Data retrieval related to resource allocation, course and student performance using graph database

The emergence of the big data era is a direct result of the rapid and exponential growth in the amount of data produced daily. Given the vast amount of data, it has the potential to provide us with valuable insights if we possess the ability to interpret it. Different groups, mainly higher education institutions (HEI), are actively evaluating and exploring big data in a planned way and this profession is commonly referred to as Learning Analytics (LA). LA involves the systematic gathering, quantification, analysis, and transmission of information about learners and their surrounds to understand and improve learning and its environments. This field use data from the Learning Management System (LMS) and the Massive Open Online Course (MOOC) platform. The aim of this study is to provide a data retrieval model that relies on an ontology and using several query strategies to evaluate its effectiveness. The study employed a case study approach and involved a sample of 11 participants, who were chosen across a focus group. The interview result is crucial for identifying the essential information needed in LA pertaining to course allocation, course performance, and student performance. The interview result is necessary in determining the classes and queries for the purpose stated in this study, from a faculty management and lecturer perspective. The suggested model comprises an independent ontology called Student Performance and Course (SPC), and SPARQL queries for the data manipulation. Furthermore, the ontology can be divided into three distinct ontologies to encompass more precise subjects.