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Pedagogy
Pedagogy Matters: The Realization of Deep Learning Theories in Physical Education

Introduction
The concept of deep learning emerged as a neural network for the purpose of absorbing and transforming data (Bengio, Courville and Vincent, 2013). With the development of the concept, it was accepted and applied to various fields, especially in computing. However, in recent years, educational professionals increasingly pay attention to the application of deep learning theories in teaching and learning (Marton & Säljö, 1976; Beatie, Collins and Mclnnes, 1997; Bowden & Marton, 1998; Biggs, 2003; Ramsden, 2003; Tagg, 2003). One of the most valuable and influential theory is the “Nine Gateways” from David Hargreaves and other professionals with the purpose to improve teaching and learning in educational institutes (2004). The nine gateways are student voice, assessment for learning, learning to learn, new technologies, curriculum, advice and guidance, mentoring and coaching, workforce development, and school organization and design. This essay will discuss the realization of nine gateways in the physical education context. It will first explain the concept of deep learning and the underlying meaning of nine gateways; then it will explore the application of nine gateways in the real world teaching activities, for example, teaching the top spin action of table tennis in class; and finally, it will analyze the potential issues for teachers when they teach with deep learning theories in the context of physical education and make the attempt to offer possible solutions.

Deep learning and nine gateways
The concept of deep learning has been introduced to education for decades, and educators and researchers have gradually established the theoretical foundation. Simply, deep learning focuses more on understanding than knowing. It is the underlying meanings of the information that requires digging and exploring, such as the key concepts, the inner relationships, and the transition



References: Beatie, V., Collins, B. and Mclnnes, B. (1997). Deep and surface learning: A simple or simplistic dichotomy? Accounting Education, 6(1), 1-12. Bengio, Y., Courville, A., and Vicent, P. (2013). Representation learning: A review and new perspectives. IEEE Trans. PAMI, special issue Learning Deep Architectures. Biggs, J. B. (2003). Teaching for quality learning at university. Buckingham: Open University Press. Bowden, J. & Marton, F. (1998). The university of learning. London: Kogan Page. Frapwell, A. & Carldecott, S. (2011). In deep: Learning to learn. London: afPE by Coachwise. Gordon, C. and Debus, R. (2002). Developing deep learning approaches and personal teaching efficacy within a preservice teacher education context. British Journal of Educational Psychology, 72, 483-511. Hargreaves, D. (2004). Personalizing learning. October 2004. Available at http://www.clusterweb.org.uk/docks/HargreavesPersonalisedLearning.pfd. [Accessed 10 July 2014] Laird, T Lynch, R., McNamara, P. M. and Seery, N. (2012). Promoting deep learning in a teacher education programme through self- and peer-assessment and feedback. European Journal of Teacher Education, 35(2), 179-197. Marton, F. and Säljö, R. (1976). On qualitative differences in learning--I: Outcome and process. British Journal of Educational Psychology, 46, 4-11. Phan, H. P. (2011). Deep processing strategies and critical thinking: Developmental trajectories using latent growth analyses. The Journal of Educational Research, 104, 283-294. Ramsden, P. (2003). Learning to teach in higher education. London: RoutledgeFalmer. Tagg, J. (2003). The learning paradigm college. Boston: Anker.

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