Large‑scale education surveys are a core asset for education policy making. They foster new analyses and findings about ways to improve the professionalism of teachers and school leaders, and the quality and equity of education around the world. The combination of several trends, including the increasing migration of surveys to digital devices and the technical progress made in data collection, storage and processing, are leading to unprecedented opportunities in the education sphere. Over the past two decades, the OECD Directorate for Education and Skills, together with its member and partner countries, has collected huge volumes of valid, reliable, comparable, rigorous, interpretable and policy‑relevant data on teachers, school leaders and students around the world. Let us take a moment to zoom in on the two largest OECD education surveys to date.

The results referred to in this volume are provided in Annex C via links to online tables.

There are many things we know about teachers. We know how much they are paid on average, we know roughly how much professional training they do each year, we know how many hours they spend teaching in class. But how much do we know about what teachers do in these classrooms that helps students do well academically, socially and emotionally?

This OECD report on the results of the TALIS‑PISA link 2018 focuses on the many ways teachers and schools matter for student achievement and social‑emotional development. This chapter provides an overview of the datasets upon which this report is based, as well as the statistical methods – including a machine learning technique – used to analyse them. It also provides an overview of the report’s main findings, followed by several directions for education policy. Finally, it offers recommendations for improving the current survey design in order to better examine and understand the connection between teaching and learning in the future.

We know that teachers and schools matter. However, there is less certainty about the specific characteristics and actions of teachers and school leaders that matter for student achievement. This chapter explores teacher and school factors that are significantly related to student achievement in the three subject domains covered by PISA: reading, mathematics and science. In order to best harness the richness of the TALIS‑PISA link data, the analysis is centred around a machine learning technique. While the chapter focuses mainly on the characteristics and practices of teachers and schools that matter for student performance in all three subjects, it also attempts to identify cross‑country patterns, differential teacher and school effects and the mediating effects of classmates’ characteristics.

This chapter begins by reviewing a broad range of student attitudes, behaviours and aspirations towards school in an attempt to identify those likely to vary significantly between schools. It then focuses on four social‑emotional outcomes for further analysis: students’ perceptions of their classroom climate, teachers’ enthusiasm for teaching, test performance, and students’ educational expectations. Using a machine learning technique, lasso, and traditional regression analyses, it then aims to identify teacher and school dimensions that are the most significantly related to these four student social‑emotional outcomes. For each of these outcomes, it attempts to identify cross‑country patterns and differential teacher and school effects, as well as the mediating effects of classroom composition.

Providing equal educational opportunities means that all students, irrespective of their gender or background, have the same chances of fulfilling their potential. However, despite significant efforts made by societies to narrow disparities in students’ outcomes in the recent past, gaps still persist. Drawing on the rich TALIS‑PISA link data, this chapter explores whether certain teacher and school factors that are identified in Chapter 2 of this report as key predictors of student achievement for average performing students also matter for low achievers and their high‑achieving peers. In addition, the chapter investigates the teacher and school factors that are significantly related to within‑school disparities in performance between girls and boys.