An Adaptive Self-assessment Model for Improving Student Performance in Language Learning Using Massive Open Online Course (MOOC)

Hashim, Hasmaini; Salam, Sazilah; Mohamad, Siti Nurul Mahfuzah; Cheong, Kar Mee; Tan, Poh Ee

doi:10.1007/978-981-15-6025-5_1

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…began the next phase in the technology .It was effective at instructing, but it lacked the expertise of a human educator. They modified the system and introduced the framework, which employed linguistic processing methods to aid learning and combine information with literacy development (Hashim et al, 2020).…”

Section: Background To the Adaptive Learning Modelsmentioning

confidence: 99%

Supporting Adaptive English Learning With Fuzzy Logic-Based Personalized Learning

Ding

2022

International Journal of Gaming and Computer-Mediated Simulations

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Serious play for learning has gained popularity in recent years. The research belongs to the opportunity to engage pupils in innovative ways to attract and hold their attention. The purpose of this research is to offer a novel interdisciplinary strategy to identify English language possibilities using complex games that integrates psychological analytic theory, fuzzy reasoning, and neural networks. The study began with a Delphi approach to collect comments from students based on a matrix of the usage of complex games to speak English. A fuzzy logic-based personalized English learning (FLPEL) system is suggested in this article. It creates a fuzzy linguistic identification method that includes four basic input variables (strong points, weak points, possibilities, and risks) recognized through the matrix. It indicates the outcome (studying potentials of the second language) by considering some dynamic input characteristics. The outcomes of the implementation were acquired using the Matlab tool and revealed trustworthy insights and discoveries.

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Section: Background To the Adaptive Learning Modelsmentioning

confidence: 99%

Supporting Adaptive English Learning With Fuzzy Logic-Based Personalized Learning

Ding

2022

International Journal of Gaming and Computer-Mediated Simulations

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Development of a Fuzzy Model of a Student Intelligent Teaching System of Technical Discipline

Tagirova¹,

Zubkova²

2022

vkit

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The development of computer technology provides great opportunities for modernizing the learning process. Teachers are actively introducing various electronic learning systems, the leading positions among which are occupied by intelligent learning systems. One of the tasks of developing an intelligent learning system is the implementation of dynamic adaptation of educational material to the level of knowledge of students. Therefore, it is necessary that it includes a student model that reflects the characteristics of the student that are important for managing the learning process. The paper describes the development of a fuzzy model of a student in teaching intelligent systems. This model is based on the concept of “student status”, the parameters of which are the level of mastering the discipline and the student’s personal qualities. A distinctive feature of the proposed fuzzy model is the joint use of the stereotypical and overlay models of the student. In addition, the developed fuzzy model is based not only on the assessment of student’s knowledge, but takes into account the personal qualities of students (responsibility, desire for self-learning and development, perseverance, attentiveness, stress tolerance). The paper proposes the use of a fuzzy expert system as a tool for decision support in determining the status of a student. During the operation of the expert system, the characteristics of students are evaluated, which are compared with the rules of the fuzzy rule base. When implementing a logical inference, a student is assigned one of the possible status values (beginner, trainee, master, professional and expert). At the final step, the student is offered to study the theoretical material of the discipline adapted to his characteristics.

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An Adaptive Self-assessment Model for Improving Student Performance in Language Learning Using Massive Open Online Course (MOOC)

Cited by 2 publications

References 17 publications

Supporting Adaptive English Learning With Fuzzy Logic-Based Personalized Learning

Supporting Adaptive English Learning With Fuzzy Logic-Based Personalized Learning

Development of a Fuzzy Model of a Student Intelligent Teaching System of Technical Discipline

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