ListMLE-based personalized teaching resources recommendation for online-to-offline hybrid course under smart education environment

Smart education is a product of the information-based education development, which enhances the intelligence of traditional education and realizes innovative education. According to the personalized learning characteristics of students, with a student-centered approach and relying on Online-to-offline hybrid courses, we have designed a new architecture for recommending educational resources to improve learning effectiveness and promote effective teaching by tapping into students' potential. The use of sequential learning technology for teaching resource recommendation is a popular research direction in intelligent education, and its core is the recommendation algorithm of personalized resources. In order to solve the problem of insufficient location information and low accuracy in the result table based on sorting learning, a recommendation algorithm of interest points based on ListMLE is proposed. Firstly, the ListMLE algorithm is applied to interest point recommendation based on the attention difference of interest point location in the recommendation list. Secondly, the influence of users' social relations is incorporated into the scoring function of ListMLE. Finally, a cost-sensitive method is introduced in the recommendation list calculation process. This paper proposes an online education resource recommendation method for personalized learning. Experimental results show that the algorithm outperforms the baseline ranking learning algorithm in terms of accuracy and recall. The method can be used to study students' learning behaviors and provide a theoretical basis for designing personalized learning programs based on students' learning status.