research-article

A Learning Ability Evaluation Method Based On Item Response Theory and Machine Learning Method

Authors:

Yan Xu,

Quanlong Li,

Huirong Dong,

Yuanlong ChenAuthors Info & Claims

ICBDE '20: Proceedings of the 2020 3rd International Conference on Big Data and Education

Pages 55 - 60

https://doi.org/10.1145/3396452.3396455

Published: 22 May 2020 Publication History

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Abstract

In recent years, online education and MOOC (Massive Open Online Courses) education have arisen. This paper proposes a learner ability modeling method based on item response theory and machine learning method. We use item response theory to calculate intermediate parameters and use machine learning to predict learners' learning ability after learning a new course. At the same time, this paper also presents a solution to the cold start problem. Compared to the traditional machine learning prediction method, the models of this paper have certain advantages for the learning ability prediction method.

References

[1]

Handbook of modern item response theory[M]. Springer Science & Business Media, 2013.

Google Scholar

[2]

Hambleton R K, Jones R W. Comparison of Classical Test Theory and Item Response Theory and their applications to test development ITEMS[J]. Madison, WI: National Council on Measurement in Education, 1993.

Google Scholar

[3]

van der Linden W J. Constrained adaptive testing with shadow tests[M]//Elements of adaptive testing. Springer, New York, NY, 2009: 31--55.

Google Scholar

[4]

Zhang S, Chang H H. From smart testing to smart learning: how testing technology can assist the new generation of education[J]. International Journal of Smart Technology and Learning, 2016, 1(1): 67--92.

Crossref

Google Scholar

[5]

Kocev D, Vens C, Struyf J, et al. Tree ensembles for predicting structured outputs[J]. Pattern Recognition, 2013, 46(3): 817--833.

Digital Library

Google Scholar

[6]

Kotsiantis S B. Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades[J]. Artificial Intelligence Review, 2012, 37(4): 331--344.

Digital Library

Google Scholar

[7]

Kai S, Almeda M V, Baker R S, et al. Decision tree modeling of wheel-spinning and productive persistence in Skill Builders[J]. JEDM| Journal of Educational Data Mining, 2018, 10(1): 36--71.

Google Scholar

[8]

Rovira S, Puertas E, Igual L. Data-driven system to predict academic grades and dropout[J]. PLoS one, 2017, 12(2): e0171207.

Google Scholar

[9]

Lykourentzou I, Giannoukos I, Nikolopoulos V, et al. Dropout prediction in e-learning courses through the combination of machine learning techniques[J]. Computers & Education, 2009, 53(3): 950--965.

Digital Library

Google Scholar

[10]

Vie J J, Popineau F, Bruillard É, et al. Automated Test Assembly for Handling Learner Cold-Start in Large-Scale Assessments[J]. International Journal of Artificial Intelligence in Education, 2018, 28(4): 616--631.

Crossref

Google Scholar

[11]

Park J Y, Joo S H, Cornillie F, et al. An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments[J]. Behavior research methods, 2019, 51(2): 895--909.

Google Scholar

Cited By

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He RXu Y(2023)Overfitting Identification in Machine Learning Models with the Person-Fit Indicator2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC)10.1109/ICCEIC60201.2023.10426639(520-524)Online publication date: 20-Oct-2023
https://doi.org/10.1109/ICCEIC60201.2023.10426639
Coban O(2022)A new modification and application of item response theory‐based feature selection for different machine learning tasksConcurrency and Computation: Practice and Experience10.1002/cpe.728234:26Online publication date: 15-Aug-2022
https://doi.org/10.1002/cpe.7282

Index Terms

A Learning Ability Evaluation Method Based On Item Response Theory and Machine Learning Method
1. Applied computing
  1. Education
    1. E-learning

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Published In

ICBDE '20: Proceedings of the 2020 3rd International Conference on Big Data and Education

April 2020

85 pages

ISBN:9781450374989

DOI:10.1145/3396452

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Sunderland, UK: University of Sunderland, UK
City University of Hong Kong: City University of Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2020

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ICBDE '20

ICBDE '20: 2020 The 3rd International Conference on Big Data and Education

April 1 - 3, 2020

London, United Kingdom

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Cited By

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He RXu Y(2023)Overfitting Identification in Machine Learning Models with the Person-Fit Indicator2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC)10.1109/ICCEIC60201.2023.10426639(520-524)Online publication date: 20-Oct-2023
https://doi.org/10.1109/ICCEIC60201.2023.10426639
Coban O(2022)A new modification and application of item response theory‐based feature selection for different machine learning tasksConcurrency and Computation: Practice and Experience10.1002/cpe.728234:26Online publication date: 15-Aug-2022
https://doi.org/10.1002/cpe.7282

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Recommendations

Systematic Review of Context-Aware Systems that use Item Response Theory in Learning Environments

Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems

Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle