From T2 FS-Based MoGoTW System to DyNaDF for Human and Machine Co-learning on Go

Lee, Chang-Shing; Wang, Mei-Hui; Yang, Sheng-Chi; Kao, Chia-Hsiu

doi:10.1007/978-3-319-72892-6_1

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Hence, the dynamic assessment agent is not proposed to replace IRT. It combines PSO machine learning mechanism with IRT and applies it to education domain, especially for future humans and robots co-learning [35,36]. Additionally, to distinguish items from students' abilities, the proposed method shows the estimated performance level, namely below basic, basic, proficient, and advanced.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Moreover, the proposed method can save the traditional paperand-pencil (P&P) test time and provide more accurate estimates of student what can do and what cannot do. Therefore, the proposed agent can help teachers evaluate students learning performance more efficiently, and it is recommended to use in the future classrooms for students and robots co-learning [35,36] based on IRT, but not to replace item response theory with the proposed method in this paper.…”

Section: Conclusion and Discussionmentioning

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: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

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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Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

Lee

Wang

Huang

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.

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From T2 FS-Based MoGoTW System to DyNaDF for Human and Machine Co-learning on Go

Cited by 2 publications

References 19 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

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

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