Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

Abstract

The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

Cluster analysis and its application in teaching resources of university curriculum: A personalized method

“…These methods cater to different situations and reduce data search time, addressing information overload and enhancing platform stickiness. Yafei et al [21] propose a personalized recommendation method for higher education resources based on multidimensional association rules, utilizing the Apriori algorithm for mining association rules. The model accurately extracts educational resources and ensures a high matching degree between learners and resources.…”

Retrieval and Recommendation of English Learning Resources Based on Knowledge Correlation

“…, ,..., 1,0,...,1 S s s s = = (13) S=0 means that there is no indirect relationship between two English learning resources, and S=1 means that there is an indirect relationship between two English learning resources. Assuming: TE s represents English learning resources directly related to the retrieval target of knowledge points; R represents the vector of correlation closeness degree values; λ represents the weight (namely the importance degree) of knowledge points in the relationship network, then the calculation formula of the closeness degree of the correlation of learning resources is given by Formula 14:…”

“…However, the existing learning resource search engines provided by current smart education platforms couldn't meet students' needs of retrieving personalized learning resources based on fuzzy learning objectives, as a result, personalized teaching and accurate application of the competency-based instructing system couldn't be realized, especially for students with large structural differences in their professional knowledge cognition level and knowledge system, more attention needs to be paid to their learning preferences and personality traits when recommending personalized learning resources for them [7][8][9][10]. In this context, it is of certain practical meaning to design personalized learning resource retrieval and recommendation methods based on students' cognition level of professional knowledge and the correlation degree of the knowledge with smart teaching assistance system as the reference framework [11][12][13][14].…”