Educational Data Mining and Learning Analytics

Since the advent of the internet, online and distance education has become the predominant mode of instructional delivery in education and training settings. Effective online learning is not solely dependent on instructional design. Factors such as student engagement, learning styles, and personal characteristics also play a significant role in determining the success of online learning. Educational data mining and learning analytics provide distance educators with insights into the students, learning patterns, and methods to support the learners. Educational Data Mining (EDM) and Learning Analytics (LA) are two closely related fields that both deal with the analysis of data in order to improve learning experiences. Romero and Ventura (2020) define EDM as the development of methods for analysing the unique types of data that are collected from learning environments. EDM is also the application of Data Mining (DM) techniques to this specific type of dataset that originates from educational environments in order to address important educational questions. EDM is concerned with the identification of patterns within educational data that may otherwise remain hidden. This is achieved through the application of statistical and machine learning techniques, which enable the identification of relationships between variables.