1 Introduction

Disadvantaged school contexts can pose a variety of challenges to novice teachers. Some challenges may be mitigated through support structures, such as mentoring which adapts to the specific needs of novice teachers in disadvantaged contexts. However, novice teachers only have inadequate access to mentor support in many education systems (OECD 2018a). To set up mentoring programmes in an adaptive way, it is necessary to understand the factors that contribute to schools being deemed “disadvantaged”: Concepts of disadvantage may differ by country as well as in the perspectives of different agents in the school context.

In this paper, we investigate different perspectives on school disadvantage in seven European education systems. To this end, we will first discuss the existing literature on how to characterise disadvantaged schools in different education systems and present our own definition of disadvantaged schools. Then, we discuss mentoring as a support structure for novice teachers with a particular focus on disadvantaged contexts. Next, we will give an overview of the Novice Educator Support and Training (NEST) project, which provides the research access for this paper. In the following sections, we will present analysis from the evaluation of the NEST project, which provides various perspectives on and measures of disadvantage in schools. Lastly, we will discuss insights relevant to the future of indicatorising disadvantaged schools and using these indicators for the development of remedial interventions.

2 Background

The notion of disadvantaged schools or schools in disadvantaged areas is widely used in educational research and administration. However, the ways in which disadvantaged schools are characterised as well as the indicators that are used to classify them as disadvantaged differ largely by theory and between education systems (McCoy et al. 2014). One approach to classify a school as disadvantaged consists in identifying characteristics of its geographic location. Depending on country-specific circumstances, disadvantaged schools can be located in both urban and rural areas. The location of the school often correlates with low socioeconomic status (SES) in the area, which is often the only or main criterion to classify a school as disadvantaged (see Kyriakides et al. 2019; Pàmies Rovira et al. 2016). Pàmies Rovira et al. (2016, p. 4) focus their research on schools situated in disadvantaged areas of four major Spanish cities. These schools are all located in densely populated areas in working-class neighbourhoods with high unemployment rates and with a large proportion of both internal and international migrants. McCoy et al. (2014) distinguish between disadvantaged schools in rural areas, two categories of disadvantaged schools in urban areas (Urban Band 1 and Urban Band 2), and non-disadvantaged schools in Ireland. Urban Band 1 schools are the most deprived schools; children attending these schools have the most disadvantaged socioeconomic background. Generally, all disadvantaged schools show an overrepresentation of children living in single-parent households compared to non-disadvantaged schools. Moreover, immigrant students are more prevalent in urban disadvantaged schools in Ireland (McCoy et al. 2014).

Kyriakides and colleagues (2019) operationalised their use of SES by focusing on the father’s and mother’s level of educational attainment, the social status of both the father’s and the mother’s job, and the main elements of the learning environment at home. Using this understanding of SES as the criterion for identifying disadvantaged schools implies a focus on input criteria, i.e. the circumstances of the children when they enter the school. In other cases, educational outcomes (output variables) are also used for designating schools as disadvantaged. For example, Martínez (2014, p. 960) argued that Mexican disadvantaged schools are ‘schools with low educational achievement located in poor economic environments’. In this definition, an output criterion—low educational achievement—is added to the input variable of poor economic environment. Hall et al. (2022) also used a combination of input and output criteria. They focused on low employment rates (input), a high reliance on social assistance (input), and a high number of students who do not manage to qualify for entering high school (output), in order to select the most disadvantaged city districts for their Coaching for Teaching (CFT) intervention in Sweden.

In sum, these findings show that research studies vary in how they classify the school or the school’s vicinity as disadvantaged. Most studies focus on input and output indicators, even though in practice a greater variety of indicators is used by educational administrations (Weishaupt 2016). In general terms, the criteria for disadvantaged schools describe potential conditions within a school learning environment, which contribute to negative outcomes. Such outcomes consist of for instance ‘school completion’, ‘obtained qualifications’ or ‘low levels of delinquency’.

The preference and weighting of desired outcomes as well as the resulting selection of criteria for classifying a school as ‘disadvantaged’ are subjected to normative decisions, theories about the relation between outcomes and characteristics of schooling, and empirical analysis. Therefore, we developed the following working definition to designate schools as disadvantaged:

“A disadvantaged school has to deal with more difficulties than other schools in the same education system in providing a social and academic learning context that enables students to acquire the competences necessary for developing their full potential.”

