Using Entry Scores to Predict University Completion

Author(s):

Knipe Sally(presenting / submitting)

Conference:

ECER 2013

Network:

11. Educational Improvement and Quality Assurance

Format:

Research Workshop

Session Information

11 SES 09 A, Using Entry Scores to Predict University Completion

Research Workshop

Time:

2013-09-12

11:00-12:30

Room:

D-312

Chair:

Knipe Sally

Contribution

A substantial amount of data now exists across a range of disciplines (Kriegel, Borgwardt,

Kroger, Oryakhin, Schubert & Zimek, 2007), and researchers, professionals, and those employed in Non-Government Organisations can access existing data for research purposes. Further, data compiled by government organisations is now more accessible to a researcher through improved systems in statistical analysis, data management and retrieval methods, as well as access to data through internet websites and computer software that enables a researcher to access, compress, and download data to a personal computer (Miller and Han, 2000). A range of institutions gather considerable amounts of data that can be ‘mined’ expanding opportunities for researchers seeking to investigate issues that are institutional in nature (Knipe 2011).

Governments world-wide, have been implementing policies and setting targets to increase the numbers of people with a university qualification. In countries such as Canada and the United States of America (Christofides, L., Hoy, M. & Yang, L. 2009), in Europe, the Bologna Declaration (1999), in Australia the Review of Australian Higher Education (2008) support increasing overall student numbers in higher education, including the removal of restrictions on student from low SES backgrounds. The increase in the number of university places ignites concerns that there is a risk of large numbers of students being admitted with unacceptably low entry scores in order to meet government targets (Devlin, Kift, Nelson, Smith and McKay, 2012).

The aim of this study was to use existing data to investigate student progress data for teacher education undergraduate courses over a four-year period. The cohort that entered university several years prior to the recent admissions targets, identified in the Review of Australian Higher Education (2008), in order to identify relationships between entry criteria and course completion. The data selected for the study were drawn from students who enrolled in five undergraduate teacher education degree programs at the beginning of the 2006 academic year and completed their studies in 2009.

Method

Data for this study were provided by the Division of Planning and Audit at Charles Sturt University (CSU). To ensure ethical protocol and confidentiality, any information that could identify an individual student was removed from the database. Data selected for the study were drawn from students who enrolled in five undergraduate teacher education degree programs at the beginning of the 2006 academic year and completed their studies in 2009. These five courses consisted of one primary and one early childhood course and three secondary courses with a total enrollment of 609.

Expected Outcomes

Results of this study raise some interesting issues regarding low socio-economic targets, course completion rates and admission pathways for students entering undergraduate programs. The profile of the 2006 intake into five undergraduate teacher education courses indicates that Charles Sturt University was already fulfilling the targets set by the federal government in 2010 regarding the percentage of candidates from low socio-economic backgrounds admitted to these courses. The greatest number of candidates (68.9%) entering teacher education programs came into early childhood and primary programs, a pattern that is consistent with national trends (Department of Education, Employment and Workplace Relations, 2010). The major finding in this study shows that for all the concern about entry standards and Australian Tertiary Admissions Rank, there was little difference in course completion rates between entry pathways.

References

Australian Government (2008) Review of Australian Higher Education. Retrieved December 2012 www.innovation.gov.au/HigherEducation/Documents/Review/PDF/Higher%20Education%20Review_one%20document_02.pdf Christofides, L., Hoy, M. & Yang, L. 2009 The Determinants of University Participation in Canada (1977-2003). Canadian Journal of Higher Education, Vol39, No. 2 pp. 1-24. Devlin, M., Kift, S., Nelson, K., Smith, L. And McKay, J. (2012) Effective teaching and support of students from low socioeconomic status backgrounds: Resources for Australian higher education, Office for Teaching and Learning, Sydney Knipe, S (2011) Research Using Government Data Sets: An underutilised resource. Educational Research and Reviews. Vol. 6(21), pp. 993-996. Kriegel, H., Borgwardt, K., Kröger, P., Pryakhin, A., Schubert, M., Zimek, A. (2007) Future Trends in Data Mining. Data Mining and Knowledge Discovery. 15: 87-97 Miller, H & Han, J. (2000) Geographic Data Mining and Knowledge Discovery: An Overview. [Electronic Version] http://www.geog.utah.edu/~hmiller/papers/GKD_Chapter1.pdf. The Bologna Declaration (1999) Joint Declaration of the European Ministers of education Retrieved 12th December 2012 www.ond.vlaanderen.be/hogeronderwijs/bologna/MDC/BOLOGNA_DECLARATION1.pdf

Author Information

Knipe Sally (presenting / submitting)

Charles Sturt University

Albury

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