Keywords
High school students deep learning
Influencing factors
Submitted : 2024-04-24
Accepted : 2024-05-09
Published : 2024-05-24
Abstract
To clarify the influential factors of deep learning among high school students in Western China and promote the high-quality development of basic education in Western China. This study adopts the method of stratified sampling and selects high school students at different altitudes as the research object. The results show that the high school students in the western region are in the middle level of deep learning. There are differences in gender, altitude, and other variables in deep learning of students. A multiple linear regression model was used to analyze the influencing factors of deep learning for high school students in Western China. The model shows that teacher-student interaction and teacher feedback are the main influencing factors of deep learning for high school students in Western China, while parental support and classroom teaching are secondary factors.
References
General Office of the State Council, 2015, The State Council on Accelerating the Development of National Education Decision, viewed June 8, 2023, https://www.gov.cn/gongbao/content/2002/content_61658.htm.
Marton F, Saljo R, 1976, On Qualitative Difference in Learning: Outcome and Process. British Journal of Educational Psychology, 46(1): 4–11. https://doi.org/10.1111/j.2044-8279.1976.tb02980.x
Zhang H, Wu X, 2012, Analysis of the Connotation and Cognitive Theoretical Basis of Deep Learning. China E-Education, 2012(10): 7–11 + 21.
Postareff L, Parpala A, Lindblom-Ylänne S, 2015, Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments. Learning Environments Research, 18(3): 315–333. https://doi.org/10.1007/s10984-015-9186-1
Sadeghi A, Sadeghi A, 2012, The Factors Affecting University Student Deep Learning (USDL) in the University of Guilan, IRAN (Comparative Study Among Humanities, Agricultural and Physical Education Faculties). Procedia-Social and Behavioral Sciences, 31: 810–815. https://doi.org/10.1016/j.sbspro.2011.12.146
Liu Z, Wang Z, 2017, An Empirical Study on the Influence of Behavioral Engagement on Deep Learning: A Case Study of Video Learning in Virtual Reality (VR) Environment. Journal of Distance Education, 35(1): 72–81.
Li Z, Liu D, Li N, et al., 2018, Research on Influencing Factors of Deep Learning in Flipped Classroom Model. Modern Educational Technology, 28(12): 55–61.
Zheng D, Ye S, 2022, Preparation of Deep Learning Questionnaire for Primary and Secondary School Students from Multi-Dimensional Perspective. Global Education Perspectives, 51(11): 103–119.
Lam S, Jimerson S, Kikas E, et al., 2012, Do Girls and Boys Perceive Themselves as Equally Engaged in School? The Results of an International Study from 12 Countries. Journal of School Psychology, 50(1): 77–94. https://doi.org/10.1016/j.jsp.2011.07.004
Jin X, Liu X, Li J, et al., 2023, Heterogeneity of Critical Thinking Tendency of Adolescents and Its Related Factors. Chinese Journal of Mental Health, 37(2): 138–144.
Liu H, 2016, Structure, Measurement and Influencing Factors of Adolescents’ Critical Thinking Tendency, thesis, Wenzhou University.
Yin Y, 2023, Research on High School Biology Teaching Focusing on the Development of Critical Thinking, thesis, Central China Normal University.
Arzy S, Idel M, Landis T, et al., 2005, Why Revelations Have Occurred on Mountains? Linking Mystical Experiences and Cognitive Neuroscience. Medical Hypotheses, 65(5): 841–845. https://doi.org/10.1016/j.mehy.2005.04.044
Ma H, Dang P, Su R, et al., 2022, Effect of High Altitude Exposure Time on Working Memory: A Follow-Up Study. Plateau Scientific Research, 6(2): 42–50.
Wu T, Chen X, Xu F, 2023, The Relationship Between Social Support and Subjective Well-Being of Hearing-Impaired Adolescents: Moderating Mediating Role. Chinese Journal of Special Education, 2023(11): 43–50.