{"title":"Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning","authors":"Anibal Flores, Luis Alfaro, Jose Herrera","doi":"10.1109/EDUNINE.2019.8875800","DOIUrl":null,"url":null,"abstract":"This paper presents a proposal model for implementing personalized e-learning. The proposal model considers the level of skills or knowledge that a student has on a particular subject; this is determined through a pretest; this aspect is very important to avoid problems as anxiety or boredom according flow theory. In addition, in an e-learning system to determine the optimal sequence of learning resources for a student, we will work in a complementary manner with two machine-learning techniques: Case Based Reasoning and Reinforcement Learning (Q-Learning). The Case Based Reasoning, will allow based on previous success cases, determine the sequence of learning resources most appropriate for the student; and if there are not very similar cases, a learning sequence will be chosen from the proposed ones by Reinforcement Learning (Q-Learning).","PeriodicalId":211092,"journal":{"name":"2019 IEEE World Conference on Engineering Education (EDUNINE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE World Conference on Engineering Education (EDUNINE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDUNINE.2019.8875800","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents a proposal model for implementing personalized e-learning. The proposal model considers the level of skills or knowledge that a student has on a particular subject; this is determined through a pretest; this aspect is very important to avoid problems as anxiety or boredom according flow theory. In addition, in an e-learning system to determine the optimal sequence of learning resources for a student, we will work in a complementary manner with two machine-learning techniques: Case Based Reasoning and Reinforcement Learning (Q-Learning). The Case Based Reasoning, will allow based on previous success cases, determine the sequence of learning resources most appropriate for the student; and if there are not very similar cases, a learning sequence will be chosen from the proposed ones by Reinforcement Learning (Q-Learning).