Predictive Monitoring of Learning Processes

Abstract:What students do in a self-paced online
learning environment is a “black box”. The instructor
has limited interactions with students and a restricted
understanding of how students are progressing in
their studies. A technology, sophisticated enough to
predict the outcome of the student in an online
learning environment was widely adopted in
Predictive Learning Analytics. In the past, research on
predictive learning analytics has emphasized
predicting learning outcomes rather than facilitating
instructors and students in decision-making or
analyzing student behavior. This research study
employed a predictive process monitoring technique
to analyze the student’s event logs in an online
learning and online test environment to predict the
next activity the student is going to perform and the
remaining time to complete the course or test. The
Long Short Term Memory neural network approach
is used in this work to predict the next activity of the
running case by analyzing the sequence of historical
data and Apromore to predict the completion time of a
case. By employing the predictive monitoring of
learning processes, new insights are developed to
analyze students’ behavior in real-time and is
achievable.