Understanding how the UAE K12 education system performs with data-driven evidence is key to inform better policy making to support UAE vision to upskill human capital growth for its economic transformation. In this study, we investigate the potential of using machine learning techniques to understand key learning factors contributing to UAE student math performance on the TIMSS 2019 assessment. Due to the fact that learning factors co-exist and interact with one another, we explore the SHapley Additive exPlanations (SHAP) approach to explain the complexity of the model. The results highlight the importance and contributions of each learning factor and uncover the relationships between the learning factors. Understanding key learning factors and identifying evidence-based intervention opportunities will help policymakers with informed education intervention designs to improve student mathematics learning, in order to improve UAE student TIMSS math performance over the long run.

Artificial Intelligence and globalization have been driving changes in how we live, what we do, how we interact with one another, and these trends have also been raising questions of what the international education systems should do to prepare children today to take on jobs that will emerge in the future and to train teachers and school leaders to be ready for future technology trends. Hence, it is important to understand what kind of tasks, skills and human agencies are being replaced or enhanced by AI already within the education system. In this paper, we present a summary of AI applications in the UAE K12 public school sector by Alef Education against the backdrop of the UAE National Strategies for AI 2031 and the UNESCO 2021 summary of global applications of AI in education. Additionally, this paper highlights Alef Education proof-of-concept industry take-aways, some future roadmap additions and long-term implications of AI on human development in education.

As our world goes through tremendous global and local changes over the last decades, international organizations (i.e. UN and OECD) and national governments have been adopting new policies and strategies across sectors to tackle issues of local and global significance such as digital & technological revolution, climate change, global pandemic, sustainable developments. Global competence has been coined by international education researchers as key 21st-century competencies for K12 learners to act as responsible global citizens to understand and take action against global and local shifts, as happenings and actions in one area will affect outcomes in others. This paper sets out to investigate evidence that supports intervention designs that incorporate global competence and other intertwined skills (i.e. metacognition & English reading) in the UAE K12 public-charter schools. Through the analysis using PISA 2018 global competence data and Machine Learning model, this paper found that global competence contributes to student academic performance, and there is an urgent need to build student awareness of global issues for UAE public schoolboys particularly in the Abu Dhabi emirate. Building stronger self-efficacy of global issues and enhancing student awareness of intercultural communication is more important for UAE public school students than private school students. This paper details evidence for intervention focus group and skill designs in Abu Dhabi public-charter school setting, connecting informal learning (i.e. Dubai Expo 2021 educational resources) with project-based formal school learning in order to cultivate global competencies, metacognition and reading literacies, with which students are able to recognize diverse perspectives, communicate ideas and collaboratively investigate and take action against local & global sustainable issues.

Grit, a non-cognitive skill that indicates perseverance and passion for long-term goals, has been shown to predict academic achievement. This paper provides evidence that grit also predicts student outcomes during the challenging period of the Covid-19 pandemic. We use a unique behavioral dataset from a digital learning platform in the United Arab Emirates to construct a new measure of grit. We find that controlling for baseline ability, students who were more gritty according to this measure before the pandemic, register lower declines in math and science scores during the coronavirus period. Using machine learning, behavioral data in the platform prior to the pandemic can explain 76% of variance in academic resilience. A survey measure of grit of the same students, on the other hand, does not have significant predictive power over performance changes. Our findings have implications for interventions on non-cognitive skills, as well as how data from digital learning platforms can be used to predict student behavior and outcomes, which we expect will be increasingly relevant as AI-based learning technologies become more common.