Analytics is all about insights. Learning-oriented insights are the targets for Learning Analytics researchers. Insights could be detected, analysed, or created in the context of variables such as the quality of interactions with the content, study habits, engagement, competence growth, sentiments, learning efficiency, and instructional effectiveness. Clustering techniques offer an effective solution for grouping learners using observed patterns. For instance, learners could be clustered based on the effectiveness of learners' self-regulation initiatives in reaching the target learning outcomes. Each learner could belong to a number of clusters that target different types of insights. One could also analyse the distance between clusters as a means to guide learners towards better performance. Further, one could analyse the effectiveness of cohesive peer groups within and among clusters. Traditional clustering techniques only cope with numerical or categorical data and are not readily applicable in offering learning analytics solutions. In addressing this gap, this research aims to design a Parallel Particle Swarm Optimization (PPSO) algorithm for the purposes of learning analytics, where the arrival of data is continuous, the types of data is both structured and unstructured, and the volume of data can be significantly large. The research will also describe the application of the PPSO algorithm to detect, analyse, and generate learning-oriented insights.