Digitalisation can transform education for the better. Using technology in school can improve teaching and learning, and help students acquire a broader range of skills. Yet, digital technology can also increase inequalities. Those who have limited access to information and communication technology (ICT), are not digitally literate, or unable to navigate in the digital world, are left behind. This chapter examines the distribution of teachers who are trained and feel self-efficient in and regularly use ICT across schools. It also looks at how ICT infrastructure is distributed across schools. Lastly, the chapter explores the type of schools (and students) that are more likely to benefit from the resources needed for effective digital learning.
Mending the Education Divide
3. Do students have equitable access to digital learning in school?
Abstract
Highlights
The share of principals who report that the school’s capacity to provide quality instruction is hindered by insufficient Internet access or the shortage or inadequacy of digital technology for teaching is greater in socio‑economically disadvantaged than advantaged schools, on average across OECD countries. It is also larger in public than in private schools. And the share of schools where teaching is hampered by insufficient Internet access is higher in rural schools than those located in cities.
Teachers who are trained in and feel capable of using information and communication technologies (ICT) and who regularly let students use ICT for projects or class work are not randomly distributed across schools. There is evidence of clustering of such teachers in all Teaching and Learning International Survey (TALIS) participants.
The share of teachers who feel they can support student learning through the use of digital technology is larger in private than in public schools in almost a quarter of countries and territories participating in TALIS. It is also larger in socio‑economically advantaged than in disadvantaged schools in seven education systems.
The share of teachers who often let students use ICT for projects or class work is larger in private schools than in public schools in almost a quarter of countries and territories participating in TALIS.
Across all TALIS participants, except for Malta, differences between schools in the frequency of ICT use remain significant after controlling for teacher characteristics such as teaching experience, self‑efficacy, initial education and continuous professional development in the use of ICT. Differences remain significant after controlling for schools’ digital infrastructure as well. Thus, reallocating teachers and improving schools’ ICT infrastructure may not be sufficient in addressing inequities in students’ access to digital learning in school.
The more often teachers participate in professional collaboration, the more likely they are to regularly let students use ICT for projects or class work. Not only does digital technology encourage teachers to collaborate by providing better tools to do it but collaboration itself helps boost the use of ICT in school.
Introduction
Information and communication technologies (ICTs) have been transforming the way people live, work and learn. Digital transformation shows great potential for economies and societies to boost productivity and improve well‑being. Education systems are no exception. Digitalisation can improve education delivery with the help of artificial intelligence, learning analytics, robotics, etc. (OECD, 2021[1]). Notably, the use of digital technology for teaching and learning at school can help students acquire digital skills, social‑emotional skills and more standard cognitive skills such as numeracy and literacy (Bulman and Fairlie, 2016[2]).
Using ICT in the classroom can improve student outcomes in various ways. It can provide self‑paced and individualised instruction; access to information and specialised materials well beyond what textbooks can offer; better tools for collaborative work; project‑based and inquiry‑based pedagogies; and increased students’ engagement given the interactive nature of its tools (Bulman and Fairlie, 2016[2]; OECD, 2015[3]). Yet, evidence of the positive effect of ICT use at school on student outcomes is mixed (Bulman and Fairlie, 2016[2]). According to an OECD report based on Programme for International Student Assessment (PISA) 2012 data, while “… limited use of computers at school may be better than not using computers at all, using them more intensively than the current OECD average tends to be associated with significantly poorer student performance.” (OECD, 2015, p. 16[3]). More recent research also shows that students who use ICT a lot or a little tend to have lower levels of reading achievement than students who have middling use of digital technology (Borgonovi and Pokropek, 2021[4]). Hence, the use of ICT at school does not automatically lead to better student outcomes. Past studies highlight that, at the classroom level, the frequency and effectiveness of digital technology use is often related to teachers’ training in ICT, teachers’ ability to integrate ICT into their teaching process, teachers’ collaboration with other teachers as well as teachers’ perceived self‑efficacy and beliefs about teaching (Comi et al., 2017[5]; Ertmer et al., 2012[6]; Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]; Voogt et al., 2013[8]).
Although evidence on the effect of ICT use in class on student outcomes is mixed, effective use of ICT at school can help students acquire digital skills (Bulman and Fairlie, 2016[2]). There is empirical evidence based on PISA data of a positive association between students’ access to digital learning at school and students’ digital skills1 (OECD, 2021[9]; OECD, 2015[3]).
As much as technology can improve people’s life, it can also increase inequalities. Those who have limited access to ICT, are not digitally literate and do not possess a certain level of cognitive skills may be unable to navigate in the digital world and hence are left behind. Inequities in access to and proficiency in ICT, in particular between socio‑economically advantaged and disadvantaged students, have long been of interest to educational policy (OECD, 2015[3]). The COVID‑19 pandemic exposed the challenges education systems face in addressing digital divides and drew further attention to this issue. The unprecedented disruption in the form of school closures and the subsequent switch to distant learning revealed many inadequacies from access to broadband and computers needed for online education to teachers’ and students’ ability to engage in online learning (OECD, 2021[10]; OECD, 2021[11]). The pandemic also showed how students from marginalised backgrounds, who have limited access to digital learning resources, lack support from their parents or are simply less motivated to learn on their own, can fall behind in digital education (Schleicher, 2020[12]).
In parallel with the steady increase in the use of ICT both at home and at school, socio‑economic differences in access to computers and the Internet have decreased in most countries and territories participating in PISA (OECD, 2020[13]; OECD, 2019[14]; OECD, 2015[3]). Yet, PISA 2018 data show that portable computers and Internet access are still more widespread in socio‑economically advantaged than disadvantaged schools on average across OECD countries (OECD, 2020[13]). More importantly, equal access to ICT tools does not necessarily lead to equal opportunities in leveraging digital technology. Investing in schools’ ICT equipment alone is not enough to improve students’ digital skills (Fraillon et al., 2019[15]). Students from socio‑economically disadvantaged backgrounds may not have the necessary knowledge, skills and motivation to make the most of what technology has to offer as they tend to spend less time reading on line and obtaining practical information from the Internet (OECD, 2015[3]). Indeed, differences in ICT use are related to differences in students’ digital skills (OECD, 2015[3]). A recent meta‑analysis looking at the relation between students’ socio‑economic status and ICT literacy shows that students from more affluent families tend to perform better on tasks related to computer skills than their peers from disadvantaged socio‑economic background (Scherer and Siddiq, 2019[16]). But it has to be noted that reducing inequalities in the ability to benefit from digital tools requires first and foremost that all students reach a baseline level of proficiency in basic skills such as reading and mathematics (OECD, 2015[3]; OECD, 2019[14]).
There is evidence in the literature that access to and use of ICT can have different effects on students’ test scores and digital skills depending on student characteristics. Based on the results of randomised experiments conducted in India, Banerjee et al. (2007[17]) found that computer‑assisted learning programmes benefit lower‑performing students more than high performers. Analysing PISA 2012 data, Tan and Hew (2017[18]) conclude that access to ICT accounts for a larger share of the variance in achievement for disadvantaged students than for students from advantaged families. Similarly, there is empirical evidence suggesting that students from more disadvantaged backgrounds rely more on their teachers to learn digital skills than their peers from affluent families (Berger, 2019[19]). And access to and use of ICT at school helps students from immigrant backgrounds narrow the achievement gap (Kim, 2018[20]). Looking beyond the use of ICT, Gómez‑Fernández and Mediavilla (2021[21]) find that the positive association between students’ ICT interest and academic performance is greatest in the case of worst‑performing students. Thus, low‑performing students and those from disadvantaged backgrounds may benefit the most from being exposed to digital learning at school.
Building on TALIS 2018 data, this chapter analyses students’ access to digital learning in school from two different angles:
Equality: By investigating the extent to which teachers who are trained in and feel capable of using technology and those who use ICT for teaching on a regular basis are equally allocated across schools, the chapter addresses issues related to equality. This analysis focuses only on teacher characteristics and disregards student characteristics, as well as the fact that students themselves sort across schools based on their personal characteristics (OECD, 2019[22]). The analysis related to equality is based on the dissimilarity index (see Box 2.1 in Chapter 2 for more detail), which captures the extent to which the distribution of teachers departs from what would be observed if teachers were allocated across schools in a perfectly random way.
Equity (or fairness): Providing equal resources to all students irrespective of their characteristics by randomly assigning teachers to schools may not effectively address equity concerns. Therefore, the chapter also examines the type of schools in which resources needed for effective digital learning tend to concentrate. These resources include ICT infrastructure and teachers who are trained in and feel capable of using digital technology and use ICT for instruction on a regular basis. Thus, the chapter also addresses equity (or fairness) issues in relation to digital learning (referred to as “digital divides” hereafter). In this context, the notion of equity (or fairness) refers to providing the opportunity for all students to realise their potential by removing obstacles over which individual students have no control such as unequal access to resources and practices related to digital learning. School systems that are able to weaken the link between education outcomes and individual circumstances such as students’ socio‑economic status, gender or immigrant background are considered equitable (OECD, 2019, p. 42[23]).
The two angles, equality and equity, are complementary. Although the analysis looking at equality in students’ access to digital learning at school disregards the characteristics of students, it can still identify the teacher characteristics and practices along which teachers tend to sort across schools. The dissimilarity index can highlight overall imbalances in teacher allocation. On the other hand, analysis focusing on equity draws a more detailed picture of teacher allocation. Notably, it examines how teachers with certain characteristics and practices are distributed across different types of schools.
