  Traditional learning systems have responded quickly to the COVID pandemic and
moved to online or distance learning. Online learning requires a
personalization method because the interaction between learners and instructors
is minimal, and learners have a specific learning method that works best for
them. One of the personalization methods is detecting the learners' learning
style. To detect learning styles, several works have been proposed using
classification techniques. However, the current detection models become
ineffective when learners have no dominant style or a mix of learning styles.
Thus, the objective of this study is twofold. Firstly, constructing a
prediction model based on regression analysis provides a probabilistic approach
for inferring the preferred learning style. Secondly, comparing regression
models and classification models for detecting learning style. To ground our
conceptual model, a set of machine learning algorithms have been implemented
based on a dataset collected from a sample of 72 students using visual,
auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire.
Results show that regression techniques are more accurate and representative
for real-world scenarios than classification algorithms, where students might
have multiple learning styles but with different probabilities. We believe that
this research will help educational institutes to engage learning styles in the
teaching process.
