Using university level data for institutional research
Background/context The university as an institution collects data on students for a variety of purposes and stakeholders, from student secondary school records to determine who will gain entrance, to student grades for academic progression and graduation, or student engagement and teaching surveys to assess the quality of education. When these data sets are combined, they can paint richer pictures of the institution, programmes of study, departments or even individual papers that would otherwise go unnoticed. Beyond that, the combined data can also inform the research on teaching and learning in tertiary settings. Analyses arising from such research can be used for professional development purposes, both to assist lecturers, departments and programmes in identifying potential issues in and across curricula and for educational managers aiming to adapt policies to improve the student learning experience. Research/evaluation method In this poster, we share examples of institutional research with combined data sets that has helped university departments develop better pictures of what types of students enter their programme, how they progress, what issues were encountered by students in the curriculum, where those issues originated and how they could be effectively addressed. In clarifying this, we will illustrate: *How using New Zealand National Certificate of Educational Achievement (NCEA) data can used to predict success in first year courses, and *How grade variability analyses (performance of the same students in different courses) can identify curricular "cake-walks" or bottlenecks. Outcomes Through illustrated examples we present both the benefits of these approaches for particular departments or lecturers, as well as the challenges in data management and the legal and ethical implications for using existing student data for research purposes. Key references American Educational Research Association. 2000, 'Ethical Standards of the American Educational Research Association,' retrieved September 7, 2007, from http://www.aera.net/uploadedFiles/About_AERA/Ethical_Standards/EthicalStandard.pdf Brogt, E., Sampson, K., Comer, K., Turnbull, M., and McIntosh, A. (in prep). NCEA achievement and success in university biology. James, A., Montelle, C., and Williams, P. (2008). From lessons to lectures: NCEA mathematics results and first-ear mathematics performance. International Journal of Mathematical Education in Science and Technology, 39(8): 1037-1050. Luan, J., and Zhao, C-M. (2006). Practicing data mining for enrollment management and beyond. New Directions for Institutional Research, 131: 117-122. Serban, A. M. (2002). Knowledge management: The 'fifth face' of institutional research. New Directions for Institutional Research, 113: 105-111.