has been and what lessons others can learn from this experience.

Design and methods: The group have targeted the review and presentation of data for quality assurance purposes across the institution, for processes including:
1) Faculty and School assessment committee meetings
2) Subject monitoring reporting
3) Comparative student outcomes
4) Annual and 5 yearly course review processes
5) Annual collaborative partner (third party provider) reviews
Specifically, the group has been looking at the types of data captured, data display formats for different audiences, its timing and method of delivery and how data reports can be targeted to particular end users. The group will also review the process for closing the loop and following-up on outcomes, improvement actions and recommendations.

The group has so far looked at the data needs of 1) assessment committees, and 2) subject monitoring, for internal quality assurance purposes and in relation to the revised Higher Education Standards Framework. In particular the group has focused on what changes are required to ensure all relevant data is captured, the method of delivery of this data (hard vs soft reports), the timing of the data collection and making it available at key points within the academic cycle. These data contain valuable teaching and assessment information for academics, part-time teaching staff and professional staff on students’ engagement, motivation and progression in courses of study. The premise behind this work is that the higher the quality of the data provided the more informed will be the quality enhancements.

Conclusions: Consensus is slowly being obtained for the type and form of data for each of the five processes. This consensus may be challenged as consultation is widened to include more stakeholders. University managers will need to be convinced of the worthiness of this work so as to allocate sufficient resources to make this happen. However, the concepts explained in this presentation have so far been received enthusiastically by all participants and demand for such data reporting is strong.
Proceedings of the Australian Conference on Science and Mathematics Education, The University of Queensland, Sept 28th to 30th, 2016, page X, ISBN Number 978-0-9871834-4-6.


Data visualisations, Learning and assessment design, Pedagogical intent & practice

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