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.
