Student Modelling on Language Teaching Based on Bayesian Networks
Acquiring a reliable student model is the principal task of an Intelligent Tutoring System (ITS). A precisely defined student model is also the key term for the success of ITSs. In this paper, a study on inferring the accurate student model on language learning is offered by utilizing artificial...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Gazi University
2021-06-01
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| Series: | Gazi Üniversitesi Fen Bilimleri Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1738645 |
| _version_ | 1797906311779713024 |
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| author | Selçuk ŞENER Ali GÜNEŞ |
| author_facet | Selçuk ŞENER Ali GÜNEŞ |
| author_sort | Selçuk ŞENER |
| collection | DOAJ |
| description | Acquiring a reliable student model is the principal task of an Intelligent Tutoring System (ITS).
A precisely defined student model is also the key term for the success of ITSs. In this paper, a
study on inferring the accurate student model on language learning is offered by utilizing
artificial intelligence. In addition to the general structure of an ITS, a probabilistic model for
inference using Bayesian Networks is stated in the paper. Bayesian Networks are acyclic
directed graphs in which nodes represent random variables and arcs represent direct
probabilistic dependences among them. In this study, graphical models and structures are
implemented in a general-purpose decision modelling system SMILE and its Windows user
interface, GeNIe, developed at the Decision Systems Laboratory. GeNIe user interface is also
used in this study to perform Influence diagrams. Influence diagrams represent decision
problems and help to choose a decision alternative with the highest expected gain. Toward the
end of the study, an ITS student model which is directly associated with a standard proficiency
level is aimed to be developed. This model is also complemented with a domain model, which
incorporates language components such as grammar, vocabulary, and functions of language in different cases. At the end of the study, an evaluation of the model is performed with an
experimental study and the results show that the participants worked with offered ITS model
gathered close results when compared to those obtained by the participants who worked with a real tutor. |
| first_indexed | 2024-04-10T10:19:56Z |
| format | Article |
| id | doaj.art-5b15a69c817b4cf0b486176541f4bd7c |
| institution | Directory Open Access Journal |
| issn | 2147-9526 |
| language | English |
| last_indexed | 2024-04-10T10:19:56Z |
| publishDate | 2021-06-01 |
| publisher | Gazi University |
| record_format | Article |
| series | Gazi Üniversitesi Fen Bilimleri Dergisi |
| spelling | doaj.art-5b15a69c817b4cf0b486176541f4bd7c2023-02-15T16:21:42ZengGazi UniversityGazi Üniversitesi Fen Bilimleri Dergisi2147-95262021-06-019234735610.29109/gujsc.929365Student Modelling on Language Teaching Based on Bayesian NetworksSelçuk ŞENERhttps://orcid.org/0000-0003-2251-5366Ali GÜNEŞhttps://orcid.org/0000-0001-6177-3136Acquiring a reliable student model is the principal task of an Intelligent Tutoring System (ITS). A precisely defined student model is also the key term for the success of ITSs. In this paper, a study on inferring the accurate student model on language learning is offered by utilizing artificial intelligence. In addition to the general structure of an ITS, a probabilistic model for inference using Bayesian Networks is stated in the paper. Bayesian Networks are acyclic directed graphs in which nodes represent random variables and arcs represent direct probabilistic dependences among them. In this study, graphical models and structures are implemented in a general-purpose decision modelling system SMILE and its Windows user interface, GeNIe, developed at the Decision Systems Laboratory. GeNIe user interface is also used in this study to perform Influence diagrams. Influence diagrams represent decision problems and help to choose a decision alternative with the highest expected gain. Toward the end of the study, an ITS student model which is directly associated with a standard proficiency level is aimed to be developed. This model is also complemented with a domain model, which incorporates language components such as grammar, vocabulary, and functions of language in different cases. At the end of the study, an evaluation of the model is performed with an experimental study and the results show that the participants worked with offered ITS model gathered close results when compared to those obtained by the participants who worked with a real tutor.https://dergipark.org.tr/tr/download/article-file/1738645bayesian networksinfluence diagramsintelligent tutoring systemsstudentmodelling |
| spellingShingle | Selçuk ŞENER Ali GÜNEŞ Student Modelling on Language Teaching Based on Bayesian Networks Gazi Üniversitesi Fen Bilimleri Dergisi bayesian networks influence diagrams intelligent tutoring systems studentmodelling |
| title | Student Modelling on Language Teaching Based on Bayesian Networks |
| title_full | Student Modelling on Language Teaching Based on Bayesian Networks |
| title_fullStr | Student Modelling on Language Teaching Based on Bayesian Networks |
| title_full_unstemmed | Student Modelling on Language Teaching Based on Bayesian Networks |
| title_short | Student Modelling on Language Teaching Based on Bayesian Networks |
| title_sort | student modelling on language teaching based on bayesian networks |
| topic | bayesian networks influence diagrams intelligent tutoring systems studentmodelling |
| url | https://dergipark.org.tr/tr/download/article-file/1738645 |
| work_keys_str_mv | AT selcuksener studentmodellingonlanguageteachingbasedonbayesiannetworks AT aligunes studentmodellingonlanguageteachingbasedonbayesiannetworks |