Improve the Curricula System of MOOCs Via Data Miningape

Abstract

In recent years, Massive Open Online Courses (MOOCs) raise wide concern of the in academia. Researchers are working on making MOOCs more efficient and easier to learn. A number of works focus on describing the characters of learners via their behaviours to personalize. Unfortunately, few studies pay attention to construct the curricula system of MOOCs. While reasonable curricula system can also improve the learning efficiency remarkably. To improve the reasonability of the curricula system of MOOCs, this paper crawls all the 112920 reviews from Coursera.org (up to Jun./30/2017) for the first time, and from which we investigate the relationship between courses, learners and job markets for the purpose of discovering any helpful suggestions. The contributions of this paper include three aspects: Firstly, it discovered the topological graph of the courses through analyzing learners' reviews. Secondly, the tendency in the number of reviews per course is found for fitting power-law distribution ideally. And which perhaps means most learners only concerns very few courses of the MOOCs. Thirdly, comparing with the data from the job markets, we have some useful suggestion. In addition, the tends in the number of reviews over time are also identified. It is a key role for the time distribution of the reviews in this study. Furthermore, some effective suggestion for enhancing levels of activity in courses is presented in this paper.