Item response theory with fuzzy markup language for parameter estimation and validation

Wang, Mei-Hui; Wang, Chi-Shiang; Lee, Chang-Shing; Teytaud, Olivier; Liu, Jialin; Lin, Su-Wei; Hung, Pi‐Hsia

doi:10.1109/fuzz-ieee.2015.7337884

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…After defining an items' parameters, we adopt the maximum a posteriori estimation approach to estimate a student's ability 𝜃 ̂ based on their response pattern u to items 1, 2, 3, …, and |I| [20]. By contrast, when the items parameters are unknown, the IRTBE mechanism can estimate them according to the examinees' abilities [29]. The inputs of IRTBE are items 1, 2, 3, ..., and |I| and the response pattern u = (u1, u2, …, u|I|) for a student.…”

Section: B Test Information Test Standard Error and Irt-based Bayesia...mentioning

confidence: 99%

“…We define the possibility of correctly answering the item as the output fuzzy variable Correct_Response_Possibility (CRP). Next, we briefly describe how to construct the knowledge base and rule base of the FML-based dynamic assessment mechanism [29].…”

Section: B Fml-based Dynamic Assessment Mechanismmentioning

confidence: 99%

“…Reference [29] shows the values of minimum, maximum, average, STD1, STD2, and STD3 of parameters a, b, and c for the fourth-grade math items. 2) Input fuzzy variable Ability: The National Assessment of Educational Progress in the United States [33] uses four levels to define student performance, namely below basic, basic, proficient, and advanced.…”

Section: ) Input Fuzzy Variables Discrimination Difficulty Andmentioning

confidence: 99%

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PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

Lee

Wang

et al. 2018

IEEE Trans. Fuzzy Syst.

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Fuzzy relationships exist between students' learning performance with various abilities and a test item. However, the challenges in implementing adaptive assessment agents are obtaining sufficient items, efficient and accurate computerized estimation, and a substantial feedback agent. Additionally, the agent must immediately estimate students' ability item by item, which places a considerable burden on the server, especially for a group test. Hence, the implementation of adaptive assessment agent is more difficult in practice. This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students' learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory (IRT)-based three-parameter logistic (3PL) model. First, we apply a Gauss-Seidel (GS)-based parameter estimation mechanism to estimate the items' parameters according to the response data, and then to compare its results with those of an IRTbased Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PSO-based FML (PFML) learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications. Index Terms-Dynamic assessment, Fuzzy Markup Language (FML), Genetic FML (GFML), item response theory (IRT), particle swarm optimization (PSO)The authors would like to thank the financial support sponsored by 1) Ministry

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Section: B Test Information Test Standard Error and Irt-based Bayesia...mentioning

confidence: 99%

Section: B Fml-based Dynamic Assessment Mechanismmentioning

confidence: 99%

Section: ) Input Fuzzy Variables Discrimination Difficulty Andmentioning

confidence: 99%

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PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

Lee

Wang

et al. 2018

IEEE Trans. Fuzzy Syst.

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“…Different studies focused on the field of assessment have suggested promoting greater efforts and strategies that demonstrate the effectiveness of learning in continuous assessment/formative assessment processes supported by technology (7)(8)(9)(10)(11). The literature contains proposals in specific domains (12)(13)(14)(15)(16). These do not consider contextual entities that allow capturing the characteristics or situations of each student, limiting possible adjustments or improvements to the assessment conditions and educational practices established by the teacher.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Hacia un Modelo de evaluación formativa en Sistemas de Aprendizaje Móvil basados en contexto

Muñoz Velasco,

Gonzáles Serrano

2024

inycomp

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La evaluación formativa contribuye en la mejora del proceso educativo, ya que recopila, interpreta y usa información de forma sistemática, con el fin de verificar y proveer retroalimentación sobre el logro de los objetivos de aprendizaje. La retroalimentación, es un componente fundamental durante la evaluación formativa, ya que, a través de ella, es posible recibir información inmediata y personalizada, que permite conocer de manera oportuna los avances en términos del conocimiento adquirido, contribuyendo a la mejora en los resultados de aprendizaje y por consiguiente aportando al logro de las competencias. En la actualidad, existen soluciones que contribuyen al proceso evaluativo, pero enfocadas en campos de estudio específico, las cuales no incluyen elementos del contexto con uso de información sensórica de los dispositivos móviles para enriquecer y personalizar la retroalimentación. Este artículo presenta un Modelo Conceptual de Evaluación Formativa en Sistemas de Aprendizaje Móvil, el cual, define de manera explícita los componentes conceptuales, que deben ser considerados para soportar procesos de evaluación formativa. Durante la investigación se desarrolló un prototipo de Servicio Web, basado en el modelo propuesto e integrado en la plataforma LMS Google Classroom que permitió brindar retroalimentación en tiempo real basada en contexto sensible. La aplicación práctica del modelo evidenció un incremento significativo en el nivel de aprendizaje de los estudiantes, sin embargo, es importante ampliar la población de estudio para generalizar los resultados.

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Genetic fuzzy markup language-based item response theory agent for online self-learning platform construction

Lee

Wang

Lin

et al. 2016

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Item response theory with fuzzy markup language for parameter estimation and validation

Cited by 3 publications

References 31 publications

PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

PSO-Based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

Hacia un Modelo de evaluación formativa en Sistemas de Aprendizaje Móvil basados en contexto

Genetic fuzzy markup language-based item response theory agent for online self-learning platform construction

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