Adaptive learning path recommendation model for examination-oriented education

作     者：Wang Jian Qiao Kuoyuan Yuan Yanlei Liu Xiaole Yang Jian Wang Jian;Qiao Kuoyuan;Yuan Yanlei;Liu Xiaole;Yang Jian

作者机构：School of Computer and Artificial IntelligenceZhengzhou UniversityZhengzhou 450001China Research and Development DepartmentTongfang Knowledge Network Technology Company LimitedBeijing 100192China The Teaching Affaires OfficeZhengzhou Hi-Tech Zone Langyuehui Foreign Language Middle SchoolZhengzhou 450001China Yunnan Key Laboratory of Smart City in Cyberspace SecurityYuxi Normal UniversityYuxi 653100China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2022年第29卷第4期

页      面：77-88页

核心收录：

学科分类：0401[教育学-教育学] 04[教育学] 08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 040110[教育学-教育技术学（可授教育学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61972133) Plan for“1125”Innovation Leading Talent of Zhengzhou City(2019) the Opening Foundation of Yunnan Key Laboratory of Smart City in Cyberspace Security(202105AG070010) 

主　　题：adaptive learning learning path recommendation Dempster-Shaferevidence theory knowledge model learner model 

摘      要：Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.