Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence.
powered by
Select sections of text to find additional relevant content using AI-assisted search.
powered by
(Link opens in a new window)
Abstract
This article delves into the profound disruptions caused by the COVID-19 pandemic on educational systems, particularly focusing on the eligibility for matriculation certificates in Israel. By examining data from pre- and post-pandemic periods, the study reveals significant shifts in social gaps and educational outcomes. The research is grounded in the risk society approach, which posits that modern societies are increasingly characterized by the management of risks, including those manufactured by human actions. The article explores how the pandemic, as a manufactured risk, has exacerbated existing inequalities in education, with disadvantaged groups experiencing greater learning losses and reduced access to educational opportunities. The study highlights the adaptive policies implemented by the Israeli Ministry of Education to mitigate these disruptions, such as reducing the scope of matriculation exams and providing more flexible assessment methods. However, it also uncovers the complex trends in educational inequality, where while the percentage of students eligible for a basic matriculation certificate increased, the gaps in eligibility for outstanding matriculation certificates widened, particularly in the Arab sector. The findings underscore the need for inclusive and adaptive educational policies that address the diverse social contexts and structural inequalities shaping school outcomes. The article concludes by emphasizing the importance of leveraging crises as catalysts for social change and the necessity of developing new modes of thinking and organizing within educational institutions to ensure long-term sustainability and social responsibility.
AI Generated
This summary of the content was generated with the help of AI.
Abstract
The COVID-19 pandemic, as a global health crisis, has disrupted schools and students’ lives, and raised concern about an increase in social inequality. Three hypotheses were examined: 1. Between pre-COVID-19, during COVID-19, and post-COVID-19, there will be a decrease over time in the percentage of matriculation certificate (PMC) and outstanding matriculation certificate eligibility (POMC) in schools; (H2) The decrease in PMC over time will be greater in schools in the Arab sector compared to those in the Jewish sector and within each educational sector, in low-socioeconomic schools compared to high-socioeconomic schools; (H3) Sectorial and socioeconomic differences in schools’ POMC eligibility over time will be greater than the PMC eligibility. These hypotheses were tested within the context of the Ministry of Education modification of the format of matriculation exams, at 12th grade. The data included 863 schools in the Jewish and Arab sectors on four time points: pre (2019), during (2020-2021), and post-COVID-19 (2022). Descriptive analysis and a two-level linear mixed model with repeated measures were conducted. An increase in the percentage of matriculation certificate eligibility was found in schools, particularly in disadvantaged ones, reducing sectoral and socioeconomic inequality. At the same time there was an increase in the percentage of outstanding matriculation certificates, mainly among advantaged schools, leading to an increase in inequality. The findings indicate a decrease in vertical stratification and an increase in horizontal stratification, emphasizing the complexity of promoting educational opportunities in the era of risk society.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Introduction: The Challenges of COVID-19
The COVID-19 pandemic has caused disruptions to social activities and educational practices worldwide. According to Williamson et al. (2020), nearly 90% of students had to adjust their schooling routines. This has posed challenges to schools, which are supposed to be the great equalizers (Downey et al., 2004), striving to provide proper education to all students, for example by implementing policies that tackle social inequality. The present study contributes to existing literature in two aspects: 1. by examining the impact of the disruption caused by school closure during COVID-19 on school outcomes as measured by the percentage of high-school credentials, and 2. by exploring differences between schools serving diverse ethnic and socioeconomic groups. First, we present the study’s framework, which is based on the risk society approach, and then relate it to the research on school achievements and COVID-19. We proceed to the Israeli setting and the policy of the Ministry of Education (MOE) regarding school credentials during COVID-19. Two additional parts focus on methodology and findings. Finally, we discuss our findings and provide theoretical and practical insights.
Risk Society and Inequality
The past few decades have been saturated with changes and social crises, providing Beck (1999) with an opportunity to develop a new intellectual horizon and theoretical perspective. According to Beck (1999), since the post-Second World War, we have been living in an age of reflexive modernity, indicating that modernity has become a problem in itself, and it needs to begin to transform its institutions and the principles that underlie modern society (Beck, et al., 2003).
Advertisement
Beck (1992) introduced the concept of the “risk society” to explain the transition from traditional industrial societies with stable institutions and predictable social structures to the “second modernity,” where the primary focus is on managing and coping with various risks. In a risk society, there is a constant probability of being in a state of risk, reflecting that “trust in our security ends and ceases to be relevant when the potential catastrophe occurs” (p. 135). Risks or “bads” are not limited to natural hazards or disasters but also include “manufactured risks”, that are embedded in the processes for improving quality of life by relying on technological advancements and industrialization. These human actions have resulted in the need to cope with unpredictable and often unintended consequences that affect all social groups (e.g., social class, ethnicity, or gender). Furthermore, no one can escape the ‘manufactured risks’ (Curran, 2013). Hence, social group, like social class, becomes irrelevant and inadequate for explaining the current social world phenomenon, such as inequality in the distribution of “bads”. In other words, in risk-society, class is a too soft category to capture social inequality (Beck, 2013).
