We investigated the effects of reflex-based self-defence training on police performance in simulated high-pressure arrest situations. Police officers received this training as well as a regular police arrest and self-defence skills training (control training) in a crossover design. Officers' performance was tested on several variables in six reality-based scenarios before and after each training intervention. Results showed improved performance after the reflex-based training, while there was no such effect of the regular police training. Improved performance could be attributed to better communication, situational awareness (scanning area, alertness), assertiveness, resolution, proportionality, control and converting primary responses into tactical movements. As officers trained complete violent situations (and not just physical skills), they learned to use their actions before physical contact for de-escalation but also for anticipation on possible attacks. Furthermore, they learned to respond against attacks with skills based on their primary reflexes. The results of this study seem to suggest that reflex-based self-defence training better prepares officers for performing in high-pressure arrest situations than the current form of police arrest and self-defence skills training. Practitioner Summary: Police officers' performance in high-pressure arrest situations improved after a reflex-based self-defence training, while there was no such effect of a regular police training. As officers learned to anticipate on possible attacks and to respond with skills based on their primary reflexes, they were better able to perform effectively.
Extended Reality (XR) technologies—including virtual reality (VR), augmented reality (AR), and mixed reality (MR)—offer transformative opportunities for education by enabling immersive and interactive learning experiences. In this study, we employed a mixed-methods approach that combined systematic desk research with an expert member check to evaluate existing pedagogical frameworks for XR integration. We analyzed several established models (e.g., TPACK, TIM, SAMR, CAMIL, and DigCompEdu) to assess their strengths and limitations in addressing the unique competencies required for XRsupported teaching. Our results indicate that, while these models offer valuable insights into technology integration, they often fall short in specifying XR-specific competencies. Consequently, we extended the DigCompEdu framework by identifying and refining concrete building blocks for teacher professionalization in XR. The conclusions drawn from this research underscore the necessity for targeted professional development that equips educators with the practical skills needed to effectively implement XR in diverse educational settings, thereby providing actionable strategies for fostering digital innovation in teaching and learning.
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