Research on Improvement Scheme of MOOC Based on Sequential Recommendation Algorithm and Big Data

With the advent of the information age, the rapid development of new-generation information technologies such as cloud computing and cloud storage has also led to changes in the education field, especially the massive open online courses (MOOC). At the same time, the society ‘s demand for talents with computer technology is increasing. The undergraduate education in the field of computer science has gradually become the focus of undergraduate education in the information age. However, even if MOOC is used as a supplement to offline education, the learning effect varies from person to person due to the differences in students ‘ learning methods and abilities. An improved MOOC model based on sequential recommendation algorithm and big data proposed in this study can provide an optimization idea for such problems. In the model testing session, this study randomly selected some undergraduates in 2020 grade majoring in computer science at the University of Electronic Science and Technology of China for comparative experiments, proving that the MOOC improvement program based on sequential recommendation algorithms and big data can effectively improve students ‘ academic performance and contribute to the promotion of educational equity.