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Tracking with (Un)certainty

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Title: Tracking with (Un)certainty
Authors: Hofman, Abe D. (ORCID 0000-0003-4269-5296), Brinkhuis, Matthieu J. S. (ORCID 0000-0003-1054-6683), Bolsinova, Maria, Klaiber, Jonathan, Maris, Gunter, van der Maas, Han L. J.
Source: Journal of Intelligence. 2020 8.
Availability: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
Peer Reviewed: Y
Page Count: 15
Publication Date: 2020
Document Type: Journal Articles
Reports - Research
Descriptors: Teaching Methods, Computer Assisted Instruction, Difficulty Level, Individualized Instruction, Learning Analytics, Integrated Learning Systems, Error Patterns, Responses, Statistical Inference, Cognitive Ability, Testing, Comparative Analysis, Competition, Educational Games, Computer Games, Probability, Visual Aids, Item Response Theory, Mathematics Instruction
ISSN: 2079-3200
Abstract: One of the highest ambitions in educational technology is the move towards personalized learning. To this end, computerized adaptive learning (CAL) systems are developed. A popular method to track the development of student ability and item difficulty, in CAL systems, is the Elo Rating System (ERS). The ERS allows for dynamic model parameters by updating key parameters after every response. However, drawbacks of the ERS are that it does not provide standard errors and that it results in rating variance inflation. We identify three statistical issues responsible for both of these drawbacks. To solve these issues we introduce a new tracking system based on urns, where every person and item is represented by an urn filled with a combination of green and red marbles. Urns are updated, by an exchange of marbles after each response, such that the proportions of green marbles represent estimates of person ability or item difficulty. A main advantage of this approach is that the standard errors are known, hence the method allows for statistical inference, such as testing for learning effects. We highlight features of the Urnings algorithm and compare it to the popular ERS in a simulation study and in an empirical data example from a large-scale CAL application.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1322594
Database: ERIC
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  Data: Tracking with (Un)certainty
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  Data: <searchLink fieldCode="AR" term="%22Hofman%2C+Abe+D%2E%22">Hofman, Abe D.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4269-5296">0000-0003-4269-5296</externalLink>)<br /><searchLink fieldCode="AR" term="%22Brinkhuis%2C+Matthieu+J%2E+S%2E%22">Brinkhuis, Matthieu J. S.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1054-6683">0000-0003-1054-6683</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bolsinova%2C+Maria%22">Bolsinova, Maria</searchLink><br /><searchLink fieldCode="AR" term="%22Klaiber%2C+Jonathan%22">Klaiber, Jonathan</searchLink><br /><searchLink fieldCode="AR" term="%22Maris%2C+Gunter%22">Maris, Gunter</searchLink><br /><searchLink fieldCode="AR" term="%22van+der+Maas%2C+Han+L%2E+J%2E%22">van der Maas, Han L. J.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Intelligence%22"><i>Journal of Intelligence</i></searchLink>. 2020 8.
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  Data: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
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  Data: 15
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  Data: <searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Instruction%22">Computer Assisted Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Instruction%22">Individualized Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+Learning+Systems%22">Integrated Learning Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Patterns%22">Error Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Responses%22">Responses</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Inference%22">Statistical Inference</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Testing%22">Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Competition%22">Competition</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Games%22">Educational Games</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Games%22">Computer Games</searchLink><br /><searchLink fieldCode="DE" term="%22Probability%22">Probability</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Aids%22">Visual Aids</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Instruction%22">Mathematics Instruction</searchLink>
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  Data: 2079-3200
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  Data: One of the highest ambitions in educational technology is the move towards personalized learning. To this end, computerized adaptive learning (CAL) systems are developed. A popular method to track the development of student ability and item difficulty, in CAL systems, is the Elo Rating System (ERS). The ERS allows for dynamic model parameters by updating key parameters after every response. However, drawbacks of the ERS are that it does not provide standard errors and that it results in rating variance inflation. We identify three statistical issues responsible for both of these drawbacks. To solve these issues we introduce a new tracking system based on urns, where every person and item is represented by an urn filled with a combination of green and red marbles. Urns are updated, by an exchange of marbles after each response, such that the proportions of green marbles represent estimates of person ability or item difficulty. A main advantage of this approach is that the standard errors are known, hence the method allows for statistical inference, such as testing for learning effects. We highlight features of the Urnings algorithm and compare it to the popular ERS in a simulation study and in an empirical data example from a large-scale CAL application.
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  Data: 2022
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  Data: EJ1322594
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      Pagination:
        PageCount: 15
    Subjects:
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Computer Assisted Instruction
        Type: general
      – SubjectFull: Difficulty Level
        Type: general
      – SubjectFull: Individualized Instruction
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Integrated Learning Systems
        Type: general
      – SubjectFull: Error Patterns
        Type: general
      – SubjectFull: Responses
        Type: general
      – SubjectFull: Statistical Inference
        Type: general
      – SubjectFull: Cognitive Ability
        Type: general
      – SubjectFull: Testing
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Competition
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      – SubjectFull: Educational Games
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      – SubjectFull: Computer Games
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      – SubjectFull: Probability
        Type: general
      – SubjectFull: Visual Aids
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Mathematics Instruction
        Type: general
    Titles:
      – TitleFull: Tracking with (Un)certainty
        Type: main
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              Type: published
              Y: 2020
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              Value: 2079-3200
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              Value: 8
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