Inquiry Into The Teaching Strategy Of Deep Learning Of High School Geography Causality Knowledge

With the deepening of curriculum reform,geography education is increasingly concerned about students' in-depth learning.Geographical Causality Knowledge is the Key and Difficult Content in High School Knowledge,This paper explores the teaching strategies of in-depth learning of students' knowledge of geographical causality,To meet the needs of students' development and teachers' teachingDeep learning is an important learning concept in today's educational circles,and it has essentially different from mechanical and passive acceptance of knowledge,and different from fragmented interpretation knowledge.Teaching strategies under Deep learning focus on mobilizing students' initiative,promoting students' internalization of knowledge,emphasis on scenario creation and knowledge integration and so on,pay attention to students' ability to transfer knowledge.Deep learning theory has the same purpose as modern education reform,therefore,the teaching strategy under deep learning has certain guiding significance for geographical causality relationship knowledge.Based on the study of geographic causality relationship knowledge and deep learning theory,using the literature research method to derive the principle of deep learning teaching strategy of geographical causality knowledge as integrity,regional differences,comprehensive principles.Teaching strategies are scenario creation,text mapping,knowledge integration,knowledge map construction strategies.Teaching practice,according to the current high school textbook,and combined with specific teaching cases to explain the application of the Deep learning teaching strategy of geographic causality relationship knowledge,implemented these instructional strategies,using concept map evaluation compared and analysed the score data of the Experimental Class and the Controlled Class to verify the effectiveness of teaching strategies to some extend.Results show the Deep learning teaching strategy of geographic causality relationship knowledge is feasible.