Box 3.1. What can TALIS say about digital divides?
Schools’ ICT infrastructure
Digital learning in school requires adequate ICT infrastructure such as software, computers, laptops, smart boards and sufficient Internet access. TALIS asks school leaders’ views about the extent to which the school’s capacity to provide quality instruction is hindered by inadequate digital technology for teaching or Internet access.
Teacher characteristics in relation to ICT use for instruction
TALIS collects data on various teacher characteristics that can be considered proxy measures for teachers’ ability to integrate ICT in their teaching in an effective manner. These include: teachers’ formal education and training, continuous professional development and self‑efficacy in the use of ICT for instruction.
Teaching practices in relation to ICT use for instruction
Addressing digital divides not only requires adequate digital infrastructure in schools and teachers who are trained in and feel capable of using ICT but teachers who use it in their teaching on a regular basis. TALIS asks teachers how often they let students use ICT for projects and class work.
Given that the analysis aims at informing policies about the allocation of teachers in order to achieve more equitable outcomes for students, the distinction between teacher characteristics and teaching practices is particularly relevant. Teacher characteristics are portable assets that teachers possess irrespective of the schools they work at. In contrast, teaching practices are assumed to be an explicit choice made by teachers depending on the context in which the instruction takes place. Hence, teachers may adopt different practices in a different school, or even with different students in the same school.
It is also important to note that the implicit assumption underlying the analysis is that all students in a given school have access to teachers in equal measure (or, equivalently, that students are randomly sorted into classes). The validity of this assumption varies across countries depending on the particular institutional arrangements governing class formation, the assignment of teachers to classes, and whether such arrangements change from grade to grade.
School characteristics along which equity issues are analysed
The chapter explores certain school characteristics along which equity issues in relation to students’ access to digital learning at school can arise. Based on the TALIS principal questionnaire, the main school characteristics included in the analysis are: socio‑economic composition of the student body (i.e. socio‑economically disadvantaged schools versus advantaged schools);1 school location (i.e. schools located in cities versus rural schools);2 school governance (i.e. privately managed schools versus publicly managed schools).3 Schools located in rural areas are smaller, have lower student‑teacher ratios, often cater to students with particular socio‑economic profiles and may face a distinctive set of challenges (Echazarra and Radinger, 2019[24]). Urban and rural schools can differ in their ability to attract and retain teachers (OECD, 2018[25]). In many countries, the type of school management (i.e. private versus public) can also be an important factor in explaining the segregation of students according to their socio‑economic background (OECD, 2019[22]). Differences in terms of the student composition of schools according to language background and special education needs are also included in the tables (see Annex C) and commented on whenever certain cross‑country patterns are observed. (The share of teachers and schools by each school type are presented in Tables A.B.2 and A.B.3 in Annex C.)
It is important to note, however, that the analysis is purely descriptive and focuses on a single characteristic of the school (be it student composition, location, or type of governance). Schools can differ in many potentially important ways that explain the differences in the characteristics and practices of teachers working in a certain school. For instance, rural schools tend to be smaller than urban schools. School size can be, in itself, a factor driving teachers’ application decisions. The results of the analysis should not then be interpreted in a causal sense (as school characteristics determining the prevalence of certain teachers), and should be complemented with country‑specific information about the specific structure of the education system.
1. A socio economically disadvantaged school is a school where the concentration of students from socio economically disadvantaged homes is above 30%. A socio economically advantaged school is a school where the concentration of students from socio economically disadvantaged homes is 10% or less. Socio economically disadvantaged homes are homes lacking the basic necessities or advantages of life, such as adequate housing, nutrition or medical care.
2. A city school is a school that is located in a city with over 100 000 habitants. A rural school is a school that is located in a rural area or a village with up to 3 000 habitants.
3. A privately managed school is a school whose principal reported that it is managed by a non governmental organisation (e.g. a church, trade union, business or other private institution). In some countries, the privately managed schools category includes schools that receive significant funding from the government (government dependent private schools). A publicly managed school is a school whose principal reported that it is managed by a public education authority, government agency, municipality, or governing board appointed by the government or elected by public franchise. In the principal questionnaire, this question does not make any reference to the source of the school’s funding, which is reported in the preceding question.
This report draws on data collected in 2018,2 i.e. before the outbreak of the COVID‑19 pandemic. Obviously, today’s reality with respect to teachers’ ability to integrate ICT in teaching and learning as well as schools’ digital infrastructure is different from what it was before school closures. Prior to the pandemic digital technology was one of many tools teachers could rely on. However, with school closures, ICT became the only tool at teachers’ disposal to teach their students. As teachers and students had to adapt to distant learning, both the frequency of ICT use as well as teachers’ capacity to use technology has increased since the start of the pandemic (OECD, 2021[11]; OECD, 2021[10]). Many education systems also enhanced teacher training in using digital tools and invested in ICT equipment as well as digital learning platforms (OECD, 2021[10]). Yet, the evidence available so far indicates that digital divides still loom. Although many countries implemented remedial measures targeting disadvantaged students such as mentoring and homework support, there is evidence from various countries that learning losses during school closures were the most severe among marginalised students (OECD, 2021[10]). Studies from England (United Kingdom), France and the Netherlands show that, due to school closures, disadvantaged students suffered greater learning losses than their peers (OECD, 2021[10]). With the pandemic putting the spotlight on inequalities in digital learning, TALIS 2018 data give insights into the extent and nature of these digital divides.
The chapter is organised as following: first, it looks at the extent to which schools with different characteristics provide effective learning environments for digital learning, including adequate ICT equipment and sufficient Internet access. Second, the chapter examines how evenly teachers who are trained in and feel capable of using ICT are distributed across schools. This section also looks at how much schools with different characteristics differ in terms of the share of teachers who are trained in and feel capable of using ICT. The last section examines the distribution of teachers who regularly use ICT for teaching across schools. It also looks at how much schools with different characteristics differ in the share of teachers who use digital technology for instruction on a regular basis. The last section concludes by exploring how much teacher characteristics and school resources in ICT infrastructure explain differences across schools in the use of ICT.
Do students have access to ICT equipment and Internet in school?
Digital learning in school requires adequate ICT infrastructure such as software, computers, laptops, smart boards and sufficient Internet access. On average across the OECD, the share of teachers who feel they can support student learning through the use of digital technology “quite a bit” or “a lot” is 7 percentage points lower in schools in which teaching is hindered by the lack of digital infrastructure (Table 3.1).3 And the percentage of teachers with high self‑efficacy in the use of ICT in the classroom is 6 percentage points lower in schools that have insufficient Internet access on average across the OECD. Moreover, the share of teachers who “frequently” or “always” let students use ICT for projects or class work is 5 percentage points lower in schools with inadequate digital infrastructure (Table 3.2). In Australia, Sweden and the United States, the difference between schools with adequate digital infrastructure and those without is 20 percentage points or more. Similarly, the percentage of teachers who often use ICT for teaching is 4 percentage points lower in schools that have insufficient Internet access on average across the OECD.
Equipping schools with ICT tools and Internet access has been an explicit goal of education policy in many OECD countries (OECD, 2019[14]). As a result, the computer‑student ratio has increased between 2009 and 2018 in most countries and territories that participate in PISA. Access to the Internet has also become virtually universal in most education systems (OECD, 2020[13]). One reason why education systems invest in schools’ ICT infrastructure is to compensate for disadvantaged students’ limited access to ICT tools and the Internet at home (Bulman and Fairlie, 2016[2]; OECD, 2015[3]). Yet, unequal access to ICT infrastructure across schools with different characteristics remains a concern for policy makers. For instance, as revealed by PISA 2018 data, socio‑economically advantaged schools tend to have larger shares of portable computers and computers connected to the Internet compared to disadvantaged schools on average across OECD countries (OECD, 2020[13]).
Differences in students’ access to ICT equipment across schools
TALIS findings show that in schools with a large concentration of socio‑economically disadvantaged students (i.e. more than 30%), the shortage or inadequacy of digital technology such as software, computers, laptops, and smart boards is more likely to hamper the quality of instruction. On average across OECD countries and territories, the share of principals reporting that the school's capacity to provide quality instruction was hindered by inadequate digital technology for instruction is 9 percentage points higher in socio‑economically disadvantaged schools than in advantaged schools (Table 3.3). The countries and territories with the largest differences include Ciudad Autónoma de Buenos Aires (hereafter CABA [Argentina]) (69 percentage points), Mexico (50 percentage points), South Africa (41 percentage points) and Colombia (40 percentage points). There are only three countries and territories where more challenging school environments are compensated by the availability of digital technology: these are Japan, Shanghai (China) and Sweden.
Students’ access to ICT equipment also depends on whether they attend publicly or privately managed schools. In more than one‑third of the countries and territories with available data, the share of principals who reported that the school's capacity to provide quality instruction was hindered by inadequate digital technology for instruction is higher in public schools than in private schools (Figure 3.1). On average across the OECD, the share of principals reporting this is 12 percentage points higher in publicly managed schools than in privately managed schools. This difference exceeds 50 percentage points in CABA (Argentina), Mexico and Viet Nam. As private schools tend to be more affluent, they have more resources to maintain and improve schools’ ICT equipment.
