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This qualitative case study of four middle school and secondary school mathematics teachers examines how teachers’ reflective practice supported their selection of strategies that align with the needs of current mathematics education according to the 10 dimensions of mathematics education. The findings suggest that teachers’ reflection practice, through collaboration with other teachers and creating a positive safe environment within their classroom for students’ feedback, impacts their design of classroom practices. They prioritize planning and preparing for the dimensions that are being discussed in the meetings with other teachers. Another major finding is that high level reflective teachers’ attitudes toward teaching mathematics is consistent with the needs of current mathematics education. Lastly, meeting individual needs of students is the dimension that all teachers in this study, regardless of their level of reflection, were struggling to reflect on and plan for. Overall, each teacher reported reflective practices that appeared to align with the attitudes and practices for teaching mathematics survey.

Exoskeletons and active prostheses promise to enhance human mobility, but few have succeeded. Optimizing device characteristics on the basis of measured human performance could lead to improved designs. We have developed a method for identifying the exoskeleton assistance that minimizes human energy cost during walking. Optimized torque patterns from an exoskeleton worn on one ankle reduced metabolic energy consumption by 24.2 ± 7.4% compared to no torque. The approach was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity. Finding a good generic assistance pattern, customizing it to individual needs, and helping users learn to take advantage of the device all contributed to improved economy. Optimization methods with these features can substantially improve performance.

Background: A cute stress symptoms (ASSs) are likely to be a common mental health problem in the acute period following the 2019 novel coronavirus pneumonia (COVID-19) outbreak. To address this risk, substantial social support has been provided to relieve ASSs during the COVID-19 outbreak. However, the effectiveness of these support methods for relieving ASSs has not yet been assessed. Objective: The current study sought to examine the underlying mechanisms by which social support relieves ASSs, among primary and secondary school teachers. Method: A sample of 751 primary and secondary school teachers was selected to answer self-reported questionnaires regarding ASSs, social support, psychological needs, and the sense of control. Results: Structural equation modelling revealed that social support had a non-significant direct relationship with ASSs, and a significant indirect relationship with reduced ASSs via the needs for autonomy and relatedness, and via the paths from three types of psychological needs to the sense of control. Conclusion: These findings suggest that social support can play a relieving role in ASSs when the support satisfies individuals' psychological needs and improves their sense of control. To alleviate ASSs among primary and secondary school teachers, more attention should be paid to satisfying their specific psychological needs and improving their sense of control.

In recent years, in the field of education, there has been a clear progressive trend toward precision education. As a rapidly evolving AI technique, machine learning is viewed as an important means to realize it. In this paper, we systematically review 40 empirical studies regarding machine-learning-based precision education. The results showed that the majority of studies focused on the prediction of learning performance or dropouts, and were carried out in online or blended learning environments among university students majoring in computer science or STEM, whereas the data sources were divergent. The commonly used machine learning algorithms, evaluation methods, and validation approaches are presented. The emerging issues and future directions are discussed accordingly.

Adolescents are spending considerable time on social media, yet it is unclear whether motivations for social media use drive different forms of social media engagement, and their relationships with body satisfaction and well-being. This study tested a proposed model of the relationships between motivations for social media use, types of social media engagement and body satisfaction and well-being. Responses to an online survey from 1432 Australian adolescents (Mage = 13.45 years, SD = 1.14, range 11–17; 55.4% boys) were collected. Structural equational modelling indicates excellent model fit. Specifically, motivations for social media use (information sharing, passing time, escapism, social interaction, social capital and appearance feedback) were associated with engagement (intensity, photo-based use, active use, passive use and liking use) and revealed mixed associations with body satisfaction and well-being. The findings support the importance of considering motivations for social media use in future research.

Learning is a natural human activity that is shaped by personal experiences, cognitive awareness, personal bias, opinions, cultural background, and environment. Learning has been defined as a stable and persistent change in what a person knows and can do. Learning is formed through an individual’s interactions, including the conveyance of knowledge and skills from others and experiences. So, learning is a personalized experience that allows one to expand their knowledge, perspective, skills, and understanding. Therefore, personalized learning models can help to meet individual needs and goals. Furthermore, to personalize the learning experience, technology integration can play a crucial role. This paper provides a review of the recent research literature on personalized learning as technology is changing how learning can be effectively personalized. The emphasis is on the terms used to characterize learning as those can suggest a framework for personalized and will eventually be used in meta-analyses of research on personalized learning, which is beyond the scope of this paper.