This definition of disadvantaged schools focuses on the challenges that disadvantaged schools face rather than on the outcomes, while at the same time stressing that difficulties might hinder the provision of the optimal social and academic learning context for the students attending the school in comparison to non-disadvantaged schools. The definition is formal, meaning that it describes a structural condition, without naming concrete challenges. For instance, concrete challenges may be the limited amount of resources, both physical and human, that many disadvantaged schools have at their disposal (Wilson 2021; Tannehill and MacPhail 2017; Martínez 2014). In addition, disadvantaged schools on average have a higher proportion of students who display challenging behaviours (Tannehill and MacPhail 2017) and students who lack basic skills and abilities (Martínez 2014). Another challenge for disadvantaged schools is involving parents in the education of their child (Martínez 2014). The combination of these different challenges means that teachers at disadvantaged schools often experience a high workload and high levels of stress.

Once a concrete notion of disadvantaged schools is established within an education system, it is possible to conceptualise remedial educational policies. In this regard, teacher education is a prominent policy area consisting of different fields of action (Hall et al. 2022). One of these fields is providing professional support for novice teachers through adaptive mentoring. However, neither induction nor mentoring programmes are available to all novice teachers in the member countries of the OECD; 51% of novice teachers do not take part in an official induction programme, and only 22% report having an assigned mentor (OECD 2018a). This lack of structured mentoring for novice teachers seems to contribute to high attrition rates among teachers in the first five years of their careers. Supporting novice teachers working at disadvantaged schools is especially relevant. Teachers are more often placed at disadvantaged schools at the beginning of their career (Allen et al. 2018) and their academic teacher qualification often does not adequately prepare them for the teaching challenges at disadvantaged schools. Therefore, they can be expected to benefit even more than others from a mentoring approach that is tailored towards their needs. Based on their literature review, Hobson et al. (2009) suggested that the main benefits are derived from the provision of emotional and psychological support, which reduces novice teachers’ sense of isolation and increases their confidence and job satisfaction. Another important precondition for successful mentoring seems to be the extent to which a mentor is capable to address the individual needs of the novice teacher and adapt the mentoring to those needs (Crasborn et al. 2011, van Ginkel et al. 2016).

3 The NEST project

Establishing a tailored mentor training programme is one of the aims of the NEST project (van Veldhuizen et al. 2022). NEST is an ERASMUS+ policy experiment which is co-funded by the European Commission and conducted simultaneously in Austria, Belgium (regions of Flanders and Wallonia), Bulgaria, Romania and Spain (regions of Madrid and Catalonia). The term ‘policy experiment’ refers to a type of project requiring several European countries which share a common challenge to find a scalable solution for this challenge in terms of a new policy or intervention, which is then to be tested in comparison to the status quo (Morgenroth et al. 2017). In the case of the NEST project, the common challenge is high attrition rates among novice teachers, especially those working at disadvantaged schools.

Overall, the project examines two interventions at the same time: In Intervention I, a group of mentors (intervention group of mentors) take part in the NEST tailored mentor training programme (Anderson-Park et al. 2022). In Intervention II, a group of novice teachers at disadvantaged schools (intervention group of novice teachers) will be supported by the mentors from Intervention I and will receive adaptive mentoring. The quasi-experimental design of the policy experiment requires the implementation of a control group of experienced teachers who do not receive special mentor training (control group of mentors) as well as a group of novice teachers who receive only the standard support prevalent in their education system (control group of novice teachers).

4 Aims

The objective of this paper is to establish a basis for the design and further development of mentor training programmes aimed at supporting novice teachers who work at disadvantaged schools by analysing the way disadvantaged schools are defined and the terminology used in this context. To set up any pedagogical measure it is important to understand the criteria used to define “disadvantage” in the specific context, especially if the context is possibly socially tabooed.

