Learning to Teach Physical Education for Health: Breaking the Curriculum Safety Zone.
Qiao ZhuHejun ShenAng ChenPublished in: Research quarterly for exercise and sport (2020)
Purpose: Based on the theorized concept "Curriculum Safety Zone (CSZ)," this study was conducted to identify the factors for breaking CSZ by contrasting the experiential accounts of two pre-service teacher groups who taught within or outside of their CSZ. Method: Pre-service teachers (n = 14) from a sport-centered PETE program were trained to teach a Health-First curriculum module and their peers (n = 14) a traditional sport module. Each group taught their respective module to 14 intact 7th grade classes in 14 schools in China. A mixed-methods design was used. Quantitative data on learner knowledge gain were collected from the learners. Qualitative data included lesson observation fieldnotes, social-media posts, and interview responses and were gathered from the pre-service teachers. Results: Learners in the Health-First schools gained more knowledge than those in the Comparison schools (t26 = 2.92, p = .007, Cohen's d = 1.10). Qualitative evidence was triangulated using a Health-First vs. Comparison contrasting approach with open-, axial-, and selective-coding to generate themes. The themes were Confidence in Doubt, Lesson Plans to the Rescue, Professional Development is Necessary But…, and Student Learning Save the Day! A grounded theory was developed using the themes and interpreted using the Interconnection Model of Teacher Professional Growth. Conclusion: Breaking CSZ requires a synergistic effort with carefully designed professional development, detailed lesson plans, an effective support network, and, most important of all, a powerful curriculum that can elicit observable and measurable learner achievement.
Keyphrases
- healthcare
- mental health
- quality improvement
- social media
- health information
- public health
- medical students
- medical education
- machine learning
- physical activity
- electronic health record
- health promotion
- high resolution
- randomized controlled trial
- study protocol
- emergency medicine
- clinical trial
- big data
- human health
- deep learning
- artificial intelligence
- body composition
- high intensity
- anterior cruciate ligament