Abstract:
With flexible learning, students are actively engaged in their own education and are held to
high standards of performance. Online academic courses make it easier for students to receive
personalized education because they provide students with more flexibility to concentrate on
what is most important to them and give them greater control over their own education. This
study’s objective was to investigate whether there is a correlation between how well students
succeed in online classes and the extent to which they make use of the schedule and the
geographical and resource flexibility offered by such programmes. This article uses a
developing approach for predicting and classifying the flexibility in online learning of
students who are at risk of failing due to academic and demographic variables. The K-nearest
neighbors (KNN) method, the random forest (RF) method, and the logistic regression method
were used to categorise the students participating in flexible online learning. The information
for the dataset came from Kaggle, and it was gathered for use in testing machine learning. The
dataset had a total of 1,875 instances representing 11 different features. Also, accuracy,
precision, sensitivity and f-score metrics were applied to evaluate the system. The results
show that the RF algorithm has a high accuracy percentage of 85%. The empirical findings
demonstrate that students formed distinct patterns of learning time, location and access to
knowledge. This suggests that flexibility was used to a significant degree. Patterns in learning
time and the availability of learning materials were shown to have a substantial relationship
with the accomplishments of the students. Gaining an understanding of the various patterns
of flexibility utilisation has the potential to promote the development of tailored learning and
to improve cooperation among students who share comparable traits.