A Study on the Influencing Factors of Teaching Interaction on Deep Learning from the Perspective of Social Cognitive Theory
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Abstract
Based on Social Cognitive Theory (SCT), a research model is constructed with teaching interaction as the independent variable, self-efficacy as the mediating variable, and Deep learning as the dependent variable. The research uses regression analysis and Bootstrap test to explore the impact of teaching interaction on college students' Deep learning and the mediating role of self-efficacy. The research results show that: teaching interaction positively and significantly affects college students Deep learning and self-efficacy, of which material-chemical interaction has the most significant effect on college students Deep learning (β=0.431); selfefficacy positively affects college students' Deep learning (β=0.255), and play a partial mediating role in teaching interaction and Deep learning. Finally, the research proposes to build a multimodal interaction mechanism to promote the realization of Deep learning; to create an embodied collaborative learning context to improve the quality of teaching interaction; Learn and reference.
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References
Xuemei Zhao, Zhong, Shao-Chun (2011) A new species of the genus Phyllostachys (Hymenoptera, Braconidae) from China. (2021). A study of multimodal interaction mechanisms to promote higher-order thinking development in the context of embodied cognition. 08, 65-71.
F Wang, L Wang, X Ye & Chen A new species of the genus Phyllostachys (Coleoptera, Staphylinidae) from China. (2021). A study on the influence of inclusive mentoring style on graduate students' innovative behavior -the mediating role of innovation self-efficacy and the moderating role of Deep learning. 02, 66-74.
F Marton, R Säaljö (1976) ON QUALITATIVE DIFFERENCES IN LEARNING-II OUTCOME AS A FUNCTION OF THE LEARNER'S CONCEPTION OF THE TASK. 46(2), 115-127.
Ling He, Jia-Hou Lai (2005) Promoting students' Deep learning. (05), 29-30.
J Li, W Wu (2009) SOLO classification theory and its view of teaching evaluation. (02), 16-19.
Feng Youmei, Yan Shigang, Li Yi (2018) On the logic of constructing the classification system of educational objectives in the context of core literacy-from the interrogation of Bloom's style educational objective classification system. 39(06), 5-10.
Nelson Laird, T Shoup, R Kuh, G (2006) Measuring Deep approaches to learning using the National Survey of Student Engagement.
John Dewey (2005) Experience, nature and art.. 354-393.
Y Wang, Huang Cuiyao (2003) Habermas and the role of linguistic interaction in environmental planning. 204-219.
Terry Anderson (2003) Getting the Mix Right Again: An Updated and Theoretical Rationale for Interaction. 4(2).
Wang Zhiyan (2019) Exploration of Blended Teaching Model for Promoting Deep Learning. 6(5), 82-87.
Lu Qiang (2021) Research on the influence of teaching interaction level on college students' online Deep learning. 42(03), 34-41.
Z Zhan, Q Wu, W He, S Cheng, J Lu, Y Han (2021) K12 teacher-student interaction patterns in the smart classrooms. 29(3), 267-286.
Chen Beilei, Zhang Yi, Yang Bing, Xiong Jie, Lin Li (2019) Research on teaching interaction in smart classrooms to promote Deep learning among college students. 40(03), 90-97.
H Li, C Xiao (1995) Students' self-efficacy and its cultivation. (01).
Albert Bandura (1977) Self-efficacy: Toward a unifying theory of behavioral change.. 84(2), 191-215.
A Bandura, G Caprara, C Barbaranelli, C Pastorelli, C Regalia (2001) Sociocognitive self-regulatory mechanisms governing transgressive behavior. 80(1), 125.
Wu Yajie (2017) A study on factors affecting learners' online Deep learning and its measurement. 38(9), 57-63.
Xiao-Li & Zhou, Lou, Zhen-Zhen (2011) A study on the relationship between online learning self-efficacy and Deep learning among students in higher education. 08, 89-96.
Lin Li, Shanshan Yang (2021) Exploring the Influence of Teacher-Student Interaction on University Students' Self-Efficacy in the Flipped Classroom. 10(2), 84.
Xie Youru, Liu Chunhua, Zhu Jingjing, Yin Rui (2011) Research on the structure, influencing factors and cultivation strategies of university students' selfefficacy in online learning. (10), 30-34.
Chen Beilei (2018) Research on the impact of teaching interaction in smart classrooms on college students' Deep learning.
Tianji Liu, Bao Ru, Yeqing Zhang (2020) The Design of a Moral Education Evaluation System for College Students Based on a Deep Learning Model. 2022(12), 1-10.
R Schwarzer (1997) Optimistic self-beliefs: Assessment of general perceived self-efficacy in thirteen cultures. 3(1), 177-190.
(2004) National Survey of Student Engagement.
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2022-11-09
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