Future Educational Technology with Big Data and Learning Analytics

In the recent years, big data and learning analytics have been emerging as fast-growing research fields. The application of these emerging research areas is gradually addressing the contemporary challenges of school and university education. Tracing out the information regarding students' misconceptions and dropping-out probabilities from the courses at the right instant of time, development of detectors of a range of educational importance and achieving the highest level of quality in the higher education are becoming more challenging. Moreover, providing well timed and the best suitable solutions to the students at-risk are even more strenuous. In this concept paper, we aim to address these contemporary challenges of school and the university education and their probable solutions by utilizing our research experiences of automated assessment, immediate feedback, learning analytics and the IT technologies. Solving such problems by knowing the history of students' activities, submissions, and the performances data is possible. The identification of students' misconceptions during the learning process, examining behavioral patterns and significant trends by efficiently aggregating and correlating the massive data, improving the state-of-the-art skills in creative thinking and innovation, and detecting the drop-outs on-time are highlighted in this article. We are aiming at extracting such knowledge so that adaptive and personalized learning will become a part of the current education system. Not only the available algorithm of supervised learning methods such as support vector machine, neural network, decision trees, discriminant analysis, and nearest neighborhood method but also new engineering and distillation of relevant data features can be carried out to solve these educational challenges.