Abstract:
There are various types of factors that influence the university students’ not
completing the degree on the first attempt such as financial, health or stress,
academic/institutional, social and personal, economical, and disposition factors. This
study’s goal is to analyze the university students’ decisions to complete the degree on
the first attempt or not and to introduce a model-based approach to predict the
university students’ not completing the degree on the first attempt in terms of the
identified most influential factors, which will be useful in the implementation of more
effective individual, group-specific or institutional prevention measures. Machine
learning is used for the analysis since it has shown tremendous potential for the
interpretation of complex data sets. Five different models have been trained and the
trained models provided a comparatively better performance in predicting the
University students’ not completing the degree on the first attempt in terms of
influencing factors since all the built models gave more than 84 % accuracy. Among
them, the Naïve Bayes classifier was identified as the model with the highest of 92.75
%. An Ensemble approach was introduced and this model demonstrated an accuracy
of 93.65 % which provided the best performance in predicting the University
students’ completion of the degree on the first attempt in terms of influencing factors
considered. Further correlation coefficients which are between r = 0.03 and r = 0.7
and ß- coefficients which are between r = 0.03 and r = 0.72 were calculated among
all the variables to determine the contribution of each variable towards the University
students’ not completion of the degree on the first attempt.
