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Systematic Mode Construction of Mixed Teaching from the Perspective of Deep Learning

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  • Danni Zhao
  • Naeem Jan

Abstract

Deep learning will be one of the key technologies to promote learning in the next five years. With the rapid advancement of the information era, significant changes in students’ learning and thinking patterns have occurred. How to further encourage the development of students’ professional skills and innovative capacity has become the focus of society under the influence of the notion of deep learning. With the application of information technology in education, blended learning, as the only key trend mentioned in the new media alliance report for five consecutive years, has injected fresh vitality into the reform of traditional classrooms and laid a foundation for better promoting in-depth learning. Therefore, how to effectively use blended learning to change these phenomena has become an urgent problem to be solved. The goal of this research is to encourage pupils to learn in depth. This study specifies the design idea of a hybrid teaching mode supported by an information environment based on the promotion of high-order thinking capacity. Firstly, this study uses the literature research method to sort out the relevant literature on deep learning and hybrid teaching, which provides a theoretical basis for the later construction. Second, a questionnaire is utilized to assess the existing state of in-depth learning as well as the need for blended teaching. The mixed teaching mode has effectively promoted the development of students’ high-level thinking abilities such as autonomous learning, problem-solving, and application innovation; played a positive role in cultivating students’ in-depth learning; and finally won the unanimous recognition of students.

Suggested Citation

  • Danni Zhao & Naeem Jan, 2022. "Systematic Mode Construction of Mixed Teaching from the Perspective of Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
  • Handle: RePEc:hin:jnlmpe:7104587
    DOI: 10.1155/2022/7104587
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