| dc.description.abstract | This paper presents the work done to support
student dropout risk prevention in a real online e-learning
environment: A Spanish distance university with thousands of
undergraduate students. The main goal is to prevent students from
abandoning the university by means of retention actions focused
on the most at-risk students, trying to maximize the effectiveness
of institutional efforts in this direction. With this purpose, we
generated predictive models based on the C5.0 algorithm using
data from more than 11,000 students collected along five years.
Then we developed SPA, an early warning system that uses these
models to generate static early dropout-risk predictions and
dynamic periodically updated ones. It also supports the recording
of the resulting retention-oriented interventions for further
analysis. SPA is in production since 2017 and is currently in its
fourth semester of continuous use. It has calculated more than
117,000 risk scores to predict the dropout risk of more than 5,700
students. About 13,000 retention actions have been recorded. The
white-box predictive models used in production provided
reasonably good results, very close to those obtained in the
laboratory. On the way from research to production, we faced
several challenges that needed to be effectively addressed in order
to be successful. In this paper, we share the challenges faced and
the lessons learnt during this process. We hope this helps those
who wish to cross the road from predictive modelling with
potential value to the exploitation of complete dropout prevention
systems that provide sustained value in real production scenarios | es |