In a couple of countries participating in TALIS, rural schools are more likely to have inadequate digital infrastructure than schools located in cities. Notably, the share of principals who reported that the school's capacity to provide quality instruction was hindered by inadequate digital technology for instruction is between 21 and 31 percentage points higher in rural schools than in city schools in Bulgaria, Colombia, Kazakhstan, Russian Federation and the United Arab Emirates (Table 3.3). This may be explained by the disadvantage rural schools tend to face when it comes to school funding. Namely, funds allocated to rural schools, which are primarily based on student enrolment, usually do not reflect the higher costs of delivering education programmes and services in remote areas (OECD, 2017[26]). Moreover, in some education systems, school funding by local authorities is highly dependent on the local tax base, which tends to be lower in rural areas (Echazarra and Radinger, 2019[24]). The reverse pattern is observed in one country. In Austria, the share of principals who reported that the school's capacity to provide quality instruction was hindered by inadequate ICT equipment is 34 percentage points higher in cities than in rural areas. These results for Austria are in line with PISA 2018 data showing that shortages of material resources are perceived to be more of an issue in urban schools than in rural schools (OECD, 2020[13]).
Figure 3.1. Shortage or inadequacy of digital technology, by school type
Note: Statistically significant differences between privately managed schools and publicly managed schools are shown next to the country/territory name (see Annex B).
1. A publicly managed school is a school whose principal reported that it is managed by a public education authority, government agency, municipality, or governing board appointed by government or elected by public franchise.
2. A privately managed school is a school whose principal reported that it is managed by a non‑governmental organisation (e.g. a church, trade union, business or other private institution).
Countries and territories are ranked in descending order of the percentage of principals who report that the school’s capacity to provide quality instruction is currently hindered “quite a bit” or “a lot” by shortage or inadequacy of digital technology for instruction.
Source: OECD, TALIS 2018 Database, Table 3.3.
Differences in students’ access to Internet across schools
In various education systems, socio‑economically disadvantaged schools are more likely to face issues with Internet access that hinder the quality of instruction than advantaged schools. On average across OECD countries and territories, the share of principals reporting that the school's capacity to provide quality instruction was hindered “quite a bit” or “a lot” by insufficient Internet access is 9 percentage points higher in socio‑economically disadvantaged schools than in advantaged schools (Table 3.4). The countries and territories where the largest differences in the availability of ICT equipment are observed between advantaged and disadvantaged schools are also the ones with the largest differences in terms of adequate Internet access. These are CABA (Argentina) (67 percentage points), Colombia (54 percentage points), Mexico (41 percentage points) and South Africa (30 percentage points). It is only in Shanghai (China) where the share of principals reporting insufficient Internet access as a factor hampering quality instruction is higher in schools attended by more advantaged students than in disadvantaged schools.
According to TALIS findings, students are more likely to have sufficient Internet access in private schools than in public schools. This holds in half of the countries and territories with available data. On average across the OECD, the share of principals who reported that the school's capacity to provide quality instruction was hindered “quite a bit” or “a lot” by insufficient Internet access is 14 percentage points higher in public schools than in private schools (Table 3.4). The largest differences (above 45 percentage points) can be observed in Latin American countries and territories such as CABA (Argentina), Colombia and Mexico. As for ICT equipment, in most education systems, private schools tend to have more resources in providing adequate Internet access for teachers and students.
In many education systems, providing adequate Internet access is more challenging in schools located in rural areas than those in cities. On average across OECD countries and territories, the share of principals who reported that the school's capacity to provide quality instruction was hindered by insufficient Internet access is 7 percentage points higher in rural schools than in schools located in cities (Figure 3.2). This difference between rural and city schools is above 40 percentage points in Alberta (Canada), Colombia, Italy and Mexico. These results may reflect the general gaps in connectivity and Internet access that persist between urban and rural areas in virtually all countries (International Telecommunication Union, 2020[27]). A reverse pattern is observed in only one country. Similar to school resources with respect to ICT equipment, in Austria the share of principals who reported that the school's capacity to provide quality instruction was hampered by insufficient Internet access is higher (by 21 percentage points) in city schools than in rural schools.
Figure 3.2. Insufficient Internet access, by school location
Note: Statistically significant differences between privately managed schools and publicly managed schools are shown next to the country/territory name (see Annex B).
Countries and territories are ranked in descending order of the percentage of principals who report that the school’s capacity to provide quality instruction is currently hindered “quite a bit” or “a lot” by insufficient Internet access.
Source: OECD, TALIS 2018 Database, Table 3.4.
Do students have access to teachers who are trained and feel self‑efficient in the use of ICT?
Adequate ICT infrastructure is essential for effective digital learning in school. However, it is equally important that students have access to teachers who are trained in and feel capable of using ICT. Past studies have shown that access to technology alone will not improve student learning; effective integration of technology into teaching and learning requires teachers who are well‑trained and able to use digital tools for instruction (Fraillon et al., 2019[15]; OECD, 2021[9]; OECD, 2019[14]; OECD, 2015[3]). However, teachers can only integrate technology into their teaching if they themselves acquire basic digital skills and are competent enough to tailor technology use to their own teaching (OECD, 2019[14]). Teachers can improve their digital skills in their initial education and training and as part of their in‑service professional development activities. Pre‑ and in‑service training can also inform teachers about pedagogical practices that work well with digital tools. For instance, a past PISA‑based study showed that teachers who rely on certain teaching practices such as inquiry‑based, project‑based, problem‑based or co‑operative pedagogies tend to be more successful in integrating new technologies into their teaching (OECD, 2015[3]). Technology use seems especially effective when it is blended with innovative teaching and learning methods such as gamification or flipped classes (Paniagua and Istance, 2018[28]; Peterson et al., 2018[29]). Besides teachers’ training in the use of ICT, the literature consistently highlights the positive relationship between teachers’ perceived self‑efficacy and their use of digital technology in the classroom (Drossel, Eickelmann and Gerick, 2016[30]; Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]; Hatlevik and Hatlevik, 2018[31]; Hsu, 2016[32]; Nikolopoulou and Gialamas, 2016[33]). Thus, both teachers’ training and perceived self‑efficacy in ICT use are important factors to consider when analysing digital divides.
Allocation of teachers trained in the use of ICT as part of initial education and training
In line with the research literature that suggests a positive relationship between the inclusion of ICT use in teachers’ initial education and training and the use of ICT in school (Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]; Tondeur et al., 2018[34]), past analyses of TALIS 2018 data show that pre‑service teacher education and training is an important driver of teachers’ adoption of digital technology for their teaching activities (OECD, 2020[35]). In almost one‑third of TALIS countries and territories with available data, teachers are more likely to let students “frequently” or “always” use ICT for projects or class work when ICT for teaching was included in their formal education and training (OECD, 2020[35]).
TALIS not only collects data on the content of teachers’ initial education and training but also allows the quality of pre‑service education to be gauged. Notably, TALIS asked teachers how well prepared they felt for the use of ICT in relation to their education and training (i.e. “not at all”; “somewhat”; “well”; and “very well)”. On average across OECD countries, 56% of teachers received training in the use of ICT in their initial education (Figure 3.3) and 43% felt well or very well prepared for the use of technology (Table 3.6). There is, however, substantial variation between countries and territories.
Figure 3.3. Teachers’ formal training in the use of ICT for teaching, by school characteristics
* For this country, estimates for sub‑groups and estimated differences between sub‑groups need to be interpreted with great care. See Annex A for more information.
Note: ICT refers to information and communication technology.
1. High concentration of disadvantaged students refers to schools with more than 30% of students from socio‑economically disadvantaged homes. Low concentration of disadvantaged students refers to schools with less than or equal to 10% of students from socio‑economically disadvantaged homes.
Countries and territories are ranked in descending order of the share of teachers for whom the use of ICT for teaching was included in their formal education or training.
Source: OECD, TALIS 2018 Database, Table 3.5.
The share of teachers who were trained in using digital technology for teaching during their formal education and training varies considerably (between 37% and 97%) across TALIS participants (Figure 3.3). The countries and territories where the large majority of teachers (more than 75%) received this type of initial training include Chile, Kazakhstan, Mexico, Singapore, Shanghai (China), the United Arab Emirates and Viet Nam. Similar to the inclusion of ICT use in formal education, the percentage of teachers who felt well prepared for the use of digital technology also varies across countries (between 20% to 86%) (Table 3.6). In Mexico, the United Arab Emirates and Viet Nam, more than 75% of teachers felt prepared in using ICT for teaching.
One way to examine whether students have access to teachers who were trained in the use of digital technology for teaching during their formal education and training is to look at whether the allocation of teachers trained in ICT use in a country’s schools resembles the teacher population of the country. The dissimilarity index is a commonly used measure to analyse deviation from evenness (see Box 2.1 in Chapter 2 for more detail). It indicates the average proportions of teachers from both groups (i.e. teachers who were trained in the use of ICT and those who were not) that would need to be reallocated in order to obtain a distribution of teachers from these groups across all schools that is identical to the overall distribution within the country, assuming that school size in terms of the number of teachers working in the school is fixed. Alternatively, assuming that school size can be adjusted, the dissimilarity index can be interpreted as the proportion of one or the other group that has to be reallocated in order to achieve a distribution of teachers from these groups that mirrors the overall population.4
In most countries and territories participating in TALIS, between 21% and 35% of teachers who were trained in the use of digital technology would need to move to another school in order to achieve a distribution of teachers in all schools that is similar to the one observed in the overall teacher population (Figure 3.4). Yet, in some countries and territories such as Alberta (Canada), Colombia, South Africa, the United Arab Emirates and Viet Nam the index is above 0.35 while at the other end of the spectrum, it is below 0.23 in Finland, the French Community of Belgium, Italy, Malta, Norway and Portugal. Thus, in the large majority of countries participating in TALIS, the dissimilarity index ranges between 0.23 and 0.35.