When developing a mentor training program that caters to the unique needs of novice teachers in disadvantaged schools, it is advisable to consider the viewpoints of different stakeholders. This is particularly important since these perspectives may diverge significantly. Educational administrations often define “disadvantage” in schools with the goal of allocating resources to achieve overall social objectives such as employability, equity, or social cohesion. On the other hand, novice teachers who work in disadvantaged schools may face specific challenges associated with disadvantage in schools, such as managing a multilingual classroom, teaching students who lack parental support, or working in understaffed schools. As a result, novice teachers’ objectives in dealing with these challenges may only partially align with the educational administration’s broader goals. This potential discrepancy in perspectives is relevant to any teacher professional development intervention. Thus, by exploring the multifaceted nature of disadvantage and how it is perceived by different stakeholders, we hope to gain a deeper understanding of teacher professional development needs in these settings, ensuring that the specific needs of the school community are met and the various challenges faced by teachers are addressed.

To this end, we first examine the perspective of educational administrations by studying the terminology used to characterize disadvantaged schools and the indicators used to classify a school as “disadvantaged” in the respective education systems. Through a comparison of the indicators used in the various education systems, we show different vantage points of disadvantage and consequentially different potential approaches of how to set up pedagogical measures such as an adaptive mentoring programme.

Second, we investigate the perspectives of novice teachers who work in schools that are classified as disadvantaged and gather insight on their perception of these school contexts. Our investigation will focus mostly on “input” factors of disadvantage, as these pertain most directly to the challenges they may face in their work. This additional perspective allows for a fuller picture of disadvantage in schools and for a better understanding of the specific challenges novice teachers face in these contexts.

Lastly, we discuss how these analyses can inform the development of pedagogical measures such as mentoring programmes tailored for the disadvantaged school context.

5 Methods

We used different methods and data sources to investigate the two perspectives. To capture the perspective of the educational administration, we combined document analyses with semi-structured interviews with educational experts in the participating countries. The educational experts from the management level of the partner organisation Teach For All filled out questionnaires and provided official administrative documents on how disadvantaged schools are classified or identified in the respective education system. Partners in some education systems could not make official documentation available (Bulgaria, Madrid); all other documents are listed in Table 1. We needed translations for documents in languages other than German and Flemish. To obtain detailed information and verify our understanding of the documents, we interviewed a convenience sample of 13 educational experts including the educational experts from the partner organisation Teach For All and representatives of educational ministries or school inspectorates on both regional and national levels (Austria: 2, Bulgaria: 3, Catalonia: 1, Flanders: 1, Madrid: 1, Romania: 4, Wallonia: 1). All interviews were conducted on the condition of anonymity.

Table 1 Administrative Documents Used as Sources in the Education Systems
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The guided interviews comprised 16 questions and focused on terminology and criteria for disadvantaged schools, support measures as well as possible negative consequences for disadvantaged schools and working conditions (for novice teachers) at disadvantaged schools. They were all led online as a video conference call between October and November 2021. The interview guideline can be found in the appendix (A1).

Indicators of disadvantaged schools that were named in either the administrative documents or in the interviews with representatives of official authorities were subsequently clustered and consensually categorised by two of the authors of this article. Due to the limited number of documents analysed and their high variability, we opted to employ an inductive approach similar to thematic analysis (Braun and Clarke 2006) instead of a formalised coding process. The results of this process were discussed within the group of the authors as well as with our local partners in the education systems.

To describe the novice teachers’ perspective of disadvantaged schools, we relied on questionnaire data collected for the evaluation of the first implementation of the NEST mentoring programme. Over a period of two years, the NEST mentors work with two successive cohorts of novice teachers: one cohort for the school year 2021/2022, and one cohort for the school year 2022/2023. Data for the novice teacher perspectives consist of questionnaire responses from the second survey for novice teachers. At this point, we only have data available for the first teacher cohort (school year 2021/2022).

We analysed a sample of 911 novice teachers. All novice teachers had at maximum five years of teaching experience and were on average 32.4 years old with a median age of 30 (see Table 2). The majority of novice teachers was female (73.7%, see Table 3). Novice teachers in the invention groups (N = 384) received mentoring from a NEST-trained mentor, whereas those in the control groups (N = 527) only had the usual support that was prevalent in their respective education systems. However, for the novice teachers’ perspectives on disadvantage at their school, the belonging to the groups is irrelevant since the intervention has no expected effect on the school context. Still, we examined the data by education system and found no relevant differences in the responses between intervention groups and control groups. Therefore, we will present the results for all novice teachers. However, due to cultural and linguistic differences in the questionnaires as well as substantial divergences between the education system (e.g. in the amount of time novice teachers spent in individual schools), the data were analysed separately for the different education systems. Especially, when teachers compare their school to others, they rely on a national frame of reference.

