Tan, Shern Meng

Non-proficient demonstration, gross motor skill assessment, and subjective feedback are but a few of the perennial problems in physical education (PE). These problems stand to benefit from a technology-based solution that uses human pose estimation to guide learning. In this approach, a criterion motor action is embedded in a deep-learning algorithm (DLA). A learner can view this motor action on an iPad and uses its kinematic signatures to guide practice. The learner’s movement is captured by the device and the recorded motor action enters the DLA for computation of movement proficiency. The output of the DLA is a quantitative index that informs the learner how well the movement has been executed. In this way, the learner gains timely and objective feedback. A separate device held by the PE teacher collates the quantitative indices from other students in the class. Collectively, the information facilitates the teacher’s selection of instructional strategies.

This chapter examines the changing socio-cultural and political salience of physical education since independence in Singapore’s schools. A brief history of physical education with developmental changes based on policy orientations across key educational milestones will be presented. Specifically, the chapter will describe significant impetus and policy synergies relevant to the direction and progress of physical education with emphasis on individual’s holistic development, drive towards thinking citizens and lifelong learners, strive for community partnership and collaboration, and enhancement of teachers’ transformative pedagogies.

Guided by Basic Psychological Need Theory, we investigated the combined associations between need satisfaction and need frustration (i.e., need profiles) and their relations with theoretically relevant correlates including mindfulness, physical literacy, physical activity enjoyment, and physical activity. The participants were Singapore based school students (N = 844, Mage = 12.45, SDage = 1.99, boys = 53.1%) who completed a cross-sectional survey. The results of the latent profile analysis identified four distinct need profiles: profile 1–average satisfaction and frustration (n = 364, 44.1%); profile 2–low satisfaction (n = 251, 29.7%), above average frustration; profile 3–very high satisfaction, very low frustration (n = 144, 17.1%); and profile 4–high satisfaction, very high frustration (n = 85, 10.1%). Among these, profile 3 was the most adaptive one; it had the highest levels of mindfulness, physical literacy, physical activity enjoyment, and moderate-to-vigorous physical activity. Experiences of need satisfaction countered the negative effects of need frustration on these correlates. These findings enhance our understanding of students’ psychological need experiences and highlight the need for investigating the combined associations between need satisfaction and need frustration.