Laura Fink and Bojan Cestnik

ABSTRACT
In this paper, we examine the academic performance of students
in different courses to determine whether good performance in
one course is related to good performance in other courses.
Although certain predictive models emphasize the importance of
course content for learning success, there are few studies that
address how student performance in different courses is related
to similar learning goals and skills. The interrelatedness of
competences is still extremely under-explored area. This study
attempts to address this gap by creating a preliminary framework
that examines how academic performance is related to specific
skills taught in courses at a higher education institution. We
examined a set of student grades from nine different courses at
the faculty from areas such as entrepreneurship, business
processes, computer technology, mathematics, economics,
marketing, innovation, English, and finance. We show that
students with more exam retakes on average reached a lower
grade rank than the students who only registered for the exam
once. We used linear regression to show the significance of the
relationships between student performance in computer
technology course compared to their achievement in other
courses. With a correlation matrix coefficient, we measured the
strength of reciprocal interrelatedness between the grade ranks
students attained in each of the nine courses. The results of this
preliminary study indicate possible stronger association between
academic achievement in courses that have similarities in terms
of content or focus, such as business administration and
entrepreneurship (correlation coefficient of 0.58). Further studies
with detailed comparison of course-specific competences are
needed for accepting the finding that interrelatedness between
achievements in courses from similar versus different disciplines
is stronger. The preliminary model could further be improved by
a broader range of courses, input explanatory student factors and
application of advanced analytical techniques.