Learning Content Classification and Mapping Content to Synonymous Learners based on 2022 Augmented Verb List of Marzano and Kendall Taxonomy

Abstract

Finding suitable learning content for learners with different learning styles is a challenging task in the learning process. Hence it is essential to follow some learning taxonomies to deliver learner-centric learner content. Learning taxonomies are used to express various learning practices and learning habits to be followed by the learner for a better learning process. The investigator has already classified the learners based on the 2022 augmented verb list of Marzano and Kendall taxonomy. The main objective of this paper is to minutely classify the tutor-defined learning contents according to the domains as well as the subdomains of the considered taxonomy which is in text format. Providing personalized learning content could help the learners for a better understanding of learning content and their interrelationship which in turn produce better learning outcomes. Mapping the six levels of learning contents into the corresponding learner is a challenging task. Hence the investigator has chosen seven algorithms including Bagging, XG Boost, Support Vector Machine from Machine Learning and four algorithms including Convolutional Neural Network, and Deep Neural Network in Deep Learning algorithm to classify the learning contents. The experimental results indicate that Support Vector Machine performed well in machine learning and Deep Neural Network yields good performance in deep learning in the learning content classification process. These micro contents were organized using a property graph. Further, the micro contents were retrieved from the property graph using SPARQL for mapping the classified contents to the corresponding learners to achieve personalization in the learning process.