Executive summary

Remote schooling during the early months of the COVID-19 pandemic salvaged education for many students. But online classes did not work for everyone. While differences in access to the Internet and digital tools have been shrinking across the OECD, PISA 2018 shows a persistent gap between disadvantaged students with lower digital skill levels and advantaged students who readily use the Internet. This contributed to greater learning losses during the pandemic for vulnerable students. As society and schools move back to more normal functioning, we will need our best teachers and digital resources to help students find their footing again. But what constitutes “good” or effective teaching? And are good teachers working in the schools that really need them?

Chapter 1 lays bare the problematic at hand: how much access do disadvantaged students have to the effective teaching, and digital equipment and infrastructure that can help them overcome difficulties they have no power over and yet, which may be holding them back?

Chapter 1 also identifies how teachers with certain characteristics like experience and self-efficacy, and practices like cognitive activation are distributed through the different kinds of schools in countries’ education systems: schools with student populations that are socio-economically advantaged or disadvantaged; public or private; rural or urban. Dissimilarity indices show the degree of clustering there is.

But what exactly constitutes good teachers and good teaching? Chapter 2 lays out good teacher characteristics and practices. TALIS 2018 survey data reveal marked unevennesses in distribution; for example, of experienced teachers who are clustered in socio-economically advantaged schools in nearly one-third of countries that participated in TALIS. This has consequences, especially for the reading proficiency of disadvantaged students: it tends to be higher in education systems in which experienced teachers are more evenly distributed across schools.

Another Chapter 2 highlight is that teachers who make the most of actual teaching time during class tend to teach in socio-economically advantaged schools, again, in more than one-third of countries that participated in TALIS. That said, it is not necessarily the case that teachers who know how to optimise actual teaching time are distributed more heavily in advantaged schools. It is also possible that teachers in schools whose students are, for the most part, less well-off are unable to maximise their teaching time because classes are frequently disrupted by disciplinary problems.

Chapter 3 turns to digital matters and equity. It looks at Internet connectivity and digital equipment in schools, and teachers’ digital skills, training, self-efficacy and use in their teaching. Unsurprisingly, schools that have such inadequate digital resourcing that it hinders good teaching fall more often in the public sphere than private, and in rural areas more than cities. They also have a mostly disadvantaged student population. But simple access to information and communication technology (ICT) on its own is not enough for students to develop digital skills such as accurately detecting biased information on the Internet. Students need guidance from teachers who are trained in and at ease with the technology, and use it regularly. TALIS 2018 data show that digitally savvy teachers are more likely to teach in private schools in almost a quarter of countries and economies participating in TALIS. But, Chapter 3 cautions, upgrading schools’ ICT infrastructure and redistributing teachers isn’t enough to give students a fair chance at digital learning. What does help? Collaboration between teachers. TALIS data show that when teachers work on projects together, their use of digital technologies in the classroom goes up on average across OECD countries.

What is the link between inequality of access to strong teachers and the difference in learning between advantaged and disadvantaged students? Chapter 4 delves into this, noting that countries and economies with uneven distributions of experienced teachers also obtained lower average scores in the PISA 2018 reading assessment. A similar observation is made of education systems in which teachers with thorough training as well as teachers who are skilled at optimising class time are unevenly allocated, and in all three cases, especially for disadvantaged students. In terms of students’ digital literacy, it is access to teachers with high digital self-efficacy that is key. Disadvantaged students have opportunities to learn digital skills that are better or just as good as advantaged students when teachers who are confident about using ICT are distributed more evenly.

Chapter 4 also looks at potential ways education systems can get more good teachers to teach in the disadvantaged schools that need them most. School autonomy shows some promise here, though there are often other factors at work as well. Systems that give schools more leeway in hiring and firing teachers and setting salaries seem to have a more equal distribution of their best teachers. Factors, like experience, which carry great weight in teacher distribution in centralised education systems are of less importance in more localised ones, rendering teacher experience a strength that is more on par with a wider range of criteria. Schools often do a better job than education systems of identifying teacher strengths that, unlike years of experience, are difficult to pinpoint. In addition, the more autonomy school leaders have in adapting teachers’ pay to reflect the difficulty of tasks, the better schools are able to attract the strongest teachers to the most challenging classrooms.

Still, there are caveats to granting schools more autonomy over staffing decisions. Increased autonomy requires more stringent accountability measures, and mechanisms to help disadvantaged schools compete against advantaged ones in attracting talented teachers. Progressive school funding models, such as those in Sweden, allow disadvantaged schools to pay higher salaries. Often, it is not financial incentives that attract better teachers to disadvantaged schools but fast-tracked teacher career progression: this is a feature of systemic policies, such as Shanghai’s (China). Lastly, controlled-choice models, such as those in the Flemish Community of Belgium, are another example of ways to achieve a more equitable distribution of effective teachers: granting parents the freedom to choose the school for their children while letting schools be involved in the recruitment of teachers incentivises schools to seek out specific teacher characteristics to meet school needs. In this model, schools’ freedom in recruiting staff is accompanied by additional accountability measures and funding based on need.

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