When interpreting the dissimilarity index, the focus is on countries and territories where the overall share of teachers with the specific characteristic analysed is 75% or less. By design, the value of the dissimilarity index tends to be high when the share of teachers with a certain characteristic in the overall teacher population is either very small or large (see Box 2.1 in Chapter 2). Thus, one also needs to take into account the overall share of teachers with certain characteristics when interpreting the results of the dissimilarity index. If the overall share of teachers who were trained in the use of ICT during pre‑service training is low, a high value of the dissimilarity index means that there might be schools where no teacher had formal training in the use of ICT. On the contrary, if the majority of teachers were trained in how to use digital technology for instruction, then even in the case of an uneven distribution of teachers, most schools have at least one teacher trained in the use of ICT, and collaboration and knowledge‑sharing among teachers would spread the effective use of digital technology in the school. Hence, an uneven allocation of teachers is less concerning in education systems where most teachers have been trained in the use of ICT. For instance, in the case of Viet Nam, where 97% of teachers reported that the use of ICT for teaching had been included in their formal education or training (Figure 3.3), its high dissimilarity index value should not be a major concern for equity (Figure 3.4).It is plausible to assume that most teachers were trained in the use of ICT in all schools. Albeit to a lesser extent, the same holds for Singapore, the United Arab Emirates, Shanghai (China), Chile and Mexico, where more than 75% of teachers had initial training in ICT use.
As expected based on past TALIS findings, the dissimilarity indices for use of ICT for teaching in formal education and training and teachers’ sense of preparedness in ICT for teaching tend to be correlated (the linear correlation coefficient (r) = 0.61) (Tables 3.5 and 3.6). In some countries such as South Africa, both indicators are relatively high (i.e. dissimilarity index above 0.35) while in others, including Italy, Malta, Norway and Portugal, they are at the lower end (i.e. dissimilarity index above 0.23).
Figure 3.4. Allocation of teachers who had formal training in the use of ICT for teaching
Notes: ICT refers to information and communication technology.
The dissimilarity index measures if the allocation of teachers with a given characteristic in a country’s schools resembles the overall teacher population of the country and it ranges from 0 (i.e. the allocation of teachers in schools resembles perfectly the teacher population of the country) to 1 (i.e. teachers with a certain characteristic are concentrated in a single school). By design, the value of the dissimilarity index is high when the share of teachers with a certain characteristic in the overall teacher population is either very small or large. Thus, cross‑country comparability warrants caution.
Countries and territories are ranked in descending order of the dissimilarity index for teachers for whom the use of ICT for teaching was included in their formal education or training.
Source: OECD, TALIS 2018 Database, Table 3.5.
The uneven allocation of teachers with certain characteristics does not necessarily mean that a school system is inequitable. Education systems may deliberately allocate more resources (e.g. teachers who are proficient in the use of ICT for teaching) to disadvantaged schools to provide all students with access to digital learning resources at home. Hence, it is warranted to look more closely at the nature of the differences across schools in students’ access to teachers who were trained and felt prepared to use ICT.
In certain education systems, teachers who were ICT‑trained tend to work in socio‑economically disadvantaged schools. The share of teachers who reported that they were trained in the use of ICT during their initial education and training is higher in schools where more than 30% of students are from socio‑economically disadvantaged homes than in schools where 10% or less of the students have socio‑economically disadvantaged family background in Australia, England (United Kingdom), France, Sweden, the United Arab Emirates, Viet Nam and on average across the OECD (Figure 3.3). The largest differences are observed in France (12 percentage points), Sweden (8 percentage points) and Australia (7 percentage points). These results may point to a generational effect. TALIS 2018 results show that novice teachers, who had their initial education more recently, are more likely to be trained in ICT and work in disadvantaged schools than their experienced colleagues – see Tables I.4.13 and I.4.32 in TALIS 2018 Results: Volume I (OECD, 2019[36]). Yet, there are two countries – Colombia and Turkey – where the share of teachers who were trained in the use of digital technology during their initial education is larger in socio‑economically advantaged schools than in disadvantaged schools. In Colombia, the proportion of teachers who were trained in the use of ICT is 11 percentage points higher in socio‑economically advantaged schools than in disadvantaged schools.
TALIS data show that teachers working in more challenging environments are more likely to have felt that their formal education and training prepared them to use digital technology in CABA (Argentina), England (United Kingdom), France, the United Arab Emirates and Viet Nam. In these countries and territories, the share of prepared teachers is higher in schools with more than 30% of students from socio‑economically disadvantaged homes than in schools where 10% or less of the students are from socio‑economically disadvantaged homes (Table 3.6). These results may also point to a generational effect. While the share of novice teachers who felt prepared for the use of ICT tends to be higher as compared to their more experienced colleagues – see Table I.4.20 in TALIS 2018 Results: Volume I (OECD, 2019[36]) – novice teachers are also more likely to work in disadvantaged schools. Yet, similar to teachers’ allocation with respect to the inclusion of ICT in their initial education and training, the share of teachers who felt prepared to use digital technology is higher in socio‑economically advantaged schools than in disadvantaged schools in Colombia and Turkey. The same pattern is observed in Mexico and Saudi Arabia.
Teaching students whose first language is different from the language(s) of instruction may require additional effort and different teaching strategies from the teacher. Hence, a school where the share of students whose first language is different from the language of instruction is high can be considered more challenging. TALIS data suggest that there are countries and territories where schools with a higher concentration of such students are more likely to employ teachers who were trained in ICT during their initial education and training (Table 3.5). Notably, in Alberta (Canada), the Flemish Community of Belgium, Latvia, Turkey, Viet Nam and on average across the OECD, the share of ICT‑trained teachers is higher in schools where the first language of more than 30% of students is different from the language of instruction than in schools where the proportion of these types of students is 10% or less. Since these countries and territories tend to have either two or more official languages or important language minorities, these results suggest that school systems provide extra resources to schools where the language of instruction is different from the first language of a large share of the students. The opposite pattern is only observed in the United Arab Emirates.
In addition, schools with a higher concentration of students whose first language is different from the language(s) of instruction are more likely to employ teachers who felt that their initial education and training had prepared them to use ICT in Latvia, England (United Kingdom), Turkey and on average across the OECD (Table 3.6). Thus, in the case of Latvia and Turkey, more challenging learning environments that are characterised by a high share of students whose first language is different from the language of instruction tend to be compensated by teachers who were trained in and felt prepared for the use of ICT. On the contrary, in Singapore, the Russian Federation and the United Arab Emirates, the share of teachers who felt prepared for the use of digital technology is higher in schools where 10% or less of the students’ first language is different from the language of instruction.
Depending on the country/territory, attending a private or public school can make a difference in students’ access to teachers trained in the use of ICT. Among TALIS participants with available data, there are seven countries and territories – England (United Kingdom), France, Japan, Kazakhstan, Singapore, Turkey and the Viet Nam – where students attending public schools have a higher chance of being taught by teachers trained in the use of ICT than their peers in private schools (Figure 3.3). The opposite pattern is observed in Colombia, Portugal and South Africa.
As one would expect, the differences across private and public schools with respect to teachers’ initial training and sense of preparedness in the use of ICT tend to be aligned. For instance, in France, Japan and Kazakhstan, public schools not only have higher shares of teachers who were trained in the use of ICT in formal education and training but also teachers who felt prepared in using ICT (Tables 3.5 and 3.6). However, in Colombia, private schools tend to have more teachers trained in and comfortable with ICT.
Students’ access to teachers who were trained in and at ease with ICT may also depend on the location of the school. The share of teachers for whom the use of ICT for teaching was included in their formal education and training is 2 percentage points higher in schools located in rural areas or villages than in city schools on average across OECD countries (Figure 3.3). The two countries with the largest differences (13 percentage points or higher) are Croatia and Romania. The opposite pattern can be observed only in Latvia, where the share of teachers trained in the use of ICT in city schools is 11 percentage points higher than in rural schools.
The pattern in the differences in the share of teachers who felt comfortable with ICT by school location is mixed. In Estonia, Georgia and Latvia, the share of teachers who felt that their initial education and training prepared them to use ICT for teaching is at least 11 percentage points higher in city schools than in rural schools (Table 3.6). On the contrary, in Chile, Croatia, Kazakhstan, Romania and the United Arab Emirates, the share of teachers who felt prepared is between 8 and 13 percentage points higher in rural schools as compared to city schools.
Allocation of teachers trained in the use of ICT as part of continuous professional development
Given the rapid pace of technological change, the digital skills and related pedagogical practices teachers acquire in their initial education and training can quickly become obsolete. While digital literacy, which has become an essential skill in everyday life, can also be acquired outside of formal education, continuous professional development in ICT use has an important role in addressing digital divides. In‑service training in the use of digital technology can help teachers to continuously validate and update their ICT skills. Moreover, professional development can foster positive attitudes towards ICT integration in teaching and learning. As shown by past research, teachers’ professional development in ICT use tends to have a positive indirect effect on ICT use in the classroom as teachers gain confidence in using ICT for instruction and related pedagogical practices (Alt, 2018[37]; Koh, Chai and Lim, 2017[38]).
