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Big Data have demonstrated significant values in extension of our insight and foresight into the world. With the rapid development of communication technologies and mobile devices, educational data have been generated at an unprecedented pace. The emerging highly flexible and scalable approaches to data processing and analysis allow us to extract new insights and meaningful information from educational data that can benefit students, teachers and the whole education ecosystem. This paper introduces some new opportunities for Big Data analytics to improve the efficiency and effectiveness of students' learning and maximise their knowledge retention. First, we propose to use supervised learning algorithms, i.e. classification or regression, to try to predict student academic performance and thereby give an an early feedback for the expected achievements, both, during the course and before the course selection process. Second, we propose to use these predictions to guide the modules, courses and content recommendation that maximizes students' potential reflected in their learning abilities, areas of interest, goals of education and career. Third, we propose to focus on the mechanics of the students' learning process and try to identify the optimal format, style, pace and organisation of the knowledge acquisition process that would lead to measurable improvements in the attained academic performance and knowledge retention in the long run. Finally, we take the introduced learning optimisation approaches together and try to formulate flexible delivery via personalization of the individual students' journeys through the educational curriculum that leave them satisfied with more knowledge delivered quicker and retained longer.
Cen et al. (Wed,) studied this question.