Abstract
With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.
Highlights
With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs
In the emotional factors, the effect of traditional recommendation is limited. is paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. e experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation
Looking at the whole picture, Deep Factorization Machines (DeepFM) based on Model 1 recommends the smallest root mean square error (RMSE) value, the second is the DeepFM recommendation based on Model 3, the third is the FM recommendation based on Model 1, and the last is the DeepFM recommendation based on Model 2, which has the lowest recommendation accuracy
Summary
Current learner models mainly include the following categories: (1) coverage model: this model only describes students’ cognitive state but lacks the description of learners’ learning behavior and learning emotion; (2) bias model: this model builds a database of learners’ knowledge processing errors to improve learners’ performance in the test; (3) cognitive model: this model makes up for the deficiency of the first two models. It focuses on the cognitive state of learners and on the study of learners’ emotions, attitudes, and learning styles It has been widely used in the field of personalized learning, but the lack of learners’ emotion has always been an urgent problem to be solved in the cognitive model. Other pieces of related information, such as learners’ emotions and learning behaviors, need to be processed more intensively before building learner models. Where D1, D2, D3, D4, D5, D6, respectively, represent six learning styles and S1, S2, S3, S4, S5, S6, respectively, represent the values of learners in the six types. e maximum value is selected as the corresponding learning style of learners and will not be changed after that
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