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Intelligent tutoring system model based on fuzzy logic and constraint-based student model
A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modele...
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Published in: | Neural computing & applications 2019-08, Vol.31 (8), p.3619-3628 |
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description | A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile. |
doi_str_mv | 10.1007/s00521-017-3311-2 |
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The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-f08d364ae797771f4f1a6a8f358c2c709fccc43c91361a08bbcac99d23e77cc73</citedby><cites>FETCH-LOGICAL-c316t-f08d364ae797771f4f1a6a8f358c2c709fccc43c91361a08bbcac99d23e77cc73</cites><orcidid>0000-0002-2430-1372</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Karaci, Abdulkadir</creatorcontrib><title>Intelligent tutoring system model based on fuzzy logic and constraint-based student model</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile.</description><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Constraint modelling</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Errors</subject><subject>Feedback</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Image Processing and Computer Vision</subject><subject>Learning</subject><subject>Original Article</subject><subject>Probability and Statistics in Computer Science</subject><subject>Questions</subject><subject>Students</subject><subject>Tutoring</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kD1PwzAQhi0EEqXwA9gsMRvOdhLHI6r4qFSJBQYmy3HsKlVqF9sZ2l9PQpCYuOWGe5_3pAehWwr3FEA8JICSUQJUEM4pJewMLWjBOeFQ1udoAbIYr1XBL9FVSjsAKKq6XKDPtc-277ut9RnnIYfY-S1Ox5TtHu9Da3vc6GRbHDx2w-l0xH3YdgZr32ITfMpRdz6TOZPy0E49P9w1unC6T_bmdy_Rx_PT--qVbN5e1qvHDTGcVpk4qFteFdoKKYSgrnBUV7p2vKwNMwKkM8YU3EjKK6qhbhqjjZQt41YIYwRforu59xDD12BTVrswRD--VIzV5TiSsTFF55SJIaVonTrEbq_jUVFQk0E1G1SjQTUZVBPDZiYdJis2_jX_D30DneF0lw</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Karaci, Abdulkadir</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-2430-1372</orcidid></search><sort><creationdate>20190801</creationdate><title>Intelligent tutoring system model based on fuzzy logic and constraint-based student model</title><author>Karaci, Abdulkadir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-f08d364ae797771f4f1a6a8f358c2c709fccc43c91361a08bbcac99d23e77cc73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Constraint modelling</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Errors</topic><topic>Feedback</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Image Processing and Computer Vision</topic><topic>Learning</topic><topic>Original Article</topic><topic>Probability and Statistics in Computer Science</topic><topic>Questions</topic><topic>Students</topic><topic>Tutoring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karaci, Abdulkadir</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>test</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karaci, Abdulkadir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent tutoring system model based on fuzzy logic and constraint-based student model</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2019-08-01</date><risdate>2019</risdate><volume>31</volume><issue>8</issue><spage>3619</spage><epage>3628</epage><pages>3619-3628</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-017-3311-2</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-2430-1372</orcidid></addata></record> |
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subjects | Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Constraint modelling Data Mining and Knowledge Discovery Errors Feedback Fuzzy logic Fuzzy systems Image Processing and Computer Vision Learning Original Article Probability and Statistics in Computer Science Questions Students Tutoring |
title | Intelligent tutoring system model based on fuzzy logic and constraint-based student model |
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