J. Pérez, E. Dapena, J. Aguilar, Gilberto Carrillo
{"title":"Reinforcement Learning for Estimating Student Proficiency in Math Word Problems","authors":"J. Pérez, E. Dapena, J. Aguilar, Gilberto Carrillo","doi":"10.1109/LACLO56648.2022.10013399","DOIUrl":null,"url":null,"abstract":"Math Word Problems (MWPs) allow preparing students to apply mathematical skills in everyday life. Teaching MWPs is challenging because the mathematics difficulties of individual students vary across students within a class. In this paper, it is proposed a reinforcement learning algorithm based on Q-learning for estimating individual student proficiency according to the basic arithmetic operations: addition, subtraction, multiplication, and division. The proposal is analyzed by simulating interactions with hand-coded users but tested by simulating interactions with data-driven users. In addition, results are compared with those in a common Cognitive Diagnosis Model (CDM). Results show that our approach allows estimating the individual student proficiency in around 5 episodes. Thus, since reinforcement learning usually is applied to induce instructional policies in tutoring systems, this paper shows that it can be used for estimating student proficiency as well.","PeriodicalId":111811,"journal":{"name":"2022 XVII Latin American Conference on Learning Technologies (LACLO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 XVII Latin American Conference on Learning Technologies (LACLO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LACLO56648.2022.10013399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Math Word Problems (MWPs) allow preparing students to apply mathematical skills in everyday life. Teaching MWPs is challenging because the mathematics difficulties of individual students vary across students within a class. In this paper, it is proposed a reinforcement learning algorithm based on Q-learning for estimating individual student proficiency according to the basic arithmetic operations: addition, subtraction, multiplication, and division. The proposal is analyzed by simulating interactions with hand-coded users but tested by simulating interactions with data-driven users. In addition, results are compared with those in a common Cognitive Diagnosis Model (CDM). Results show that our approach allows estimating the individual student proficiency in around 5 episodes. Thus, since reinforcement learning usually is applied to induce instructional policies in tutoring systems, this paper shows that it can be used for estimating student proficiency as well.