[Retracted] Personalized Recommendation Model of High‐Quality Education Resources for College Students Based on Data Mining

Abstract

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

Optimal Allocation of Higher Education Resources Based on Data Mining and Cloud Computing

“…The research on DW has been paid more and more attention by academic circles and government departments, and the research results of DW are obviously increasing. However, there are still a few high-quality breakthrough scientific research papers, most of which are limited to general theoretical introduction, summary and improvement and application of DW algorithms [11].…”

The Teaching Strategy of Socio-Political Education by Deep Learning Under Educational Psychology

“…The performance of the recommendation algorithm has a key impact on using educational psychology theory February 2023 | Volume 13 | Article 910677 Chen et al Social Political Education Class to provide students with adaptive teaching, so it is crucial to improve the performance of the recommendation algorithm . The development of recommendation algorithms mirrors the development of recommendation systems (Fang and Lu, 2021). The earliest recommendation algorithms were designed to bring personalized movie viewing services to movie watchers.…”

Research on personalized recommendation of MOOC resources based on ontology

“…As one of the most important learning resources in the digital age, massive open online course (MOOC) faces the problem of high participation rate and low completion rate (Dalipi et al , 2018; Seng et al , 2018). And massive MOOC resources made learners face the danger of being lost in the information at any time during the learning process (Zhan et al , 2015; Fang and Lu, 2021). More and more learners are eager for personalized and intelligent knowledge acquisition to improve the efficiency of online learning (Hui et al , 2018).…”

“…More and more learners are eager for personalized and intelligent knowledge acquisition to improve the efficiency of online learning (Hui et al , 2018). Personalized recommendation of learning resources is to meet the learning habits of learners in the digital age and to improve the quality of educational services (He et al ; Fang and Lu, 2021). With the continuous maturity of learning analysis and data mining technologies, various personalized recommendation methods have emerged (Fang and Lu, 2021).…”

“…Personalized recommendation of learning resources is to meet the learning habits of learners in the digital age and to improve the quality of educational services (He et al ; Fang and Lu, 2021). With the continuous maturity of learning analysis and data mining technologies, various personalized recommendation methods have emerged (Fang and Lu, 2021). However, the current personalized recommendation technology is still immature (Hui et al , 2018), and problems such as data scarcity (Guo and Zhao, 2013; Hui et al , 2018), cold start (Yu et al , 2018; Cheng and Bu, 2020), over-specialized recommendation (Li et al , 2007) and imprecise recommendation results (George and Lal, 2019) still exist.…”