ObjectiveFaced with surging demand and Covid restrictions, our assessment process for young children with developmental needs and possible autism required transformation. Families told us that they wanted us to minimise assessment time and have their child’s individual needs considered.MethodsOur service is commissioned to provide assessments for possible autism in children referred before the age of 5 years. Previous assessments followed a standard format for all patients. This enabled a fixed yearly number of high-quality assessments but as referrals for assessment increased, capacity was insufficient, lengthening waiting times.Using existing staff with 4 additional weekly sessions of S+LT time, we developed a multi-faceted model of assessment which defined four possible assessment pathways (see figure 1). Using a structured report form, we gathered high-quality, detailed, observations from education staff focussing on DSM V defined features of autism spectrum disorder. Remote consultations were regularly used to gather information from parents. All patients were seen for at least one in-person assessment in a clinic setting. This model enabled assessments to continue throughout periods ofCovid restrictionsData on referral numbers and assessments completed were compared between two 48-month periods: pre-covid standard model 2018–2019 and new flexible model 2021–2022.We sought qualitative feedback from the MDT.ResultsNumbers of patients added to the assessment pathway and numbers of assessment completed were recorded by year (see graph 2).In the pre-covid 2018–2019 period, atotal of 540 patients were identified as needing formal assessment for autism through the multi-disciplinary assessment process (MDA). During that time468 assessments were concluded.In the 2021–2022 period, 846 patients were identified as needing formal assessment for autism through MDA. This represents a 56% increase in demand on the service. During that time 820 assessments were concluded, a 75% increase.MDT members reported liking the flexibility, panel discussions were good learning opportunities and consultants felt empowered to shorten assessments for children with classical features. Team members felt that their contribution was highly valued. Some MDT members noted that the workload was less predictable in their weekly timetable, and pressure of demand remains high.Abstract 121 Figure 1ConclusionWe have shown that a flexible patient-centred approach to autism assessment has resulted in more assessments being completed. The model is generally perceived positively by healthcare professionals. This, combined with modest additional staff time, should meet high demand efficiently.

This study examined predictors of access to early support amongst families of 0-6-year-old children with suspected or diagnosed developmental disabilities in the United Kingdom. Using survey data from 673 families, multiple regression models were fitted for three outcomes: intervention access, access to early support sources, and unmet need for early support sources. Developmental disability diagnosis and caregiver educational level were associated with intervention access and early support access. Early support access was also associated with child physical health, adaptive skills, caregiver ethnicity, informal support, and statutory statement of special educational needs. Unmet need for early support was associated with economic deprivation, the number of household caregivers, and informal support. Multiple factors influence access to early support. Key implications include enhancing processes for formal identification of need, addressing socioeconomic disparities (e.g., reducing inequalities, increasing funding for services), and providing more accessible services (e.g., coordinating support across services, flexible service provision).

The prevalence of bullying worldwide is high (UNESCO, 2018). Over the past decades, many anti-bullying interventions have been developed to remediate this problem. However, we lack insight into for whom these interventions work and what individual intervention components drive the total intervention effects. We conducted a large-scale individual participant data (IPD) meta-analysis using data from 39,793 children and adolescents aged five to 20 years (Mage = 12.58, SD = 2.34) who had participated in quasi-experimental or randomized controlled trials of school-based anti-bullying interventions (i.e., 10 studies testing nine interventions). Multilevel logistic regression analyses showed that anti-bullying interventions significantly reduced self-reported victimization (d =  − 0.14) and bullying perpetration (d =  − 0.07). Anti-bullying interventions more strongly reduced bullying perpetration in younger participants (i.e., under age 12) and victimization for youth who were more heavily victimized before the intervention. We did not find evidence to show that the inclusion of specific intervention components was related to higher overall intervention effects, except for an iatrogenic effect of non-punitive disciplinary methods–which was strongest for girls. Exploratory analyses suggested that school assemblies and playground supervision may have harmful effects for some, increasing bullying perpetration in youth who already bullied frequently at baseline. In conclusion, school-based anti-bullying interventions are generally effective and work especially well for younger children and youth who are most heavily victimized. Further tailoring of interventions may be necessary to more effectively meet the needs and strengths of specific subgroups of children and adolescents.