Table 2 Novice Teachers’ Age in Years by Education System and Group
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Table 3 Novice Teachers’ Gender by Education System and Group
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The questionnaires we used to capture the novice teachers’ perspective included a prompt to estimate various aspects of the backgrounds of students at the novice teachers’ schools, which we slightly adapted from TALIS (OECD 2018b, Principal Questionnaire, p. 8) to fit our context. Novice teachers were asked to estimate proportions regarding the composition of students at their schools. For example, novice teachers were asked to estimate the percentage of students with special needs or the percentage of students from ethnic minorities.

Additionally, the questionnaire included a set of Likert-type items on potential shortages hindering quality instruction (TALIS Principal Questionnaire; OECD 2018b, p. 20). Novice teachers were asked to rate to what extent their schools’ capacity to provide quality instruction is hindered by 14 different issues such as “insufficient internet access”, “shortage of support personnel”, or “shortage or inadequacy of time for instructional leadership” on a scale from 1 (not at all) to 4 (a lot).

Data for the cohort 2021/22 were collected between end of May and end of June 2022. Overall, 911 novice teachers completed the second questionnaire (384 in the intervention group and 527 in the control group). The questionnaires were translated to the languages of the investigated education systems.

6 Results

6.1 Terminology for disadvantaged schools

The first goal of the document analysis and the expert interviews was to examine the terminology used for disadvantaged schools in the different education systems and by comparison find similarities or differences between the education systems. In the interviews with the educational experts, we asked for the terminology used to refer to disadvantaged schools in the individual education systems. We found that in some cases, the official term used by the government differs from the term that is more widely used in society. For example, one expert in Austria explained:

“There is the term ‘hotspot school’. If you want to classify a school in this way, you say ‘hotspot school’. And in the same breath you say that you don’t want the term at all. But there is no non-stigmatizing term. A ‘hotspot school’ always means a school with ‘hotspot children’ and ‘hotspot teachers’ and ‘hotspot parents’. And the approach is basically terribly stigmatizing. When we talk about these schools, we tend to say schools with major social challenges, which is more descriptive”.

Reviewing the different terms in the participating education systems, we found a distinction between stigmatising terminology and neutral terminology. Among the stigmatising terms were terms such as ‘ghetto school’ (Madrid) and ‘hotspot school’ (Austria), while among the neutral terminology were terms such as ‘high complexity school’ (Madrid, Catalonia) and ‘index school’ (Austria). Among the neutral terminology, we found few terms that were describing the student body, which even though only descriptive could be stigmatising none the less. Examples for this type of terminology were ‘school serving vulnerable students/communities’ (Bulgaria) and ‘school with a high concentration of students living in disadvantaged circumstances’ (Flanders).

Table 4 shows the terms used to describe disadvantaged schools in each of the education systems participating in the NEST project according to the experts we interviewed. In most education systems, multiple terms are used for disadvantaged schools.

Table 4 Terminology used for designating disadvantaged schools in the education systems participating in the NEST project
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6.2 Indicators for disadvantage in schools

To examine what constitutes a disadvantaged school, the second goal of document analysis and expert interviews was to analyse how the educational administration determines which schools are classified as disadvantaged in the different education systems. Thus, we studied which indicators are used in the different education systems to this purpose. We primarily relied on official documents but used the expert interviews to help us clarify questions on the indicators for our analysis. In the case of two educations systems (Bulgaria and Madrid), we had to rely solely on the accounts of the educational experts (see Table 5). For example, a representative of the Bulgarian school evaluation authority walked us through the specificities of the urban-rural divide in the country and its implications for the officially used definition of disadvantage in schools:

“The first coefficient is the regional coefficient. It is in a scale of eight categories. It depends on the size of the place of where the school is, the town where the school is. Your coefficient would be one if you’re living in a big town with good prosperity and good average income per capita and you receive eight if you’re in the smallest place of living where income is not that big. […] In category eight would be the schools that are the most remote from the big towns”.

