Success in teacher training

Teachers have a significant influence on the quality of teaching and the learning success of pupils. Aptitude tests are used to ensure that only people who have the necessary skills and (personality) characteristics to be successful in this profession enrol on a teacher training course (and subsequently enter the teaching profession).

The aptitude test TESAT (Teacher Student Assessment Austria; Neubauer et al., 2017), which is used in several Austrian federal states, was developed in the Differential Psychology department and is continuously evaluated and further developed. The TESAT consists of various performance tests and self-assessment procedures and measures, for example, language skills, intelligence, emotion regulation and the Big Five. In the (further) development of the TESAT, particular attention is being paid to using test formats other than the classic rating scale tests in order to counteract potential falsification tendencies, among other things.

Against the background of an (ongoing) evaluation of the TESAT, we are researching the prediction of different criteria of academic and professional success in the teaching profession. For example, in longitudinal studies - also using machine learning (AI) - we are investigating which cognitive and non-cognitive characteristics can best predict performance and satisfaction during studies as well as dropout.To maximize prediction accuracy and validate the procedure, machine learning (AI) methods are used that allow large amounts of data to be analyzed and complex relationships to be identified.