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[Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System

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  • Fangxia Zheng

Abstract

Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and interests of the students. In essence, the support of a teacher guides the pupils to learn to work on their own during a declining guidance policy, to improve their abilities and skills. Thus, the teachers are asked to modify their teaching methods instead of applying a similar way of teaching for all students. The modified teaching style should meet the different levels of readiness of students, the different ways they learn, and their different interests. In support of this specific task for teachers, the current work presents a personalized education system based on hybrid intelligent recommendations. Specifically, a hybrid framework of artificial intelligence is proposed, which focuses on the way to provide targeted recommendations for the implementation of integrated standard lesson plans, which will be the main tool for creating flexible differentiated pedagogical programs that will perfectly meet the personal needs and particularities of each student.

Suggested Citation

  • Fangxia Zheng, 2022. "[Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
  • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:1313711
    DOI: 10.1155/2022/1313711
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    References listed on IDEAS

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    3. Sundaresan Bhaskaran & Raja Marappan & Balachandran Santhi, 2021. "Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications," Mathematics, MDPI, vol. 9(2), pages 1-21, January.
    4. Julius T. Nganji, 2018. "Towards learner-constructed e-learning environments for effective personal learning experiences," Behaviour and Information Technology, Taylor & Francis Journals, vol. 37(7), pages 647-657, July.
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