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An Enhanced ELO-Based Student Model for Polychotomously Scored Items in Adaptive Educational System

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Title: An Enhanced ELO-Based Student Model for Polychotomously Scored Items in Adaptive Educational System
Language: English
Authors: Bingxue Zhang, Yang Shi, Yuxing Li, Chengliang Chai, Longfeng Hou
Source: Interactive Learning Environments. 2023 31(9):5477-5494.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 18
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Electronic Learning, Models, Students, Individualized Instruction, Academic Achievement, Cognitive Ability, Test Items, Achievement Rating, Student Evaluation
DOI: 10.1080/10494820.2021.2010099
ISSN: 1049-4820
1744-5191
Abstract: The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system has greater potential as compared to the other models regarding application in the online learning environment. Based on the Elo model, this study proposes the EELO, an enhanced Elo rating system, in consideration of the application scenarios of polychotomously scored items and multi-dimensional granularity evaluations not covered by the basic Elo rating system. The EELO model estimating students' cognitive abilities and predicting their future performances on unknown questions is evaluated based on one public set (Assigment2) and one proprietary dataset (HSK), and achieved an AUC of 0.92 for Assigment2 and 0.84 for HSK, which shows that the EELO model has the best performance regarding the above-mentioned objectives as compared with the latest extensions of the IRT and BKT models. Subsequently, the EELO model was tested and applied successfully in a real large-scale online learning environment to demonstrate the potential of the EELO model in adaptive learning applications.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1403011
Database: ERIC
FullText Text:
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  Data: An Enhanced ELO-Based Student Model for Polychotomously Scored Items in Adaptive Educational System
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Bingxue+Zhang%22">Bingxue Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Yang+Shi%22">Yang Shi</searchLink><br /><searchLink fieldCode="AR" term="%22Yuxing+Li%22">Yuxing Li</searchLink><br /><searchLink fieldCode="AR" term="%22Chengliang+Chai%22">Chengliang Chai</searchLink><br /><searchLink fieldCode="AR" term="%22Longfeng+Hou%22">Longfeng Hou</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Interactive+Learning+Environments%22"><i>Interactive Learning Environments</i></searchLink>. 2023 31(9):5477-5494.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Y
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  Data: 18
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  Data: 2023
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Students%22">Students</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Instruction%22">Individualized Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Rating%22">Achievement Rating</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Evaluation%22">Student Evaluation</searchLink>
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  Data: 10.1080/10494820.2021.2010099
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  Data: 1049-4820<br />1744-5191
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system has greater potential as compared to the other models regarding application in the online learning environment. Based on the Elo model, this study proposes the EELO, an enhanced Elo rating system, in consideration of the application scenarios of polychotomously scored items and multi-dimensional granularity evaluations not covered by the basic Elo rating system. The EELO model estimating students' cognitive abilities and predicting their future performances on unknown questions is evaluated based on one public set (Assigment2) and one proprietary dataset (HSK), and achieved an AUC of 0.92 for Assigment2 and 0.84 for HSK, which shows that the EELO model has the best performance regarding the above-mentioned objectives as compared with the latest extensions of the IRT and BKT models. Subsequently, the EELO model was tested and applied successfully in a real large-scale online learning environment to demonstrate the potential of the EELO model in adaptive learning applications.
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      – Type: doi
        Value: 10.1080/10494820.2021.2010099
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 5477
    Subjects:
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Students
        Type: general
      – SubjectFull: Individualized Instruction
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Cognitive Ability
        Type: general
      – SubjectFull: Test Items
        Type: general
      – SubjectFull: Achievement Rating
        Type: general
      – SubjectFull: Student Evaluation
        Type: general
    Titles:
      – TitleFull: An Enhanced ELO-Based Student Model for Polychotomously Scored Items in Adaptive Educational System
        Type: main
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            NameFull: Bingxue Zhang
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            NameFull: Yang Shi
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            NameFull: Yuxing Li
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            NameFull: Chengliang Chai
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            NameFull: Longfeng Hou
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              M: 01
              Type: published
              Y: 2023
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              Value: 31
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            – TitleFull: Interactive Learning Environments
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