Personalized Employee Training Based on Learning Styles Using Unsupervised Machine Learning

Abstract

Advancement in technology, artificial intelligence, and machine learning have resulted in an explosion in the creation of tech-enabled training solutions over the past decade, contributing to the popularity of personalized learning. This research advocates for a transition away from archaic, rote learning paradigms and toward individualized employee learning experiences in which instructional styles and training tactics are tailored to the requirements of each individual employee rather than standard lesson preparation that exist today. This technique can also foster enjoyable and engaging training environments that benefit both employees and the organizations. We applied unsupervised machine learning algorithm, namely, K-means, and Hierarchical clustering algorithms to classify 1000 employees into different clusters based on the Felder-Silverman Learning Styles Model (FSLSM). As expected, no one of the employees could be precisely classified into a single category, and they demonstrated a variety of learning methods and tactics. The experiments showed 3 significant clusters across the different pairs of Processing, Input, Understanding, and Perception dimensions of the FSLSM. The findings suggest that employees can be grouped into at least 3 clusters to create personalized training materials and approaches for each group. We also discussed suitable instruction techniques, contents, and paths for each cluster. The proposed model and the findings would work in both digital and offline settings.