It is becoming increasingly vital for the next generation of students to acquire problem-solving, critical thinking, and collaborative skills for them to be successful in the 21st century. The use of technology greatly bolsters the integration of these skills. Robotics, one of the many emerging arrays of technologies, presents learners with challenges and opportunities for developing innovative ideas, critical thinking, and higher-order thinking skills. As a result, the Ministry of Education in Dubai realized these potentials and took an essential step in the form of the distribution of Lego Mindstorms Education kits to schools with the goal of encouraging teachers to use these kits in their classrooms. This research study aimed to investigate teachers’ perceptions of a training on Lego Mindstorms in terms of content, methodology, activities, and recommendations. In addition, the study investigated how teachers perceived Lego Mindstorms based on TAM model. A total of 59 high school (cycle 2) teachers participated in the current study. The data from the teachers were collected using both quantitative and qualitative approaches. Data analysis consists of descriptive statistics and thematic analysis. Teachers showed positive perceptions of robotics integration, emphasizing its potential to enhance teaching and learning. Their willingness to learn and adapt, combined with their recommendations for enhanced training methods, highlights the importance of continuous professional development for effective robotics integration. Teachers expressed the need for more practical training, hands-on activities, and a balance between theoretical and practical aspects. A positive correlation between perceived learning usefulness, perceived teaching usefulness, perceived ease of use, attitudes, and teachers’ intention to use Lego Mindstorms in their future teaching was found.

This study is to evaluate the performance of fully Bayesian information criteria, namely, LOO, WAIC and WBIC in terms of the accuracy in determining the number of latent classes of a mixture IRT model while comparing it to the conventional model via non-random walk MCMC algorithms and to further compare their performance with conventional information criteria including AIC, BIC, CAIC, SABIC, and DIC. Monte Carlo simulations were carried out to evaluate these criteria under different situations. The results indicate that AIC, BIC, and their related CAIC and SABIC tend to select the simpler model and are not recommended when the actual data involve multiple latent classes. For the three fully Bayesian measures, WBIC can be used for detecting the number of latent classes for tests with at least 30 items, while WAIC and LOO are suggested to be used together with their effective number of parameters in choosing the correct number of latent classes.

The focus of this study is to use the mixture item response theory (MixIRT) model while implementing the no-U-turn sampler as a technique for investigating the presence of latent classes (i.e., subpopulations) among eighth-grade students who were administered TIMSS 2019 mathematics subtest in paper format from the gulf cooperation council (GCC) countries. One-, two-, and constrained three-parameter logistic MixIRT models with one to four classes were used to fit to the data, where the model data fit was assessed using Bayesian fit indices. The results indicate that multiple latent classes or subpopulations can better reflect the mathematical proficiency of eighth graders from the four GCC countries, and specifically the two-class constrained three-parameter MixIRT model provides a relatively better fit to the data. The results also indicate that when a mixture of several latent classes present, the conventional unidimensional IRT model is limited in providing information for multiple latent classes and shall be avoided. In addition to adding to the existing literature on MixIRT models for international large-scale assessments such as TIMSS on its heterogenous subpopulations from a fully Bayesian approach, this study sheds light on the limitation of conventional unidimensional IRT models and subsequently directs attention to the use of the more complex MixIRT model for such assessments.

This systematic review examines how collegial supervision is conceptualised and operationalised in schools, its reported relationship with teacher professional learning (TPL), the conditions shaping that relationship, and the limitations of the existing evidence. Following PRISMA 2020 guidelines, eight databases were searched for English-language scholarly publications published between 2010 and July 2026. Thirty-three sources met the eligibility criteria and were included following methodological appraisal. Practices were identified in the studies as coaching, mentoring, professional discussion, feedback, observation, and supervision. Professional development processes included professional identity, agency, teaching, learning, and reflection. Relationships and organisational factors were seen as potentially affecting the implementation of supervision. There was no evidence of an effect of peer supervision on TPL, nor of a distinct causal pathway. This review identified three characteristics of effective collegial supervision: attention to teaching practice, structured analysis of evidence within practice, and evidence-informed reflection and feedback. These characteristics set collegial supervision apart from broader forms of professional collaboration. This review also suggests a school leader-influenced framework that defines the organisational contexts in which collegial supervision can contribute to TPL.