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Fall prevention has always been a crucial topic for injury prevention. Research shows that real-time posture monitoring and subsequent fall prevention are important for the prevention of fall-related injuries. In this research, we determine a real-time posture classifier by comparing classical and deep machine learning classifiers in terms of their accuracy and robustness for posture classification. For this, multiple classical classifiers, including classical machine learning, support vector machine, random forest, neural network, and Adaboost methods, were used. Deep learning methods, including LSTM and transformer, were used for posture classification. In the experiment, joint data were obtained using an RGBD camera. The results show that classical machine learning posture classifier accuracy was between 75% and 99%, demonstrating that the use of classical machine learning classification alone is sufficient for real-time posture classification even with missing joints or added noise. The deep learning method LSTM was also effective in classifying the postures with high accuracy, despite incurring a significant computational overhead cost, thus compromising the real-time posture classification performance. The research thus shows that classical machine learning methods are worthy of our attention, at least, to consider for reuse or reinvention, especially for real-time posture classification tasks. The insight of using a classical posture classifier for large-scale human posture classification is also given through this research. 

This qualitative study investigates the development of science teacher leaders during and after professional development. It examines the impactful experiences during a teacher leadership program that allowed them to explore their leadership identity and how this identity manifests in their schools’ post-program. The participants are 14 chemistry and physics teacher leaders from schools in the Southeastern U.S. who attended a five-year Noyce teacher leadership program. They are emergent leaders who entered the program with limited leadership exposure or expertise. Social learning theory provides the lens to examine during and post-program interviews with science teacher leaders. Three themes emerge from the interviews: 1) Redefining leadership, 2) Responsibility for others, and 3) Collaborative community that developed the science teacher leadership identity during and after the program. The findings from this study have theoretical and practical implications for teacher leaders, schools, and leadership development programs. 

Abstract BackgroundTeacher turnover is a dire and chronic problem for many education systems across the globe. According to UNESCO, 70% of teachers will be replaced by 2030. This study investigates the relationship between the retention of science and mathematics teachers and factors related to human, social, structural, and positive psychological capital—a four-capital teacher retention model. More specifically, this study explores how teaching self-efficacy, leadership engagement, teacher-school fit, diversity beliefs, community connections, and professional social network characteristics (e.g., size, bridging, proximity, reach) relate to teacher retention. Additionally, potential differences in retention and the aforementioned factors related to the four-capital model between Master Teaching Fellows (MTFs) and their peers (non-MTFs) with similar human capital (demographics and experience) are explored in this study. Participants were K-12 science and mathematics teachers (85 MTFs and 82 non-MTFs) from six different regions across the U.S. MTFs participated in one of seven long-term (5–6 years) Robert Noyce Master Teaching Fellowship Programs funded by the National Science Foundation. ResultsLeadership engagement was positively associated with shifting (from teaching to a formal leadership position). Teacher-school fit was negatively associated with leaving. For secondary teachers, teaching self-efficacy was positively associated with shifting to a leadership position. Leadership network size, bridging, and geographic proximity variables were positively related to shifting when compared to staying as classroom teachers. Teaching network bridging and leadership network bridging were positively related to leavers when compared to stayers. MTF shifters were likely to shift earlier in their careers than non-MTFs. Lastly, MTFs had higher self-efficacy, geographically larger teaching networks and leadership networks, and more contacts and bridging roles in their leadership networks than non-MTFs. ConclusionFindings provide support for teacher leadership programs in promoting leadership roles and responsibilities for STEM teachers and retaining teachers in STEM education either in the classroom or in administrative roles. These findings suggest that school administrators may also play a key role in encouraging teachers to engage in leadership activities and have a broader impact on public education by, for example, adopting a hybrid model of leadership roles that involves classroom teaching. 

