Research on Learner Modeling and Curriculum Recommendation Based on Emotional Factors

• Published in: Journal of sensors Vol. 2022; pp. 1 - 9
• Main Authors: Hua, Xin, Zhang, Hongchen, Xie, Feihong, Wei, Juntian, Wei, Junjia, Li, Huimin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 17.05.2022; Hindawi Limited
• Subjects: Artificial intelligence; Attitudes; Consciousness; Construction; Curricula; Data analysis; Data mining; Deep learning; Demography; Dictionaries; Emotional factors; Emotions; Factorization; Instructional design; Learning management systems; Mathematical analysis; Personalized learning; Quality of life; Recommender systems; Simulation; Spinal cord injuries; Students; Teaching; Technology assessment
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2022/3296713

With the increasing with the number of courses, learners cannot find the courses they need quickly. Therefore, the primary problem to change the efficiency of online courses is to recommend corresponding courses for a certain group of people according to their needs. Learner characteristics are an important aspect of reflecting learner preferences, and learner models are abstract representations and descriptions of learner characteristics. It is necessary to enhance the use of online courses among students; we must build a relatively comprehensive curriculum model. At present, the construction of learner model is mostly based on cognitive level and learning style, ignoring the emotion expressed by learners to the curriculum, and emotion is a very important characteristic of learners. In order to establish a perfect learner model, it is necessary to incorporate learners’ aspect emotion into the learner model to make the course recommendation process more accurate. Firstly, based on the attention mechanism long-term and short-term memory network, this paper extracts the learner’s aspect emotion to the curriculum from the learner’s curriculum review. At the same time, it studies various characteristics, such as demography, cognitive level, motor behavior, and learning style. By establishing a perfect model integrating researchers’ emotional state, finally, the complex interaction between learner characteristics and curriculum characteristics is modeled by using deep factor natural decomposition, so as to achieve accurate curriculum recommendation. In this study, the learner’s aspect emotion is included in the construction of learner model and enriched and perfected the learner model. It provides a reference for the theoretical research and applied research of learner model and has reference significance. At the same time, combining Deep learning can improve the accuracy of course recommendation, help learners’ learning efficiency and personalized learning quality, and also contribute to the long-term development of online platform. The mathematical modeling in this paper uses learning analysis technology and general factor model based on matrix factorization to calculate and uses factorization machine to reduce the dimension of high-dimensional data, which is efficient and accurate.