Personalization of Learning Objects according to the Skill Set of the Learner using Knowledge Graph

Abstract

There are numerous eLearning tools have been developed to provide the learning objects for the learners to grow in knowledge in any discipline. There is an increasing need and the demand for educational applications which provides learning object based on the ability of the learners to acquire the learning content according to their ability. Even though there were quite a lot of factors like motivation, geographic location and prerequisite knowledge that influences the learning ability, the knowledge representation for different types of learners is an important factor. Hence a system called Knowledge Graph for Online Learner (KGOL) is proposed to create a knowledge graph for the learning objects and enable the learners to understand the learning concepts better. The system utilizes heterogeneous pedagogical data from the education domain to provide personalized learning content in an eLearning environment. The learner is categorized by their learning ability and the system identifies the relationship between the concepts and pulls out the concepts in the learning objects. Specifically, it adopts the information extraction technique called Named Entity Recognition (NER) which uses spaCy, which is an open-source library for advanced Natural Language Processing in Python. The proposed system also uses student skill set from learning activity to find out the learning ability level to peruse the course content based on the classed such as highly skilled, moderate and slow learners. The proposed system demonstrates the architecture with the knowledge graph constructed for the Programming language to different types of learners. The researchers have demonstrated the proposed work with the python language. For learners’ classification, a machine learning algorithm called random forest has been used; NER and spaCy library have been used to extract the information and to construct KG for learning object on python programming language. There are three predicate levels used to provide the learning object using KG to different learners based on their learning ability like highly skilled, moderate and slow learner.