Comparison of Various Techniques Implemented for Educational Data Mining and Learning Analytics

Learning Analytics is closely related to EDM and encompasses activities such as measuring, collecting, analyzing, and reporting data about learners and their learning environments. The goal is to enhance our understanding of learning processes and optimize both learning and educational settings. Therefore, when ‘LA and EDM’ are mentioned together, it generally implies the convergence or collaboration between Learning Analytics and Educational Data Mining. This collaboration aims to leverage data-driven methodologies to enhance education and improve learning outcomes. These two domains often collaborate to examine educational data, extracting valuable insights regarding student performance and the efficacy of teaching methods. The author explores a range of techniques aimed at enhancing the learning environment and its outcomes. The paper's main objective is to compare and contrast various techniques implemented for Educational Data Mining and Learning Analytic approaches, with an aim to find out probable pros and cons if any; related to the proposed techniques.