Student’s Performance Prediction Using Hybrid Optimization Algorithm-based Map Reduce Framework

Learning analytics (LA) is a growing research area, which aims at selecting, analyzing and reporting student data (in their interaction with the online learning environment), finding patterns in student behaviour, displaying relevant information in suggestive formats; the end goal is the prediction of student performance, the optimization of the educational platform and the implementation of personalized interventions. According to the Society of Learning Analytics Research1, LA can be defined as "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs". The topic is highly interdisciplinary, including machine learning techniques, educational data mining, statistical analysis, social network analysis, natural language processing, but also knowledge from learning sciences, pedagogy and sociology; up-to-date overviews of the area are provided in. Various educational tasks can be supported by learning analytics, as identified in analysis and visualization of data; providing feedback for supporting instructors; providing recommendations for students; predicting student's performance; student modelling; detecting undesirable student behaviours; grouping students; social network analysis; developing concept maps; constructing courseware; planning and scheduling. Similarly, seven main objectives of learning analytics are summarized in: monitoring and analysis; prediction and intervention; tutoring and mentoring; assessment and feedback; adaptation; personalization and recommendation; reflection.