MOOC recommendation algorithm based on learning process sequence modeling and quantitative analysis

Abstract

MOOC platform is one of the most important data sources of educational big data, so the correlation analysis of MOOC learning behavior data has become a research hotspot in educational data mining and learning analysis. The purpose of this paper is to study the MOOC recommendation algorithm based on the learning process sequence modeling and quantitative analysis. Aiming at the problem of frustration caused by dropping classes in MOOC, this study improves the recommendation feature model, and proposes an adaptive process recommendation method. Based on the data modeling of MOOC learning process and quantifying the learning status, it realizes multi-feature adaptive trade-off recommendation, and achieves Reduce the purpose of dropping out. First, the traditional recommendation characterized by interest is improved, and a new feature model is adopted to reflect the learner's satisfaction needs and reduce frustration. Secondly, the influence of various similarity distances such as time distance and knowledge distance on learning features is considered to improve the recommendation accuracy. Finally, the recommendation results are evaluated. The experimental results show that when k1 is 10, the recall of MRSS reaches 0.42, and the accuracy rate is the best.