Enhancing personalized exercise recommendation with student and exercise portraits  

作　　者：Wei-Wei Gao Hui-Fang Ma Yan Zhao Jing Wang Quan-Hong Tian 

机构地区：[1]College of Computer Science and Engineering,Northwest Normal University,Lanzhou,730070,China [2]Computer Center of Gansu Province,Lanzhou,730070,China

出　　处：《Journal of Electronic Science and Technology》2024年第2期91-109,共19页电子科技学刊（英文版）

基　　金：supported by the Industrial Support Project of Gansu Colleges under Grant No.2022CYZC-11;Gansu Natural Science Foundation Project under Grant No.21JR7RA114;National Natural Science Foundation of China under Grants No.622760736,No.1762078,and No.61363058;Northwest Normal University Teachers Research Capacity Promotion Plan under Grant No.NWNU-LKQN2019-2.

摘　　要：The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.

关 键 词：Educational data mining Exercise recommend Joint random walk Nearly uncoupled Markov chains Optimization Personalized learning 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]