Past analysis of TALIS 2018 data not only shows the importance of pre‑service teacher education but also the crucial role of continuous professional development in teachers’ adoption of digital technology for their teaching activities (OECD, 2020[35]). In almost all TALIS countries and territories with available data, teachers are more likely to let students “frequently” or “always” use ICT for projects or class work when ICT skills for teaching were included in their recent professional development activities (OECD, 2020[35]).
TALIS not only collects information about the content of continuous professional development activities attended by teachers but also how much professional development teachers need to integrate ICT skills into their teaching (i.e. “no need”; “low level of need”; “moderate level of need”; and “high level of need”). The information collected about teachers’ needs helps policy makers implement effective professional development (OECD, 2020[39]; Opfer and Pedder, 2011[40]). There may be various different reasons for teachers’ reporting a high level of need for professional development in the use of ICT skills (OECD, 2020[39]). It can signal that teachers want more training in this area due to their lack of knowledge or their dissatisfaction with previous training in digital technology. Yet, it is also possible that teachers simply want to invest more time in developing their ICT skills given that this knowledge field changes rapidly. Previous TALIS findings show that ICT skills for teaching is an area of continuous professional development where, on average across OECD countries and territories, there is a great need for in‑service training and where participation is already high (OECD, 2020[39]). Thus, a high level of need for professional development in the use of ICT skills for teaching does not necessarily mean the lack of such skills. It can also signal that teachers who already use digital technology for teaching are willing to develop their ICT skills further. Analysis of TALIS data shows that teachers who report much need also tend to use ICT more on average across the OECD (Table 3.9). This suggests that a high level of need reflects teachers’ eagerness to learn more rather than a critical lack of knowledge.5
On average across the OECD, 60% of teachers reported attending professional development focusing on ICT skills in the 12 months prior to the survey (Figure 3.5). This share varies between 33% and 93% across TALIS countries and territories while more than 75% of teachers participated in this type of professional development in Colombia, Kazakhstan, Latvia, the Russian Federation, Saudi Arabia, Shanghai (China), the United Arab Emirates and Viet Nam. The share of teachers who reported having a high level of need for in‑service training in ICT skills ranges from 5% in England (United Kingdom) to 55% in Viet Nam (OECD average: 18%) (Table 3.8).
In almost all TALIS participants, the dissimilarity index for teachers for whom ICT skills for teaching was included in their professional development activities in the 12 months prior to the survey ranges between 0.24 and 0.40 (Figure 3.6). This means that between 24% and 40% of teachers who participated in professional development in the use of ICT would need to move to another school in order to achieve a distribution of teachers in all schools that is similar to the one observed in the overall teacher population. Countries and territories where the dissimilarity index for teachers’ participation in professional development focusing on ICT skills is 0.41 or above are: Iceland, the United Arab Emirates and Viet Nam. Yet, it is important to note that the United Arab Emirates and Viet Nam are among the countries where more than 75% of teachers reported attending professional development activities focusing on ICT skills in the 12 months prior to the survey. Thus, in these countries the large majority of students attend schools that have at least one teacher who received in‑service training in the use of ICT.
The distribution of teachers who reported a high level of need for in‑service training in ICT skills across schools shows a different cross‑country pattern to what is observed for teachers who participated in this type of professional development. The dissimilarity index indicates that, among TALIS participants, teachers who have a high level of need for in‑service training in ICT skills are more likely to be concentrated in certain schools (i.e. dissimilarity index at 0.41 or above) in Alberta (Canada), Denmark, England (United Kingdom), the Flemish Community of Belgium, Iceland, Turkey and the United States (Table 3.8). However, it has to be noted that the overall share of teachers who reported needing professional development in the use of ICT is fairly low in these countries and territories, in particular in England (United Kingdom) (5%),Turkey (7%), Alberta (Canada) (8%) and the Flemish Community of Belgium (9%). Thus, the high dissimilarity index value reflects that, by design, the few teachers who have a high level of need for training in ICT skills are less likely to be distributed randomly across schools.
Figure 3.5. Teachers’ participation in professional development in ICT skills, by school characteristics
* For this country, estimates for sub‑groups and estimated differences between sub‑groups need to be interpreted with great care. See Annex A for more information.
Note: ICT refers to information and communication technology.
1. High concentration of disadvantaged students refers to schools with more than 30% of students from socio‑economically disadvantaged homes. Low concentration of disadvantaged students refers to schools with less than or equal to 10% of students from socio‑economically disadvantaged homes.
Countries and territories are ranked in descending order of the share of teachers for whom the use of ICT for teaching was included in their formal education or training.
Source: OECD, TALIS 2018 Database, Table 3.7.
TALIS findings show no clear cross‑country pattern of differences in the share of teachers attending in‑service training in ICT skills and the proportion of those reporting a high level of need for such professional development between socio‑economically advantaged and disadvantaged schools. The share of teachers for whom ICT skills for teaching were included in their professional development activities in the 12 months prior to the survey is higher in socio‑economically disadvantaged schools (i.e. more than 30% of students are from socio‑economically disadvantaged homes) than in advantaged schools (i.e. 10% of students from socio‑economically disadvantaged homes or less) in France (8 percentage points), Kazakhstan (4 percentage points) and Viet Nam (4 percentage points) (Figure 3.5). Thus, in these education systems, teachers working in more challenging environments may have more access to in‑service training in ICT skills or they may feel more need for or willingness to engage in such professional development. In Bulgaria, England (United Kingdom), Estonia, South Africa, Sweden and Turkey, the opposite pattern is observed. The difference in favour of advantaged schools is especially marked in Estonia (16 percentage points), England (United Kingdom) (13 percentage points) and Bulgaria (10 percentage points).
Figure 3.6. Allocation of teachers who participated in professional development in ICT skills
Notes: ICT refers to information and communication technology.
The dissimilarity index measures if the allocation of teachers with a given characteristic in a country’s schools resembles the overall teacher population of the country and it ranges from 0 (i.e. the allocation of teachers in schools resembles perfectly the teacher population of the country) to 1 (i.e. teachers with a certain characteristic are concentrated in a single school). By design, the value of the dissimilarity index is high when the share of teachers with a certain characteristic in the overall teacher population is either very small or large. Thus, cross‑country comparability warrants caution.
Countries and territories are ranked in descending order of the dissimilarity index for teachers for whom ICT skills for teaching were included in their professional development activities in the 12 months prior to the survey.
Source: OECD, TALIS 2018 Database, Table 3.7.
In Georgia, Israel, Kazakhstan, Romania and South Africa, teachers teaching in more challenging environments are more likely to report more need for training in ICT skills than in schools with less than or equal to 10% of students from socio‑economically disadvantaged homes (Table 3.8). The opposite pattern was observed in CABA (Argentina), Lithuania and Sweden, where the share of teachers who reported a high level of need for professional development in the use of ICT is between 5 and 6 percentage points higher in socio‑economically advantaged schools than in disadvantaged schools.
Although in most countries and territories participating in TALIS, teachers’ attendance of in‑service training in ICT skills is similar across public and private schools, there are some exceptions. In Australia, Belgium (including its Flemish Community), Brazil, Mexico and South Africa, the share of teachers for whom ICT skills for teaching were included in their professional development activities in the 12 months prior to the survey is higher in private schools than in public schools (difference between 6 and 10 percentage points) (Figure 3.5). On the contrary, the share of teachers who attended in‑service training on digital technology is higher in public than private schools in France, Kazakhstan and Norway. The difference in favour of teachers working in public schools is particularly large in France (17 percentage points) and Norway (13 percentage points).
In Brazil and South Africa, teachers in private schools are more likely to participate in the professional development of using ICT. They are also less likely to report a high level of need for such training compared to their colleagues working in public schools (Table 3.8). In Kazakhstan, while teachers teaching in public schools are more likely to participate in professional development on the use of digital technology, they are also more likely to feel more need for it than teachers working in private schools. Other countries where teachers teaching in public schools are more likely to report a high level of need for professional development in the use of ICT include Denmark and Estonia. The only TALIS participant where the opposite pattern was observed was Shanghai (China).
Based on TALIS results, in certain education systems, the location of the school may matter for teachers’ participation in or need for professional development in the use of ICT. In Australia, Belgium and New Zealand, teachers for whom ICT skills for teaching were included in their professional development activities in the 12 months prior to the survey tended to be concentrated in cities (Figure 3.5). The difference between city and rural schools in the share of teachers whose professional development activities included ICT skills for teaching is particularly large in New Zealand (29 percentage points) and Australia (22 percentage points). In general, teachers’ access to professional development activities may be more limited in remote areas due to the higher costs of delivering in‑service training (Echazarra and Radinger, 2019[24]). Yet, the differences observed in Australia and New Zealand may also reflect the specific educational context of their remote areas. On the contrary, in Croatia and Spain, the share of teachers for whom ICT skills for teaching were included in their professional development activities in the 12 months prior to the survey is higher in rural schools than in cities.
The share of teachers who reported a high level of need for professional development in the use of ICT tends to be higher in cities than in rural areas in Croatia, Denmark and France (Table 3.8). Georgia is the only TALIS participant where teachers working in cities are less likely to report needing professional development in digital technology than their colleagues teaching in rural schools.
