Bridging MOOC Education and Information Sciences: Empirical Studies

Abstract

In the rise of massive open online courses (MOOCs), massive data of online learning behaviors are generated, which are valuable for the research on distance education in the aspects of making courses more attractive, learning more effective, and platforms wiser. Two empirical studies, here, utilized basic technologies of information sciences to analyze the log data of viewing behavior provided by the MOOC platform iCourse, illustrating the potential values of scientometrics and network sciences in the assessment of course attractions and in the improvement of teaching quality. One study extended information entropy to describe the diminishing marginal utility of repeated viewing and the increasing information of viewing new videos and further showed the possible applications of scientometrics to course assessment by integrating the viewing time on course videos invested by learners and the number of videos viewed by learners. The other study derived a network to represent learning paths adopted by the crowds of learners, which sheds light on the applications of network navigation and link prediction to the MOOC education in the aspects of teaching optimization and learning navigation. This paper adumbrates not only the practicability but also the limitations of the provided methods.

Keywords

• Data science applications in education
• distance education and online learning
• evaluation methodologies