BigData Analysis and Data Mining

Biography

Biography: Prajakta Diwanji

Abstract

In recent times, there is a steady growth in student’s personal as well as academic data in the education field. Many universities and institutes have adopted information systems like virtual learning environments, learning management systems and social networks that collect student’s digital footprints. This data is both large in volume and diversity. Learning analytics offers tools to facilitate the understanding of different parameters related to student’s engagement/motivation, learning behavior, performance, teaching content and learning environment. Such information could help teachers better prepare for the classroom sessions and to deliver personalized or adaptive learning experiences. This, in turn, could enhance student performance. The current literature research states that there is a shift of focus from classroom based learning to a more anytime, anywhere learning; as well as from a teacher as a sole knowledge contributor to agent or learner as a contributor towards learning. The use of intelligent digital tutors/chatbots has taken the learning process to a new level of student engagement, interaction, and learning. Such intelligent data analysis tools/systems make use of data analysis techniques like machine learning, natural language processing etc. along with artificial/cognitive intelligence techniques. The research work identifies the current challenges faced by universities in learning/teaching processes in a real world context and tries to address these problems using data driven intelligent analysis methods. The main goal would be to focus on preparing students as well as lecturers effectively for the classroom lectures; to understand learning needs of students beforehand; to address those needs proactively in a timely manner.