A variety of research studies reveal the advantages of actively engaging students in the learning process through collaborative work in the classroom. However, the complex nature of the learning environment in large college general chemistry courses makes it challenging to identify the different factors that affect students’ cognitive and social engagement while working on in-class tasks. To provide insights into this area, we took a closer look at students’ conversations during in-class activities to characterize typical discourse patterns and expressed chemical thinking in representative student groups in samples collected in five different learning environments across four universities. For this purpose, we adapted and applied a ‘Community of Learners’ (CoL) theoretical perspective to characterize group activity through the analysis of student discourse. Within a CoL perspective, the extent to which a group functions as a community of learners is analyzed along five dimensions including Community of Discourse (CoD), Legitimization of Differences (LoD), Building on Ideas (BoI), Reflective Learning (RL), and Community of Practice (CoP). Our findings make explicit the complexity of analyzing student engagement in large active learning environments where a multitude of variables can affect group work. These include, among others, group size and composition, the cognitive level of the tasks, the types of cognitive processes used to complete tasks, and the motivation and willingness of students to substantively engage in disciplinary reasoning. Our results point to important considerations in the design and implementation of active learning environments that engage more students with chemical ideas at higher levels of reasoning. 

The STEM teacher workforce in the United States has faced a host of pressing challenges, including teacher shortages, pervasive job dissatisfaction, and high turnover, problems largely attributable to working conditions within schools and districts. These problems have been exacerbated in high-needs districts with fewer resources and more students from low-income communities. Since social network research has shown that workplace relationships are vital for retention, this study investigates the demographic and relational antecedents to what we dub ties of retention. We explore how demographic and relational properties affect the likelihood that teachers have “retention-friendly” networks, characterized by connections important for retention. Our analysis of data from a sample of 120 STEM teachers across five geographic regions identifies key demographics (i.e., site, gender, career changer, and prior teaching experience) and relational properties (network size, positive affect, and perceptions of bridging) associated with ties of retention. We discuss the implications of our findings for the STEM teacher workforce and for teacher education programs. 

Abstract Background Teacher communities of practice, identity, and self-efficacy have been proposed to influence positive teacher outcomes in retention, suggesting all three may be related constructs. Qualitative studies of communities of practice can be difficult to empirically link to identity and self-efficacy in larger samples. In this study, we operationalized teacher communities of practice as specific networks related to teaching content and/or pedagogy. This scalable approach allowed us to quantitatively describe communities of practice and explore statistical relationships with other teacher characteristics. We asked whether these community of practice networks were related to identity and self-efficacy, similar to other conceptualizations of communities of practice. Results We analyzed survey data from 165 in-service K-12 teachers prepared in science or mathematics at 5 university sites across the USA. Descriptive statistics and exploratory factor analyses indicated that math teachers consistently reported smaller communities of practice and lower identity and self-efficacy scores. Correlations revealed that communities of practice are more strongly and positively related to identity than self-efficacy. Conclusion We demonstrate that teacher communities of practice can be described as networks. These community of practice networks are correlated with teacher identity and self-efficacy, similar to published qualitative descriptions of communities of practice. Community of practice networks are therefore a useful research tool for evaluating teacher characteristics such as discipline, identity, self-efficacy, and other possible outcomes (e.g., retention). These findings suggest that teacher educators aiming to foster strong teacher identities could develop pre-service experiences within an explicit, energizing community of practice. 

Several studies have highlighted the positive effects that active learning may have on student engagement and performance. However, the influence of active learning strategies is mediated by several factors, including the nature of the learning environment and the cognitive level of in-class tasks. These factors can affect different dimensions of student engagement such as the nature of social processing in student groups, how knowledge is used and elaborated upon by students during in-class tasks, and the amount of student participation in group activities. In this study involving four universities in the US, we explored the association between these different dimensions of student engagement and the cognitive level of assigned tasks in five distinct general chemistry learning environments where students were engaged in group activities in diverse ways. Our analysis revealed a significant association between task level and student engagement. Retrieval tasks often led to a significantly higher number of instances of no interaction between students and individualistic work, and a lower number of knowledge construction and collaborative episodes with full student participation. Analysis tasks, on the other hand, were significantly linked to more instances of knowledge construction and collaboration with full group participation. Tasks at the comprehension level were distinctive in their association with more instances of knowledge application and multiple types of social processing. The results of our study suggest that other factors such as the nature of the curriculum, task timing, and class setting may also affect student engagement during group work.