Table 5 Social Indicators Used in the Education System to Establish Disadvantage in Schools
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In all education systems, these indicators are primarily used for decisions over the allocation of financial or human resources. We found that most education systems use at least four indicators or factors to determine this decision. The indicators refer to the characteristics or a selection of characteristics which educational authorities use to decide if a school is disadvantaged.

The research literature on different indicators used to classify schools as disadvantaged presented at the beginning, suggested the distinction of input and output indicators. Additionally, it is important to consider expanding this typology to include context and process indicators. Context indicators characterize the environment in which educational processes take place, such as the geographical location of a school. Input indicators are indicators which characterise immediate preconditions for pedagogical processes such as students’ family background, teacher qualification or material resources such as computer equipment or books. Process indicators relate to the actual learning activities, such as time for instruction and finally output indicators characterise results of educational processes such as student assessment grades or teacher retention. The research studies mainly yielded a distinction between input and output indicators. The input indicators mentioned in research literature often concern preconditions of the children when entering school such as SES (Kyriakides et al. 2019). Some studies exclusively used input- or output-oriented indicators to characterise the disadvantage of schools, but in others input- as well as output-oriented indicators were used for this purpose (Hall et al. 2022; Martínez 2014). Reviewing the data from the document analysis and expert interviews, we found this distinction between input- and output-oriented indicators as well. Additionally, we found context-oriented indicators, extending the twofold distinction to a threefold distinction. However, again we found no indicators focusing on the learning process. Our data showed a clear preference for input-oriented indicators for most education systems. In all education systems except in Bulgaria and Romania the educational administration only uses input-oriented indicators to designate schools as disadvantaged. In Romania input- and output-oriented indicators are used and Bulgaria is the only country were all three types of indicators are used to classify schools as disadvantaged. This type of distinction between context, input and output is useful in informing the development of pedagogical measures for disadvantaged schools. However, to develop a successful pedagogical measure this distinction which is based on a broad perspective, can be further differentiated by considering in what way the indicators relate to the actors involved in school, respectively the school itself. Reviewing the indicators in this content-related way, we identified that most indicators are related to students; either directly, such as students’ grades and qualifications or are mediated through the parents or social environment of the students such as migration and language, economic situation, parents’ educational background etc. The former ones we refer to as proximal student indicators, the latter ones we refer to as distal student indicators. In our data, one indicator was related to teachers (level of qualification of teachers) and one to the school itself (remoteness of school location).

Table 6 shows the different indicators used by the education systems participating in the NEST project to designate disadvantaged schools. According to our information, most education systems only use student indicators to classify schools as disadvantaged. This means they use either a combination of proximal and distal student indicators as they do in Austria and Catalonia. In Madrid, only proximal student indicators are used and in Flanders and Wallonia only distal student indicators are used.

Table 6 Classification of Indicators for Identifying Disadvantaged Schools in the Education Systems Participating in NEST
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Overall, distal student indicators are used more frequently than proximal student indicators in our data. Among the distal student indicators, the parents’ level of education is most often used to classify schools as disadvantaged (in five of the seven education systems). Economic criteria such as parents’ type of employment, household income or receiving social benefits are used as indicators by four of the seven education systems. In Austria, Catalonia and Flanders language criteria such as the language spoken at home/mother tongue or migration background are used to identify disadvantaged schools.

In Bulgaria and Romania context, input and output indicators are used to classify schools as disadvantaged. In Bulgaria, the type, size and location of a school is a key indicator for establishing the level of disadvantage of a school in Bulgaria (context indicators). Very small and remote schools face many challenges; for example, it is difficult to recruit teachers to these schools because they would have to relocate to the remote area, and sometimes the position is not even full time. In a similar way, the level of socioeconomic development in the area of the school is considered as a contextual indicator in Romania. Both countries consider output criteria in the form of average marks in national assessment tests as indicators to classify disadvantaged schools. Both countries also consider input indicators. However, while Bulgaria uses student indicators, Romania is the only country considering teacher criteria when classifying schools as disadvantaged. The percentage of untrained or unqualified teachers in the school district is deemed an important criterion in identifying disadvantaged schools. Overall, Austria and Catalonia use the highest number of different criteria to designate schools as disadvantaged, whereas Flanders and Madrid use only a small selection of criteria.

