This meta-analysis synthesizes research findings on the effects of integrated STEM education implemented in K12 settings. The implementation fell into three categories: (1) adopting integrated STEM education, (2) using extra teaching and learning strategies to enhance integrated STEM education, and (3) using specific learning technologies to support integrated STEM education. Student learning outcomes were investigated in terms of knowledge and skills acquisition, problem-solving task performance, and student perceptions. Based on 124 extracted and coded studies (2010–2022), the findings are as follows. All three types of interventions yielded a medium effect on knowledge acquisition and a small effect on student perceptions. Besides, adopting integrated STEM education had a large effect on cognitive skills; using extra teaching and learning strategies in integrated STEM programs produced a medium effect on cognitive skills and problem-solving task performance; using specific learning technologies had a small effect on problem-solving task performance. Some factors, such as task type (inquiry or design-based task) and program duration, may influence STEM learning outcomes. Future studies should pay more attention to the effective design and implementation of STEM programs by integrating the four core characteristics of STEM education, applying extra teaching and learning strategies, incorporating relevant learning technologies, and assessing learning outcomes in multiple dimensions.

Previous studies have revealed the restorative effects of exposure to natural environments on psychological well-being and cognitive performance. Recent studies have reported the effects of exposure to nature sounds (e.g., the sounds of birds, rainfall, and waves) through a mobile application on reducing students’ mental fatigue and improving their cognitive performance. However, it remains unknown whether exposure to nature sounds through a mobile application may influence students’ learning performance. To address the gap, we conducted a study with 71 university students. During the four-week intervention, 36 students in the experimental group were exposed to nature sounds through a free mobile application for at least 30 consecutive minutes per day when working on academic-related tasks; 35 students in the control group did not have such exposure when working on similar tasks. The results show that students in the experimental group outperformed those in the control group in their engagement in deep learning, frequency of academic procrastination, and academic self-efficacy. The findings reveal the promising effects of exposure to nature sounds through a mobile application on improving students’ learning performance. The implications of the findings are discussed.

Computational thinking, the thinking processes and skills for analyzing and developing computational solutions to solve real-world problems, has been increasingly cultivated through STEM (Science, Technology, Engineering, and Mathematics) programs that integrate programming and non-programming disciplines in authentic problem-solving tasks. To guide students in learning with complex programming tasks, existing studies have employed explicit instructional models (i.e., identifying problems, decomposing programming tasks, coding, and optimizing programs). However, these models are inadequate to support young learners, who often struggle to connect non-programming knowledge to programming processes and accomplish each phase of the complex task. This study proposed the ICDCO instructional model that features (a) incorporating interdisciplinary knowledge acquisition into the task process and (b) providing rules-of-thumb to guide each phase of the task. An action research study was conducted with 136 students from three primary schools in Mainland China and Macau. Results show that after taking a STEM course designed using the ICDCO instructional model, students significantly increased their overall self-perceived computational thinking skills and performed well in decomposing a complex problem. The impact of the approach was affected by class size and students’ educational level but not by gender. The findings suggest that the proposed approach can benefit students’ computational thinking skills and performance in decomposing a complex task. More support should be provided for students in large classes and at lower educational levels. Further research is needed to examine the effects of the ICDCO instructional model and the factors that influence its application in cultivating computational thinking in STEM courses.

The study explores teachers' self-regulated learning (SRL) process patterns in technological pedagogical content knowledge (TPACK) development. SRL processes were analyzed through think-aloud protocols. TPACK achievements were assessed by the quality of participants' lesson plans. Preliminary results showed high achievers performed a more recursive sequential regulatory pattern, while low achievers showed a more linear one. Teachers with a more successful SRL model could achieve better TPACK performances. These findings could contribute to detecting the relations between SRL and TPACK.

This study examined the global media citation network of COVID-19-related news at two stages of the pandemic alert phase, i.e., the national level alert stage and the global level alert stage. The findings reveal that the small-world pattern and scale-free property of media citation networks contributed to the rapid spread of COVID-19-related news around the world. Within the networks, a small number of media outlets from a few countries formed the backbone of the network to control the risk communication; meanwhile, many media of geographical and cultural similarities formed cross-border collaborative cliques in the periphery of the network. When the alert phase escalated from the national level to the global level, the network demonstrated a number of changes. The findings contribute to the understanding of risk communication for international public health emergencies by taking into account the network perspective and evolutionary nature of public health emergencies in analysis.