There is growing pressure on teacher education institutions to provide more rigorous academic admission procedures as an initial step in improving teacher quality. The University of Melbourne has developed and implemented TeacherSelector, a comprehensive process for selecting preservice teaching candidates that assesses a range of cognitive, behavioural and motivational characteristics using online assessments and questionnaires. Benefits of the process are bi-directional in that they provide a process of reflection for the candidate as well as information to the institution about the potential of the candidate to succeed in the course and beyond. An integral part of the development process for TeacherSelector was confirmation that measures and assessments were relevant to course success and the role of teaching, and that they demonstrated sound psychometric structure. Conceptual substantiation of the relationship between a measure and teaching was achieved through synthesis of the relevant literature. Psychometric performance of a measure included the traditional indicators of construct validity and reliability that is; the tool was measuring what it purports to measure and doing so in a consistent manner. In addition, the validation process was extended to include item response modeling to understand if the entire range of a construct was represented and that the assessment was able to discriminate candidates with differing levels of ability. This section of the symposium will present the results of this validation process for TeacherSelector by presenting brief results on all measures and then discussing in depth two of the assessments: numerical ability (cognitive) and conscientiousness (non-cognitive). These measures demonstrated sound psychometric properties as well as a strong conceptual basis for inclusion in the tool. Subsequently, their relevance to the selection process was confirmed quantitatively in their capacity to predict preservice teachers’ course performance, the discussion of which will form the focus of the second presentation in this symposium.

Barnard, L., Lan, W., To, Y., Paton, V., Lai, S. (2009). Measuring self-regulation in online and blended learning environments. Internet and Higher Education, 12, 1-6. Beltman, S., Mansfield, C., & Price, A. (2011). Thriving not surviving: A review of research on teacher resilience. Educational Research Review, 6(3), 185-207. Costa, P. & McCrae, R. (1995). Domains and facets: Hierarchical personality assessment using the revised NEO Personality Inventory. Journal of Research in Personality, 64, 21–50. Dowell, J., Lumsden, M.A., Powise, D., Munro, D., Bore, M., Makubate, B., & Kumwenda, D. (2011). Predictive validity of the personal qualities assessment for selection of medical students in scotland. Medical Teacher, 33, e485-e488. Hair, J. F. (2006). Multivariate data analysis / Joseph F. Hair, Jr. ... [et al.]: Upper Saddle River, N.J. : Pearson Prentice Hall, 2006. 6th ed. Howard, S., & Johnson, B. (2004). Resilient teachers: Resisting stress and burnout. Social Psychology of Education, 7(4), 399–420. O’Connor, M., & Paunonen, S. (2007). Big Five personality predictors of post-secondary academic performance. Personality and Individual Differences, 43, 971–990. Panesar, S. (2010). Exploring disparities between teacher’s expectations and the realities of the education profession. Research in Higher Education Journal, 8, 1-19.