After presenting the perspective of the educational administration, we now turn to novice teachers working at disadvantaged schools and their viewpoint on disadvantages.

6.3 Novice teacher perspective on disadvantage at their schools

To investigate the type and amount of disadvantage novice teachers perceived in their specific school contexts, we used data from the second online survey for novice teachers, which contained a question from TALIS on student body composition (Kindlinger et al. 2023). Novice teachers were asked to estimate the percentage of students at their school coming from certain potentially disadvantaged backgrounds. This question focused entirely on the input and mostly included student preconditions mediated through the parents, such as students coming from socioeconomically disadvantaged families, students who speak a different language at home, etc. Further, it included a question on different shortages at school. Novice teachers had to rate on a 4-point scale ranging from 1 (not at all) to 4 (a lot) to what extent their school’s capability to provide quality instruction was hindered by 14 different aspects, such as a shortage of qualified teachers, insufficient internet access, or inadequate time for students.Footnote 1

If we look at these aspects against the background of the context, input, process, output typology, we find that they relate mostly to input for educational processes. But they are much closer to enabling a structure for educational processes that is malleable by the education system or individual school. Especially, the last two of the 14 aspects are malleable on the level of the individual school.

Regarding novice teachers’ perceptions of the composition of the student body at their schools, we noticed high levels of variance within the education systems themselves. However, in the example of Catalonia (Fig. 1) we can see that estimates sometimes tended to cluster in certain ranges. For instance, estimates of the percentage of students with special needs clustered around 20 to 40 per cent, while estimates of the percentage of students who at home speak a language that differs from the language of instruction show a cluster above the average of 70%. Overall, given the variance in the answers, the estimates reflect the indicators used by the Catalan education authority to designate disadvantaged schools: special education needs, language spoken at home, migration background, level of parent education, parent employment, and the receipt of social benefits (see Table 2). Looking at averages, we found that novice teachers in Flanders tended to provide the highest estimates of numbers of students whose language at home differed from the language of instruction (M = 81.1%, SD = 23%, range: 0–100%). The estimates for this category were also high in Catalonia (M = 70.3%, SD = 25.5%, range: 2–100%) and Bulgaria (M = 52.4%, SD = 37.5%, range: 0–100%), but comparatively low in Romania (M = 19.7%, SD = 29.1%, range: 0–100%). Novice teachers in Bulgaria and Flanders also reported high numbers of students from ethnic minorities, with average estimates of 54.3% in Bulgaria (SD = 36.7%, range: 0–100%) and 63.7% in Flanders (SD = 33.5%, range: 0–100%). In all education systems, novice teachers tended to estimate that a substantial part of the students at their school came from socioeconomically disadvantaged homes, with average estimates ranging from 43.3% in Wallonia (SD = 29.8%, range: 3–100%) to 67.4% in Flanders (SD = 26.8%, range: 0–100%). In general, estimates of homogeneity tended to be lower in Romania and Wallonia than in other education systems. However, we saw far more variance in the individual education systems than between systems.

Fig. 1
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Novice Teachers’ Perception of Student Body Composition at Their Schools in Spain (Catalonia)

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Regarding disadvantages in the form of restraints for quality instruction, generally novice teachers answered very similarly in all seven education systems. Figure 2 combines the two education systems with the most contrasting novice teacher perceptions: Novice teachers in Romania perceived the highest restraints compared to novice teachers in the other education systems. While the ones in Bulgaria perceived overall the least restraints. Given the material basis for schooling in Bulgaria and Romania is more similar to each other than compared with the other five education systems, the result is probably not only an example for real differences in each case. However, it is as well an example for cultural frame difference and how demanding it is to perceive and name deficits within a professional context. Therefore, it is important to look not only for different levels of deficits, but also for common patterns and contrasting profiles. Regarding human resources, novice teachers tended to identify a shortage of support personnel as a restraint to their school’s capability to provide quality instruction. This was most pronounced in Romania, where 36% of novice teachers answered that this shortage hindered the quality of instruction ‘a lot’. When additionally considering the answer category ‘quite a bit’, then the shortage of support personnel was most pronounced in Catalonia. Compared to the other education systems, a higher percentage of novice teachers in the Belgian regions found that a shortage of qualified teachers was hindering quality instruction quite a bit or a lot. Regarding material resources, the majority of novice teachers in all countries did not see them as a hindrance to quality instruction at their school. For instance, a shortage of library materials was perceived as a strong hindrance (‘a lot’) by 12% of novice teachers in Flanders and 21% of novice teachers in Romania. In general, novice teachers in all education systems perceived higher restraints regarding human resources (shortage of teachers with specific competences, shortage of support personnel) than material resources such as internet access, books or other instructional material (see Fig. 2).

