Psychometric profiling of students at risk for academic underachievement at a university of technology

Abstract

Poor results on academic performance indicators at South African institutions of higher education have significant implications for institutions, requisite graduate skills and the country s economy. Institutions are, therefore, confronted with a need to be proactive in implementing strategies to deal with underprepared students and the challenges of articulation to higher education. To enhance student success, Alan Seidman s (2005) formula for retention was used as a basis for structuring the provisioning of student support initiatives at a university of technology. The formula provided for the early identification of students at risk, and the provision of early, intensive and continuous interventions. As a component of an early warning system to identify students at risk for academic underachievement, 4718 first-year students at the institution were assessed with a battery of instruments at the beginning of the academic year. The battery comprised the English Literacy Skills Assessment, Career Choice Questionnaire, Learning and Study Strategies Inventory, and Emotional Skills Assessment Process. The results of these instruments were used to refer students for relevant interventions. The study analysed the relationship between the results obtained in the instruments and three measures of first-year academic performance, namely, retention, percentage of subjects passed and average mark. Demographic variables and intervention programmes were also included as independent variables. The sample was grouped into two categories, first-time entering students and students who were repeating the first year. Using Pearsons Chi-square tests of independence, most of the independent variables indicated a significant relationship with at least one academic performance indicator for the first-time entering students. This finding supports the use of the instruments in the risk profiling evaluation for first-time entering students. Self- Testing, Study Aids, Empathy, and Self-esteem did not have a significant relationship with any academic performance measure. Amongst the students repeating, there were much fewer variables that had significant relationships with academic performance measures. The variables predicting academic underachievement were substantially reduced when entered into stepwise logistic regression models for both first-time entering students and students who were repeating. The models and associated tables may be utilised for profiling students to identify those at risk of academic underachievement and for using the profiles for the recommendation of necessary interventions.