Allocation of teachers with high self‑efficacy in the use of ICT
There is consensus among educational researchers, policy makers and practitioners that teachers’ self‑efficacy is strongly associated with their pedagogical practices and quality of instruction (Ainley and Carstens, 2018[41]; Holzberger, Philipp and Kunter, 2013[42]). Thus, as one would expect, teachers who feel they can use digital technology for instruction are more likely to use ICT tools (Drossel, Eickelmann and Gerick, 2016[30]; Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]; Hatlevik and Hatlevik, 2018[31]; Hsu, 2016[32]; Nikolopoulou and Gialamas, 2016[33]). TALIS data also show that teachers who feel they can support student learning through the use of digital technology “quite a bit” or “a lot”6 are more likely to “frequently” or “always” let students use ICT for projects or class work. This holds true while controlling for teacher characteristics, teacher training in the use of ICT and classroom composition (Table 3.9).
While exploring how teachers with high self‑efficacy in ICT use are distributed across schools, it is also important to examine the relationship between teachers’ age and confidence in using digital technology. As one would expect, younger teachers tend to report more self‑efficacy in ICT use than their colleagues. According to TALIS data, the older teachers are, the lower their self‑efficacy is in ICT use (Table 3.10). This holds true in around half of the countries and territories participating in TALIS as well as on average across the OECD. This association holds in around one‑fourth of TALIS participants and on average across the OECD even while accounting for other teacher characteristics,7 teacher training in the use of ICT and classroom composition (Table 3.11).
The share of teachers with high self‑efficacy in the use of ICT for teaching varies between 35% and 88% across countries and territories participating in TALIS (Figure 3.7). The countries and territories where the large majority (more than 75%) of teachers feel they can support student learning through the use of digital technology “quite a bit” or “a lot” include Alberta (Canada), Australia, Chile, Colombia, Denmark, Hungary, Italy, Kazakhstan, New Zealand, Portugal, Saudi Arabia, Turkey and the United Arab Emirates.
On average across OECD countries and territories, around one‑third of teachers who feel they can support student learning through the use of digital technology would need to move to another school so that the distribution of teachers across schools mirrors the overall teacher population (Figure 3.8). Teachers with high self‑efficacy in ICT use tend to be more concentrated in certain schools (i.e. dissimilarity index above 0.35) in Alberta (Canada), Belgium, Colombia, Denmark, Hungary, New Zealand, Turkey, the United Arab Emirates and the United States as compared to other countries and territories participating in TALIS. Also important to note, a large majority (more than 75%) of teachers report high self‑efficacy in the use of ICT in most of the countries and territories with high dissimilarity index values. The unequal allocation of teachers should be less of a concern in these countries. At the other end of the spectrum, teachers who feel they can support student learning through the use of ICT are more evenly distributed across schools (i.e. dissimilarity index below 0.24) in Estonia, France, Korea, Malta, Shanghai (China) and Slovenia.
Students’ access to teachers with high self‑efficacy in digital technology tends to differ depending on whether the school they attend is privately or publicly managed. In almost one‑fourth of the countries and territories participating in TALIS, the share of teachers who feel they can support student learning through the use of digital technology “quite a bit” or “a lot” is higher in private schools than in public schools (Figure 3.7). In five countries, this difference reaches 10 percentage points or more: Singapore (22 percentage points), Mexico (18 percentage points), Brazil (13 percentage points), Georgia (11 percentage points) and Belgium (10 percentage points). Thus, the fact that teachers who can use ICT with confidence tend to be more concentrated in certain schools in Belgium and the United Arab Emirates may partly result from differences between private and public schools. Teachers in private schools may report higher self‑efficacy in ICT use because private schools tend to have better ICT infrastructure (Tables 3.3 and 3.4). On the contrary, in Chile, the Czech Republic, the French Community of Belgium, Norway and Viet Nam, the share of teachers who feel they can use ICT for teaching is higher in public schools than in private schools. In these countries and territories, students attending public schools are more likely to have teachers who feel capable of supporting teaching and learning with digital technology (Figure 3.7).
Figure 3.7. Teachers with high self‑efficacy in the use of ICT for teaching, by school characteristics
* For this country, estimates for sub‑groups and estimated differences between sub‑groups need to be interpreted with great care. See Annex A for more information.
Note: ICT refers to information and communication technology.
1. High concentration of disadvantaged students refers to schools with more than 30% of students from socio‑economically disadvantaged homes. Low concentration of disadvantaged students refers to schools with less than or equal to 10% of students from socio‑economically disadvantaged homes.
Countries and territories are ranked in descending order of the share of teachers who feel they can support student learning through the use of digital technology “quite a bit” or “a lot”.
Source: OECD, TALIS 2018 Database, Table 3.12.
Figure 3.8. Allocation of teachers with high self‑efficacy in the use of ICT for teaching
Notes: ICT refers to information and communication technology.
The dissimilarity index measures if the allocation of teachers with a given characteristic in a country’s schools resembles the overall teacher population of the country and it ranges from 0 (i.e. the allocation of teachers in schools resembles perfectly the teacher population of the country) to 1 (i.e. teachers with a certain characteristic are concentrated in a single school). By design, the value of the dissimilarity index is high when the share of teachers with a certain characteristic in the overall teacher population is either very small or large. Thus, cross‑country comparability warrants caution.
Countries and territories are ranked in descending order of the dissimilarity index for teachers who feel they can support student learning through the use of digital technology “quite a bit” or “a lot”.
Source: OECD, TALIS 2018 Database, Table 3.12.
TALIS findings suggest that there are some countries and territories where teachers with high self‑efficacy in ICT tend to work in socio‑economically advantaged schools. In Austria, Belgium, Brazil, CABA (Argentina), Colombia, Mexico and South Africa, the share of teachers who reported high self‑efficacy in supporting student learning through the use of digital technology is higher in schools with 10% of students from socio‑economically disadvantaged backgrounds or less than in schools with more than 30% of disadvantaged students (Figure 3.7). Thus, in these countries and territories, students from disadvantaged backgrounds, who tend to be less exposed to digital learning at home, are also less likely to have access to teachers with high self‑efficacy in teaching with ICT at school. The largest difference in favour of socio‑economically advantaged schools is observed in Belgium (13 percentage points), which also happens to be the country with one of the most uneven distribution of teachers across schools according to the dissimilarity index. Similar to the gap between private and public schools, teachers working in socio‑economically advantaged schools may report higher self‑efficacy in digital technology because these schools tend to have more adequate ICT infrastructure (Tables 3.3 and 3.4). Table 3.1 shows that the share of teachers who feel they can use ICT is higher in schools where the quality of instruction is not hindered by inadequate digital infrastructure. It is only in Alberta (Canada) that disadvantaged students are more likely to have teachers with high self‑efficacy in the use of ICT than their more affluent peers.
In a few education systems, school location can matter in terms of students’ access to teachers with high self‑efficacy in ICT. In Australia, Turkey and the United States, the share of teachers who reported high self‑efficacy in the use of digital technology is higher in schools located in cities than in rural schools (Figure 3.7). These differences amount to 15 percentage points in Australia and the United States. In the case of Australia, the self‑efficacy of teachers in rural schools may be hindered by their more limited participation in professional development activities focusing on ICT skills (Table 3.7). The uneven allocation of teachers with high self‑efficacy in ICT in the United States, as shown by the dissimilarity index, may be partly explained by school location. In contrast, other countries such as Austria, Chile, the Czech Republic, Hungary, Portugal and the Slovak Republic show a higher proportion of teachers in rural than urban schools who report high self‑efficacy in ICT. This can be explained by rural schools often having fewer students, lower student‑teacher ratios and better school climate, which in turn result in more supportive learning environments and better disciplinary climate (Echazarra and Radinger, 2019[24]).
Do students have access to teachers who use ICT for teaching on a regular basis?
Past findings based on PISA data show that providing access to ICT tools at school does not automatically lead to better student outcomes (Borgonovi and Pokropek, 2021[4]; OECD, 2019[14]; OECD, 2015[3]). According to recent research, students who use ICT either a lot or a little tend to have lower levels of reading achievement than students who engage in medium levels of use of digital technology (Borgonovi and Pokropek, 2021[4]). This means that both too limited and overly excessive use of ICT can be associated with lower student achievement. Yet, teachers’ and students’ ability to make the most of ICT for teaching and learning is reinforced by regular and judicious use of digital technology in the classroom. Past research highlights the positive relationship between teachers’ perceived self‑efficacy and their use of digital technology in the classroom (Drossel, Eickelmann and Gerick, 2016[30]; Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]; Hatlevik and Hatlevik, 2018[31]; Hsu, 2016[32]; Nikolopoulou and Gialamas, 2016[33]). Therefore, it is worth examining if teachers who “frequently” or “always” use ICT for teaching are distributed evenly across schools and if schools with different characteristics differ in the share of teachers who use digital technology for instruction on a regular basis.
As it was shown in the previous section, younger teachers tend to report higher self‑efficacy in ICT use (Tables 3.10 and 3.11). The question arises whether younger teachers are also more likely to let students use ICT8 on a regular basis than their colleagues. Based on TALIS data, the fact that younger teachers tend to report higher self‑efficacy in ICT use does not necessarily result in more frequent use of digital technology in their teaching. In almost one‑third of the countries and territories participating in TALIS, teachers’ age and the frequency of ICT use for instruction are positively associated (Table 3.13). This signals the presence of confounding factors. Younger teachers’ use of digital technology in the classroom may be hampered by the fact that they tend to work in more challenging schools. School principals of disadvantaged schools are more likely to report that instruction is hindered by the lack of adequate ICT infrastructure (Tables 3.3 and 3.4). The relationship between teachers’ age and the regular use of ICT for teaching remains significant only in a couple of countries and territories when other teacher characteristics,9 ICT training, and classroom composition are accounted for (Table 3.14).