Fig. 2
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Novice Teachers’ Perspective on Disadvantage at Their Schools in Bulgaria and Romania. (Figures for the other education systems are provided in the online supplement (Novice Teachers’ Perspective on Disadvantage at Their Schools, Fig. 3–6))

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7 Discussion

The document analyses on terminology of and designating indicators used for disadvantaged schools and their indicatorisation brought several interesting results to light. To begin with, we found a distinction between stigmatising terminology and neutral terminology for disadvantaged schools in our data. The choice of a specific term can have a profound impact on disadvantaged schools. On the one hand, a stigmatising term can negatively impact the students attending the school, for instance by leading to less favourable opportunities in the job market for students. Regarding possible effects on teachers and teacher attrition, is also possible that in addition to the known negative effects of various context factors of disadvantaged schools (Allen et al. 2018), the stigma and lack of prestige connected to pejorative terms for disadvantaged schools might be an additional contributing factor (Bettini and Park 2021). On the other hand, it may be easier to justify dedicating extra resources to disadvantaged schools when the term describing the school emphasises that the school is facing problems. A more neutral term might imply that students and teachers at these schools are less negatively affected; however, the technocratic nature of a neutral description makes it more complicated to communicate to policymakers and to wider society why these schools require additional resources. The categorisation of terminology shows that it remains difficult to find a term for disadvantaged schools that encapsulates the challenging situation of the schools without creating a stigma. However, changing terminologies in some education systems, such as Austria, are an expression of an effort to discursively deal with the problem of disadvantaged school settings in such a way that at least discrimination and blaming are avoided. Still, even if a government has found an accepted official term for designating disadvantaged schools, it is difficult to establish the use of the term in wider society and to prevent it from becoming stigmatising in the future. This is probably due to the fact that the different designations carry a variety of different connotations somewhat obscuring what constitutes a disadvantaged school. Thus, the terminologies (apart from the descriptive terms for disadvantaged schools used in Flanders and Bulgaria) remain mostly unclear and no approach for professional action can be derived from them yet. One way to address these challenges might be to involve schools and school networks more in the process of identifying and selecting terminology.

Regarding the indicators used for classifying schools as disadvantaged, we found that in research literature as well as in our data, the following typology could be applied as an ordering structure. While in research literature we only found input and output indicators to describe disadvantaged schools (Hall et al. 2022; Kyriakides et al. 2019; Martínez 2014), in our document analyses and expert interviews from the seven education systems we also found context indicators. However, the majority of education systems base their classification of disadvantaged schools on input indicators only. Only one of the seven education systems used context, input and output indicators. Further investigation yielded that indicators mostly related to student characteristics, either directly concerning students or indirectly mediated through their family background. Only few indicators related to teachers or the school context. However, it may be worthwhile to transfer our typology to other education systems. As another example for a more contextual type of indicator, the Swiss canton of Bern uses the percentage of buildings with low residential use as one of four indicators for disadvantaged schools (Weishaupt 2016).

