{"title":"自适应电子评估教学系统中确定问题难度的模糊推理模型","authors":"Oscar M. Salazar, D. Ovalle, F. de la Prieta","doi":"10.1109/LACLO49268.2019.00021","DOIUrl":null,"url":null,"abstract":"The e-assessment is a crucial process that allows to validate the previous state of knowledge acquired by the student, as well as to monitor his/her progress and/or validate the level of knowledge obtained by him/her at the end of the learning process. The aim of this paper is to propose a fuzzy inference model for determining the difficulty level of e-assessment questions within adaptive instructional systems, thus allowing to integrate and analyze key features of the questions properly matching with the student's cognitive profile for an appropriate question selection. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of the proposed fuzzy inference model for adaptive e-assessment instructional systems.","PeriodicalId":229069,"journal":{"name":"2019 XIV Latin American Conference on Learning Technologies (LACLO)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fuzzy Inference Model for Determining the Question Difficulty Level within Adaptive e-Assessment Instructional Systems\",\"authors\":\"Oscar M. Salazar, D. Ovalle, F. de la Prieta\",\"doi\":\"10.1109/LACLO49268.2019.00021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The e-assessment is a crucial process that allows to validate the previous state of knowledge acquired by the student, as well as to monitor his/her progress and/or validate the level of knowledge obtained by him/her at the end of the learning process. The aim of this paper is to propose a fuzzy inference model for determining the difficulty level of e-assessment questions within adaptive instructional systems, thus allowing to integrate and analyze key features of the questions properly matching with the student's cognitive profile for an appropriate question selection. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of the proposed fuzzy inference model for adaptive e-assessment instructional systems.\",\"PeriodicalId\":229069,\"journal\":{\"name\":\"2019 XIV Latin American Conference on Learning Technologies (LACLO)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 XIV Latin American Conference on Learning Technologies (LACLO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LACLO49268.2019.00021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 XIV Latin American Conference on Learning Technologies (LACLO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LACLO49268.2019.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy Inference Model for Determining the Question Difficulty Level within Adaptive e-Assessment Instructional Systems
The e-assessment is a crucial process that allows to validate the previous state of knowledge acquired by the student, as well as to monitor his/her progress and/or validate the level of knowledge obtained by him/her at the end of the learning process. The aim of this paper is to propose a fuzzy inference model for determining the difficulty level of e-assessment questions within adaptive instructional systems, thus allowing to integrate and analyze key features of the questions properly matching with the student's cognitive profile for an appropriate question selection. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of the proposed fuzzy inference model for adaptive e-assessment instructional systems.