{"title":"交互式重建学生建模:一种机器学习方法","authors":"A. Mitrovic, S. Djordjevic-Kajan","doi":"10.1080/10447319509526132","DOIUrl":null,"url":null,"abstract":"Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved. This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about t...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Interactive reconstructive student modeling: A machine-learning approach\",\"authors\":\"A. Mitrovic, S. Djordjevic-Kajan\",\"doi\":\"10.1080/10447319509526132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved. This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about t...\",\"PeriodicalId\":208962,\"journal\":{\"name\":\"Int. J. Hum. Comput. Interact.\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Hum. Comput. Interact.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10447319509526132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Hum. Comput. Interact.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10447319509526132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interactive reconstructive student modeling: A machine-learning approach
Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved. This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about t...