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The Frontiers of Society, Science and Technology, 2023, 5(5); doi: 10.25236/FSST.2023.050512.

Study on Characteristics and Constituent Elements of Learning Plan from the Perspective of Deep Learning

Author(s)

Yueqin Gao1,2

Corresponding Author:
Yueqin Gao
Affiliation(s)

1The University of Perpetual Help System-DAL TA, Metro Manila, 1740, Philippines

2Guangdong Communication Polytechnic, Guangzhou, 510650, China

Abstract

The in-depth learning from the perspective of learning science, which emerged in the 1970s, focuses on the social character and key ability of learners in the era of knowledge economy to adapt to and lead the development of modern society and become people who are beneficial to themselves and society. As a technical tool or spiritual value carrier of classroom teaching, learning plan attaches importance to "situational" complex learning research, pays attention to the questioning of cognitive subject "human value", shifts from attaching importance to the capacity of classroom knowledge to enhancing the depth of classroom knowledge, and aims to pursue an effective learning process of understanding and innovative learning of knowledge with important concepts as the core, and cultivate students' knowledge, ability and emotion necessary for in-depth learning. The study plan from the perspective of deep learning has the characteristics of knowledge questioning, problem thinking, hierarchical thinking and hierarchical gradient. It is composed of learning objectives, knowledge structure, problem design, scenario creation, reflection and evaluation, and forms a deep learning logical thinking framework with problem orientation as the guide and ideological development as the purpose throughout the process.

Keywords

deep learning; study plan; connotation; features; structural elements

Cite This Paper

Yueqin Gao. Study on Characteristics and Constituent Elements of Learning Plan from the Perspective of Deep Learning. The Frontiers of Society, Science and Technology (2023) Vol. 5, Issue 5: 74-80. https://doi.org/10.25236/FSST.2023.050512.

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