Based on past TALIS findings, letting students use ICT on a regular basis is not as widespread as other teaching strategies such as practices involving classroom management and clarity of instruction. On average across the OECD, about 53% of teachers reported that they “frequently” or “always” let students use ICT for projects or class work10 (OECD, 2019[36]). Unlike those more common teaching practices, the use of ICT for teaching and learning may require additional school resources as well as teachers who are able to use ICT for teaching effectively. As shown in the previous sections, teachers who are trained in and feel capable of using ICT may concentrate in certain types of schools. At the same time, there are also differences in students’ access to ICT equipment and adequate Internet across schools. It is worth looking at whether students have fair access to teachers who use ICT for teaching on regular basis.
Allocation of teachers who use ICT for teaching on a regular basis
Overall, the share of teachers who reported “frequently” or “always” letting students use ICT for projects or class work varies a lot (between 15% and 90%) across countries and territories that participated in TALIS (Figure 3.9). While in Denmark (90%), New Zealand (80%), Australia (78%) and the United Arab Emirates (77%), most teachers frequently use technology for teaching, less than 20% of teachers regularly do in the French Community of Belgium and Japan.
On average across the OECD, around one‑third of teachers who regularly use ICT in the classroom would need to move to another school for them to be distributed evenly across schools (Figure 3.10). Teachers who use ICT in class on a regular basis are more unevenly distributed across schools (i.e. dissimilarity index above 0.35) in Alberta (Canada), Australia, Denmark, Iceland, Japan, the Netherlands, New Zealand, Saudi Arabia, Sweden, the United Arab Emirates, the United States and Viet Nam than in the rest of countries and territories participating in TALIS. However, as the large majority (more than 75%) of teachers reported using ICT for teaching on a regular basis in Australia, Denmark, New Zealand and the United Arab Emirates, the uneven distribution of teachers, as indicated by the dissimilarity index, should be less of a concern in these countries. It is plausible that, in these countries, most schools employ teachers who frequently use ICT for teaching. At the other end of the spectrum, in Croatia, Estonia, France, Lithuania, Malta and Portugal, teachers who regularly use ICT for instruction are more evenly distributed across schools (i.e. dissimilarity index below 0.25).
TALIS results suggest that the differences across schools in the use of ICT are most pronounced between private and public schools. In almost one‑fourth of the countries and territories participating in TALIS, the share of teachers who reported “frequently” or “always” letting students use ICT for projects or class work is higher in private schools than in public schools (Figure 3.9). The three countries with the largest differences are Singapore (35 percentage points), Australia (11 percentage points) and Spain (10 percentage points). In these education systems, students attending private schools are more likely to be exposed to digital learning at school than their peers who attend public schools. Teachers in private schools may use ICT for instruction more regularly since private schools tend to have better ICT infrastructure (Tables 3.3 and 3.4). Also, students attending private schools may have better access to digital learning resources at home, which in turn, helps teachers implement digital learning at school more smoothly and effectively. The opposite pattern is observed in three countries. The share of teachers who “frequently” or “always” let students use ICT is at least 10 percentage points higher in public schools than in private schools in Chile, the Flemish Community of Belgium and Turkey.
Figure 3.9. Teachers’ regular use of ICT for teaching, by school characteristics
* For this country, estimates for sub‑groups and estimated differences between sub‑groups need to be interpreted with great care. See Annex A for more information.
Note: ICT refers to information and communication technology.
1. High concentration of disadvantaged students refers to schools with more than 30% of students from socio‑economically disadvantaged homes. Low concentration of disadvantaged students refers to schools with less than or equal to 10% of students from socio‑economically disadvantaged homes.
Countries and territories are ranked in descending order of the share of teachers who report to "frequently" or "always" let students use ICT for projects or class work.
Source: OECD, TALIS 2018 Database, Table 3.15.
In a few education systems, teachers’ use of ICT may also vary depending on the concentration of students from socio‑economically disadvantaged homes. In Australia, England (United Kingdom), the United Arab Emirates, Viet Nam and on average across OECD countries, the share of teachers who reported “frequently” or “always” letting students use ICT for projects or class work is higher in schools where 10% or less of the students are from socio‑economically disadvantaged backgrounds than in schools with more than 30% of the students coming from disadvantaged backgrounds (Figure 3.9). This difference is above 10 percentage points in Viet Nam (19 percentage points), England (United Kingdom) (13 percentage points) and Australia (13 percentage points). Hence, in these school systems, disadvantaged students, who tend to have limited access to digital learning at home, are also less likely to be exposed to ICT use at school. Teachers working in schools with a lower share of students from socio‑economically disadvantaged homes tend to have access to better ICT infrastructure at school (Tables 3.3 and 3.4). They also tend to teach students who have better access to digital learning resources at home. On the contrary, the share of teachers who frequently use ICT for teaching is higher in socio‑economically disadvantaged schools than in advantaged schools in Alberta (Canada) (16 percentage points) and the Flemish Community of Belgium (10 percentage points). In these education systems, providing disadvantaged students with preferential access to digital learning at school may be a deliberate policy.
Figure 3.10. Allocation of teachers who use ICT for teaching on a regular basis
Notes: ICT refers to information and communication technology.
The dissimilarity index measures if the allocation of teachers with a given characteristic in a country’s schools resembles the overall teacher population of the country and it ranges from 0 (i.e. the allocation of teachers in schools resembles perfectly the teacher population of the country) to 1 (i.e. teachers with a certain characteristic are concentrated in a single school). By design, the value of the dissimilarity index is high when the share of teachers with a certain characteristic in the overall teacher population is either very small or large. Thus, cross‑country comparability warrants caution.
Countries and territories are ranked in descending order of the dissimilarity index for teachers who “frequently” or “always” let students use ICT for projects or class work.
Source: OECD, TALIS 2018 Database, Table 3.15.
TALIS data suggest that in a couple of countries and territories regular use of ICT for teaching is lower in schools that have a higher concentration of students whose first language is different from the language(s) of instruction (Table 3.15). In Bulgaria, the French Community of Belgium, the Russian Federation and Singapore, the share of teachers who reported letting students use ICT in the classroom is 7 to 13 percentage points lower in schools with a higher share (more than 30%) of students whose first language is different from the language of instruction than in schools with a lower share (10% or less). The United Arab Emirates is the only country where students attending more multicultural schools in terms of language background are more likely to use ICT regularly in class. This may reflect the presence of affluent, non‑English speaking expatriate families whose children tend to attend international schools where the language of instruction is typically English.
In some education systems teachers’ use of digital technology for instruction varies depending on the share of students with special education needs at school. In Alberta (Canada), New Zealand, Singapore and the United Arab Emirates, the share of teachers who use ICT for teaching on a regular basis is 3 to 12 percentage points higher in schools where more than 10% of students have special education needs (Table 3.15). These findings may be explained by the use of assistive technologies for students with special education needs. Computer programmes and applications that support students with learning disabilities can provide a more personalised and helpful learning experience. There is empirical evidence showing a positive relationship between the use of assistive technologies and the improved outcomes of students with special needs (Maor, Currie and Drewry, 2011[43]) On the contrary, in Croatia and Hungary, the share of teachers who reported “frequently” or “always” letting students use ICT for projects or class work is 6 percentage points higher in schools where the concentration of students with special education needs is 10% or less.
School location can also matter for students’ access to digital learning in the classroom. The share of teachers who usually let students use ICT for projects or class work is higher in schools located in cities than in rural schools in Viet Nam (16 percentage points), the United States (13 percentage points), Turkey (8 percentage points) and Georgia (6 percentage points) (Figure 3.9). Yet, in Austria, Chile and Italy, the share of teachers who let students use ICT in class on a regular basis is between 9 and 14 percentage points higher in rural areas than in cities. In the case of Austria, this result may be related to the finding that rural schools’ ICT infrastructure tend to be better, as reported by school leaders (Tables 3.3 and 3.4).
How much do teacher characteristics and school resources in ICT infrastructure explain differences in the use of ICT across schools?
As described above, teachers who use ICT for teaching on a regular basis are not randomly allocated across schools. However, their uneven distribution does not necessarily imply that the school system is inequitable. Exposing students to digital learning at school is one way to compensate for limited access to digital learning at home. Yet, as shown in the previous section, the share of teachers who regularly use ICT in school is higher in private schools than in public schools. In a few education systems, there is also evidence that the share of this type of teachers is higher in socio‑economically advantaged than disadvantaged schools. Hence, the question of how to enhance digital learning in public schools and less affluent schools arises. Should education systems aim to reallocate teachers who are trained in and feel capable of using ICT for teaching, which would provide all students with access to digital learning? Or should school systems provide better ICT infrastructure such as adequate ICT equipment and access to Internet to schools that are most in need?
One way to address these questions is to examine the share of the overall variation in teachers’ frequent use of ICT that lies between schools once teacher and school characteristics have been taken into account.11 If teachers’ use of ICT no longer varies between schools when teacher characteristics have been taken into account, then reallocating teachers based on their years of experience, self‑efficacy, initial education and continuous professional development in the use of digital technology may sufficiently provide all students with access to digital learning, irrespective of the school they attend. However, if the share of the overall variation in teachers' use of ICT that lies between schools remains significant after teacher characteristics have been adjusted for, then teacher allocation policies may not be enough. Similarly, if teachers’ use of ICT no longer varies between schools when the quality of ICT equipment and Internet access are taken into account, then investment in ICT infrastructure may have an important role in addressing digital divides. Yet, if the share of the overall variation in teachers’ use of ICT that lies between schools remains significant after the quality of schools’ ICT equipment and Internet access have been taken into account, then investment in ICT infrastructure alone may not be enough to address inequities in students’ access to digital learning at school.
