Innovative teaching units applied to physical education

Abstract

The aim of this article is to reflect on the original attributes that define planning in order to reformulate the concept of teaching units in Physical Education (PE), providing teachers with new structures of planning that allow them to achieve authentic outcomes. Some issues related to students’ learning are raised as regards the duration of a traditional teaching unit, as well as the total time amount and allocation dedicated to students’ learning. Based on previous psychological and learning theories, as well as on in-service teachers’ experience, four proposals of innovative teaching units are provided. Providing opportunities for significant and meaningful learning level in PE is not an easy task for teachers. The main objective of the proposals is to provide students with substantial learning experiences and to accomplish authentic outcomes by planning PE classes. Intermittent, alternated, irregular, and reinforced teaching units are analysed. The proposals focus on new approaches to time allocation in PE, and on a renovated concept of unit planning and curriculum design. Several applications for each type of teaching unit are shown in order to highlight PE teachers’ opportunities to innovate their planning. The proposals of intermittent, alternated, irregular, and reinforced teaching units could guide teachers through planning of PE based on related learning and may provide an effective tool for PE planning to overcome learning deficiencies attributed to traditional teaching units.

Efficacy of school‐based interventions for improving muscular fitness outcomes in children: A systematic review and meta‐analysis

“…The characteristics of the 17 included studies are presented in Table 1. The included studies were conducted in different continents including “the Americas” (Annesi, Westcott, Faigenbaum, & Unruh, 2005; Faigenbaum et al, 2013, 2014, 2015; Siegel, Camaione, & Manfredi, 1989), Europe (Alves, Marta, Neiva, Izquierdo, & Marques, 2016; Arabatzi, 2018; Engel et al, 2019; Grainger, Innerd, Graham, & Wright, 2020; Granacher, Goesele, et al, 2011; Granacher, Muehlbauer, Maestrini, Zahner, & Gollhofer, 2011; Larsen et al, 2016; Lucertini et al, 2013; Marta et al, 2019; Viciana, Mayorga‐vega, & Cocca, 2013), and Asia (Qi et al, 2019; Sadres, Eliakim, Constantini, Lidor, & Falk, 2001). The included studies involved a total of 1653 participants ( n = 1264 “exercise group” participants and 28% girls), ranging from a sample size of 32 (Granacher, Goesele, et al, 2011; Granacher, Muehlbauer, et al, 2011) to 570 (Annesi et al, 2005) participants.…”

“…In addition, Grainger et al, (Grainger et al, 2020) found non‐significant positive results with slight increases in countermovement jump height in the EG following MF training. Methods of assessing local muscular endurance included the sit‐up and push‐up tests (Annesi et al, 2005; Engel et al, 2019; Faigenbaum et al, 2014, 2015; Lucertini et al, 2013; Siegel et al, 1989; Viciana et al, 2013). With respect to muscular strength and muscular power, most of studies included lower limbs assessments including the standing long jump, single‐leg hop tests, countermovement jump, vertical jump, drop jump and knee extension‐flexion or upper body assessments including the 1‐kg and 3‐kg medicine ball toss, hand‐grip, pinch‐strength, biceps curl test, cable flexion, cable extension, flexed arm hang and pull‐ups.…”

[Retracted] Personalised Recommendation of PE Network Course Environment Resources Using Data Mining Analysis

“…This led to the development of recommendation technology, which has since flourished. According to the information that users already possess, recommendation technology makes suggestions for information that the target users might be interested in, making it easier for users to find the information they require [ 2 ]. The most popular recommendation algorithms at the moment are the CF (Collaborative Filtering) recommendation algorithm, the content-based recommendation algorithm, the recommendation algorithm based on user-project bipartite graph relationship, and the hybrid recommendation algorithm.…”

Inheritance of Sports Intangible Cultural Heritage and the Construction of Physical Education in Colleges and Universities under the Wireless Communication Microprocessor

“…It is not easy to provide students with meaningful learning opportunities in PE. Jes ú s Viciana analyzed the intermittent, alternate, irregular, and intensive teaching units [5]. Petherick La aimed to explore the racial and cultural issues in health education in secondary school health and PE curriculum in Ontario, Canada.…”