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Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning

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Title: Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning
Language: English
Authors: Boxuan Ma (ORCID 0000-0002-1566-880X), Sora Fukui, Yuji Ando, Shinichi Konomi
Source: Journal of Educational Data Mining. 2024 16(1):303-329.
Availability: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Peer Reviewed: Y
Page Count: 27
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Language Proficiency, Brain Hemisphere Functions, Language Processing, Task Analysis, Second Language Learning, Second Language Instruction, Evaluation Methods, Language Tests, Computer Software, Guidelines, Language Skills, Concept Formation, Models, Diagnostic Tests, Item Response Theory, Computational Linguistics, Item Analysis, Learning Analytics, Semantics
ISSN: 2157-2100
Abstract: Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1430513
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Boxuan+Ma%22">Boxuan Ma</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1566-880X">0000-0002-1566-880X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sora+Fukui%22">Sora Fukui</searchLink><br /><searchLink fieldCode="AR" term="%22Yuji+Ando%22">Yuji Ando</searchLink><br /><searchLink fieldCode="AR" term="%22Shinichi+Konomi%22">Shinichi Konomi</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Data+Mining%22"><i>Journal of Educational Data Mining</i></searchLink>. 2024 16(1):303-329.
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  Data: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
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  Data: 27
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  Data: <searchLink fieldCode="DE" term="%22Language+Proficiency%22">Language Proficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+Hemisphere+Functions%22">Brain Hemisphere Functions</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Processing%22">Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Tests%22">Language Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Guidelines%22">Guidelines</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Skills%22">Language Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+Formation%22">Concept Formation</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+Tests%22">Diagnostic Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Analysis%22">Item Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink>
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  Data: 2157-2100
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  Data: Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.
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  Data: EJ1430513
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 303
    Subjects:
      – SubjectFull: Language Proficiency
        Type: general
      – SubjectFull: Brain Hemisphere Functions
        Type: general
      – SubjectFull: Language Processing
        Type: general
      – SubjectFull: Task Analysis
        Type: general
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Second Language Instruction
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Language Tests
        Type: general
      – SubjectFull: Computer Software
        Type: general
      – SubjectFull: Guidelines
        Type: general
      – SubjectFull: Language Skills
        Type: general
      – SubjectFull: Concept Formation
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Diagnostic Tests
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: Item Analysis
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Semantics
        Type: general
    Titles:
      – TitleFull: Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning
        Type: main
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            NameFull: Boxuan Ma
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            NameFull: Sora Fukui
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            NameFull: Yuji Ando
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            NameFull: Shinichi Konomi
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              Type: published
              Y: 2024
          Identifiers:
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              Value: 2157-2100
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            – TitleFull: Journal of Educational Data Mining
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