The indicators used to identify and classify schools as disadvantaged to some extent reflect the restraints or challenges that teachers at these schools perceive. Although specific challenges varied between education systems, we did find overall similar answer patterns in our novice teacher data. Overall, novice teachers perceived moderate restraints or challenges. If they did perceive challenges, they were mostly focused on input (perceived lack of support personnel, lack of materials). This finding supports research literature which has identified limited amounts of resources, both physical and human, as common challenges of disadvantaged schools (Wilson 2021; Tannehill and MacPhail 2017). According to the novice teachers in all education systems, the lack of support personnel was one of the most prominent restraints for quality instruction and they perceived overall higher restraints for quality instruction through lack of human resources. This suggests that measures should be directed less towards mitigating material disadvantages but towards pedagogical interventions to support retention of pedagogical personnel, such as programmes including mentoring, supervision, or resilience training. Concerning novice teachers’ perceptions of student body compositions, we found high levels of variance within education systems, making it difficult to grasp concrete results. This could be grounded in novice teachers’ ignorance of these data about their school environments. As new teachers they might simply not know enough about student body composition. However, it could also indicate that the student body compositions vary strongly even within the group of “disadvantaged schools” in one education system. This in turn would indicate that it is not sufficient to base interventions for disadvantaged schools on the most prevalent average challenges within an education system, but it is instead necessary to develop adaptive interventions better targeted to the individual school.

Against the backdrop of the rather elusive terminology for disadvantaged schools, it might be even more important to define measurable, reliable, and comparable indicators to classify disadvantaged schools, possibly along different dimension of disadvantage. Even though the international comparison enriches our perspective on which conditions can contribute to disadvantage, these different vantage points are usually not integrated into a theoretical model from which starting points for interventions like adaptive mentoring could be directly derived. Instead, there seems to be a lack of indicators at the level of the education systems that capture disadvantage more closely in the educational processes of the students and thus support the derivation of target-oriented pedagogical measures. To cover the whole spectrum of disadvantage, it would make sense to choose a mix of indicators of different types, such as context, input and output indicators. The current choices of indicators could be rooted in the availability of data or in educational theory (divergent ideas about the workings of deprivation). Further research into this area should bring stakeholders from the educational administration, educational policy, educational researchers and practitioners together to inform the development of a practicable indicatorisation further. These stakeholders might also benefit from considering perspectives outside their national frame. Practicable indicatorisation may mean aligning indicators used to designate disadvantage in schools with the pedagogical measures that are necessary in these environments, such as context-specific remedial interventions. Indicators like these could then also be used for the planning of these measures across schools with similar challenges. It seems conclusive that an education system which uses only input-oriented indicators to define disadvantage will—according to its own metrics—not be as successful with a pedagogical measure focused on output-oriented indicators. In addition, the content-related perspective of indicators can inform the planning of remedial interventions such as mentoring programmes. For example, mentoring that is designed based on distal student indicators might need to enable novice teachers to include parents in the learning process of their children instead of only focusing on the students themselves. The use of indicators selected for this purpose would allow for more adaptive approaches to mentoring, which could be beneficial for novice teachers. (Crasborn et al. 2011, van Ginkel et al. 2016). It seems favourable to develop an impact model for remedial interventions which could be considered in future quasi-experimental evaluations of pedagogical measures for disadvantaged schools when measuring the success of a programme.

One major limitation of our study is that our qualitative analysis of the educational administrations’ perspectives is based on a small and non-random sample of data. While we made efforts to gather diverse perspectives from each education system, the sample size and sampling method may limit the generalizability of our findings. Additionally, our reliance on self-reported data from novice teachers through questionnaires may have limitations in capturing the full range of perspectives on disadvantage in novice teachers who work in these contexts. Future research could use a larger and more representative sample and a more diverse set of methods to provide a more comprehensive understanding of the various perspectives within educational administrations.

8 Conclusions

This paper explored the terminology and indicators used to designate and classify disadvantaged schools in various educational systems, as well as the perceptions of novice teachers regarding disadvantage in their specific school contexts. The indicators used to identify and classify schools as disadvantaged tend to relate to student characteristics, with only a few indicators related to teachers or the school context. The perspective of the educational administrations was complemented by novice teachers’ perspectives on various more specified input variables. We found that novice teachers at disadvantaged schools perceived moderate restraints or challenges, often related to a lack of human resources. These results showed that novice teachers’ perspectives on the challenges associated with disadvantage only partially align with the educational administration’s objectives in this context. This discrepancy is relevant to the development of any pedagogical intervention that supports pedagogical personnel, such as mentoring, supervision, or resilience training.