TALIS results indicate that differences between schools in the use of ICT remain significant after controlling for teaching experience and teachers’ self‑efficacy, and initial education and continuous professional development in the use of ICT in all TALIS participants except for Malta (Table 3.16). On average across OECD countries, 34% of the variance in the propensity to let students use ICT is between schools after adjusting for teacher characteristics (Figure 3.11). Hence, based on TALIS data, inequities across schools in the use of ICT cannot be addressed only by reallocating teachers based on characteristics such as years of teaching experience, self‑efficacy, initial education and continuous professional development in the use of ICT.
Figure 3.11 also shows that the share of the variation in teachers’ use of ICT that lies between schools ranges considerably across countries and territories – from less than 10% in Estonia and Malta to more than 60% in Australia, Colombia, Denmark, Mexico and Sweden. The results for Estonia and Malta suggest that teachers’ use of ICT depends mainly on teacher‑level factors rather than school‑level characteristics. On the contrary, high between‑school variation in teachers’ ICT use signals that school context (e.g. composition of student body) and school‑level factors explain variations in the use of digital technology. Students in Australia, Denmark and Sweden tend to use digital technology at school more frequently than their peers in other OECD countries (OECD, 2019[14]). Thus, the high between‑school variation suggests that in these countries, there are certain schools where teachers use technology for instruction less frequently than most of their colleagues working in other schools for whom the use of ICT is common practice.
TALIS data also show that in most countries and territories, school characteristics such as location, school type and student composition in terms of socio‑economic and language background as well as special education needs explain little of the variation in ICT use once teacher characteristics have been taken into account (Figure 3.11). Since certain teacher characteristics are more prevalent in schools of certain types, adjusting for teacher characteristics implicitly results in taking school characteristics into account. For instance, the share of experienced teachers tends to be higher in socio‑economically advantaged schools than in disadvantaged schools.
Yet, there are some exceptions to this general pattern. For instance, in Sweden the share of the overall variation in teachers’ use of ICT that lies between schools decreases by 13 percentage points when also adjusting for school characteristics and not just teacher characteristics (Figure 3.11). Thus, in the case of Sweden, school characteristics such as location, school type and composition of the student body matter more for teachers’ use of ICT than in other education systems. Other countries and territories where a similar pattern is observed, albeit to lesser extent (i.e. decrease of between 5 to 6 percentage points), include the French Community of Belgium, the Netherlands and Singapore.
Figure 3.11. Variation in teachers’ regular use of ICT for teaching
Notes: ICT refers to information and communication technology.
The proportion of the overall variation that lies between schools is calculated as 100*rho, where rho stands for the intra‑class correlation. The intra‑class correlation, in turn, is computed as the variation between schools, divided by the sum of the variation between schools and the variation within schools.
Controls for teacher characteristics include years of teaching experience, teachers’ self‑efficacy in ICT use, the inclusion of ICT use in teachers’ formal training and teachers’ profession development activities in ICT use.
Controls for school characteristics include school location, school type (i.e. in terms of public/private management) and student composition of schools according to socio‑economic and language background, as well as special education needs.
Controls for schools’ digital infrastructure include schools’ resource issues, such as shortage or inadequacy of digital technology for instruction and insufficient Internet access, which hinder schools’ capacity to provide quality instruction.
Cross‑country comparability warrants caution, as the between‑school variance estimates depend on how schools are defined and organised within countries and territories and how they are chosen for sampling purposes.
OECD average‑31 excludes Alberta (Canada), Japan, New Zealand, Norway, the Russian Federation and the United States. Estimates for these countries/territories are missing due to computational process (e.g. regression coefficients are missing if there is at least one variable without valid data included in a regression model; or else in the case of non‑convergence of maximum likelihood function).
Countries and territories are ranked in descending order of the proportion of the overall variation in teachers’ regular use of ICT that lies between schools after controlling for teacher characteristics.
Source: OECD, TALIS 2018 Database, Table 3.16.
As discussed previously, school’s resources with respect to ICT equipment and Internet access tend to correlate with school characteristics in many education systems. The share of principals who reported that inadequate digital technology was a major impediment to good teaching tends to be higher in public schools and schools with a high concentration of disadvantaged students than in private schools and in socio‑economically advantaged schools (Table 3.3). In addition, insufficient Internet access is more of an issue in schools located in rural areas, public schools and in schools with a high share of students from a disadvantaged background (Table 3.4). Thus, adjusting for schools’ ICT infrastructure once teacher and school characteristics have been taken into account has limited effect on the share of the overall variation in teachers’ use of ICT that lies between schools (Figure 3.11). This finding is in line with past research, which suggests that there are factors such as teacher training in ICT, collaboration among teachers, teachers’ perceived self‑efficacy and beliefs about teaching and the availability of educational software that matter more for teachers’ actual use of ICT in the classroom than the school’s ICT infrastructure (Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]).
There may be other factors that explain the variation in teachers’ use of ICT that lies between schools. The literature highlights the positive association between the frequency teachers use ICT in the classroom and how much they collaborate with their colleagues (Fraillon et al., 2019[15]; Gil-Flores, Rodríguez-Santero and Torres-Gordillo, 2017[7]; Hatlevik and Hatlevik, 2018[31]). Collaboration can boost knowledge‑sharing among teachers, including about ICT use, which in turn can translate into more frequent use of digital technology in the classroom. TALIS data also show that the more frequently teachers collaborate with their peers at school,12 the more likely it is that they let their students use ICT for projects or class work on a regular basis (Table 3.17). This holds true in around half of the countries and territories participating in TALIS and on average across the OECD while controlling for teacher characteristics,13 teachers’ training in the use of ICT and classroom composition. This means that while digital technology fosters collaboration by providing better tools for collaborative work, collaboration among teachers itself can boost ICT use in school.
Yet, as shown by TALIS 2018 results, most of the variance in professional collaboration is at the individual (teacher) level14 (OECD, 2020[39]). This suggests that when a teacher collaborates at school, that teacher does not collaborate with all teachers but only a few while other colleagues from the same school do not collaborate at all, hence the considerable within‑school variation. This points to clusters of teachers within schools when it comes to collaboration. Policies targeting teacher collegiality and collaborative school culture can encourage teachers to collaborate instead of working in silos.
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Notes
← 1. Digital skills encompass a broad range of skills, from more generic skills – such as understanding basic ICT concepts, being able to manage computer files, and using keyboards or touch‑screen devices – to more specific ICT skills – like using work‑related software, creating online content, evaluating online risks, framing problems in ways that computers can help solve them, and distinguishing fact from opinion. Advanced skills include coding or software development (OECD, 2019[14]).
← 2. Southern Hemisphere countries were surveyed in 2017.
← 3. TALIS asks school principals about the extent to which school resource issues, including the shortage or inadequacy of digital technology for instruction and insufficient Internet access, hinder their school’s capacity to provide quality instruction (i.e. “not at all”; “to some extent”; “quite a bit”; or “a lot”).
← 4. Hereafter in this chapter, the dissimilarity index is interpreted with the assumption that school size can be adjusted. This allows the analysis to focus on the proportion of one or the other group that has to be reallocated in order to achieve a distribution of teachers from these groups that mirrors the overall population.
← 5. This relationship holds while also controlling for teacher characteristics such as years of teaching experience, gender and employment status, teachers’ training in the use of ICT and classroom composition.
← 6. In addition to factual indicators on teachers’ initial education and training, and their continuous professional development, TALIS also collects more subjective measures of teachers’ perceptions of the quality of their own teaching. Namely, TALIS asks teachers about the extent to which they can do a series of goal‑oriented actions – for instance, supporting students’ learning through the use of digital technology (e.g. computers, tablets, smart boards) – asking them to mark one choice among four options: “not at all”; “to some extent”; “quite a bit”; “a lot”.
← 7. Other teacher characteristics include teachers’ years of teaching experience, gender and employment status.
← 8. ICT use refers to tools that can be used for projects or class work as defined in the TALIS 2018 Teacher Questionnaire. As such, it is a broadly defined concept.
← 9. Other teacher characteristics include teachers’ self‑efficacy in ICT use, years of teaching experience, gender and employment status.
← 10. TALIS asks teachers about the frequency with which they use ICT for projects and class work (“never or almost never”; “occasionally”; “frequently”; or “always”).
← 11. This analysis is based on logistic multilevel regression models that take into account the nested structure of the data (i.e. the fact that teachers are clustered within particular schools) while also providing estimates of the overall variance in the outcome variable that lies between and within schools.
← 12. The index of professional collaboration measures teachers’ engagement in deeper forms of collaboration that involve more interdependence between teachers, including teaching jointly as a team in the same class, providing feedback based on classroom observations, engaging in joint activities across different classes and age groups, and participating in collaborative professional learning.
← 13. Teacher characteristics include teachers’ self‑efficacy in ICT use, years of teaching experience, gender and employment status.
← 14. On average across the OECD, 87% of the variation in teachers’ responses regarding their engagement in deeper forms of collaborative activities lies across teachers within schools, while the rest (13%) is accounted for by differences in the average level of collaboration between schools – see Table II.4.12 in TALIS 2018 Results: Volume II (OECD, 2020[39]).