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The Research On Theory Of Deep Learning Of Michael Fullan

Posted on:2021-02-09Degree:MasterType:Thesis
Country:ChinaCandidate:Y H YangFull Text:PDF
GTID:2427330611464393Subject:Curriculum and pedagogy
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Under the background of promoting the reform of basic education oriented by key Competencies all over the world,it is a worldwide educational problem to choose what kinds of learning styles to develop key competencies.As an international authority on education reform,Michael Fullan systematically constructs and practices the deep learning theory that points to the development of key competencies.This learning method takes knowledge application to promote key competencies as the basic path,and relies on deep learning tasks to build a bridge between knowledge learning and real life.This research follows the research direction of combining theoretical interpretation and case analysis,mainly through the literature analysis method and comparative research method to systematically study the main views of Fullan's deep learning theory,and through contrasting and reflecting on the deep learning research of domestic teams in China,in order to provide useful reference and inspiration for China to promote the reform of discipline key competencies.Part?: Introduction.This chapter mainly introduces the background,the definition of core concepts,the research overview at home and abroad,research design of the study.Part?: The origin of Michael Fullan's deep learning theory.This chapter mainly introduced from the meaning,positioning and implementation strategies of deep learning.1.The meaning of deep learning.Fullan summarizes the meaning of deep learning as a learning method that points to the cultivation of key competencies.It proposes that it is an effective response to the application or transfer of knowledge in the era of key competencies.The application and creation of knowledge in real life is the difference between deep learning and traditional teaching or shallow learning.Among them,the application of knowledge is the core position of deep learning,and attention and reflection on life are necessary prerequisites for deep learning,and digital resources and tools are its learning accelerators.2.The vision of deep learning.Deep learning promotes the generation of learners' key competencies through the application of knowledge to solve real-life problems.The vision of deep learning is to reflect on life and participate in the world,and the mission is to promote the generation of key competencies.3.The learning strategies of deep learning.The specific strategies include: the role of teacher and student as a partnership;task design to build learning and bridge;literacy evaluation that points to the description of evidence;and the construction of a learning culture for systematic change.Part ?:Evaluation on the characteristics of Fullan deep learning theory.This part highlights the characteristics of Fullan's deep learning theory by comparing Fullan and China's deep learning research.The main features are: First,the learning method in the humanistic perspective: reflecting on the value vision of life and changing the world;the learning method in the core literacy era: the application of knowledge promotes the generation of key competencies;the learning method in the information age: integrating digital resources to accelerate the deep learning process.Part?: Evaluation and inspiration of Fullan's deep learning theory research.This chapter mainly evaluates the value and limitations of Fullan's deep learning theoretical research and the following enlightenment to the study of the change of the key competencies of learning in China: firstly,accurately grasp the mechanism of deep learning to cultivate key competencies;secondly,Local education in the application of knowledge;Thirdly,the relationship between knowledge mastery and knowledge application is handled properly in the traditional way;Finally,digital resources are widely used to promote the development of deep learning.
Keywords/Search Tags:Michael Fullan, Key Competencies, Deep Learning
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