Several researchers contest Beck’s approach and argue that risk is an additional dimension of social stratification associated with social group (e.g., class) (Curran, 2016; Mythen, 2005). By incorporating critical perspectives (e.g., Bourdieu) into the risk society approach, we can assume that social groups with more resources (e.g., wealth) are less exposed and vulnerable to risks and can deal more effectively with bads or risk/crisis than groups with fewer resources (Curran, 2016). These differences eventually deepen social inequality and increase social injustice.
Researchers have characterized COVID-19 as a manufactured risk (Pietrocola et al., 2020), in which disadvantaged groups were exposed to its negative consequences and impact more than advantageous social groups (Constantinou, 2021). While this idea has been explored in relation to economic or environmental issues, it can also be adopted vis-à-vis inequality within the educational field, as presented below.
COVID-19 and Inequality in Education
As part of global competition, educational systems strive to improve student outcomes, while reducing social gaps and providing educational opportunities for diverse social groups (OECD, 2023), a goal considered one of the central pillars of public education (Merry & New, 2016). The ethos of educational opportunities reflects the efforts to achieve social stability, economic prosperity, well-being, and maintain social justice (Savaş-Yalçın & Koşa, 2021). These issues have gained greater attention during COVID-19 (Haelermans et al., 2022), as diverse groups may have experienced considerable learning loss (Donnelly & Patrinos, 2021).
Advertisement
However, there are no consistent findings regarding the impact of COVID-19 on achievement levels and social gaps (Agostinelli et al., 2022). Some studies indicate that during COVID-19, schools improved students’ achievement (Donnelly & Patrinos, 2021; Hammerstein et al., 2021). Other studies report that there was no compromise in learning and no difference in achievement between pre- and during COVID-19 (Gore et al., 2021), while others report a decline in student achievements (Zierer, 2021). Further, a few studies suggest that there was little or no evidence for an increase in achievement gaps during school closures (Ludewig et al., 2022). In others, there is mixed evidence. For example, in some regions such as Metro-Atlanta, an increase in the race/ethnicity gap in school achievement was observed, while in other regions, there was no change (Sass & Mohammad Ali, 2022). Similar trends were found in the 2022 PISA results, which showed that in some countries, education systems made progress toward closing the achievement gap (PISA, 2022), while in others, the achievement gap based on socioeconomic groups remained the same as in 2018 (OECD, 2023). Yet, the prevailing dominant trend indicates that in many countries, school closures during the pandemic have led to a decrease in students' achievements and an increase in social gaps (Ludewig et al., 2022).
The decline in the performance levels of students occurred differently across social groups (Hammerstein et al., 2021; Schult et al., 2022). Disadvantaged and vulnerable groups (e.g., migrant parents, single mothers, parents with low socioeconomic status), were found to be the most at risk during the pandemic (Borg & Mayo, 2022). The children of these groups experienced more learning loss compared to advantaged groups (e.g., Haelermans et al., 2022), leading to an increase in social inequality on top of the already exciting one (e.g., in the Netherlands, Engzell et al., 2021; Schuurman et al., 2023; in United States, Kuhfeld et al., 2022; in China, Liao et al., 2022). Advantaged groups, such as parents with higher education and white-collar jobs, knew better how to cope with the challenges posed by COVID-19 and were more likely to provide support and engagement toward their children’s learning (European Commission, 2020), which helped to mitigate the negative impact of distance learning on student achievement (Liao et al., 2022). In contrast, socially disadvantaged families lacked resources (economic, technological and knowledge), and the ability to support their children’s learning (Goudeau et al., 2021). Thus, the achievements and learning gaps between the groups may increase.
Gaps also existed between schools. Schools serving high concentration of disadvantaged populations face multiple challenges, including lower prior achievements, limited learning resources at home, and a shortage of qualified teachers, particularly in high-demand subjects (Sullivan & Whitty, 2007). Further, coping with the pandemic was especially difficult for these schools, which had fewer technological resources to support effective distance learning compared to schools serving more advantaged populations (Andrew et al., 2020). They reported about lower student participation in learning activities (Kuhfeld et al., 2022), and that teachers were often unprepared for new teaching methods. Parents also received little support or guidance from schools (Goudeau et al., 2021). Teaching was further hindered by families’ struggles with distance learning (Frohn, 2021). In contrast, schools serving advantaged students managed the crisis more effectively, leading to higher student achievements (e.g., in the U.S.A., Kuhfeld et al., 2022; in Germany, Schult et al., 2021, 2022).
In response, educational systems worldwide implemented policies to address school closures and many countries canceled or postponed national standardized tests (Bozkurt et al., 2020). In Italy, language and math assessments were suspended until 2022 (Contini et al., 2023), while the Netherlands, Egypt, and Ireland adopted alternative assessment methods (e.g., research projects) to replace standardized exams (Bozkurt et al., 2020). These policies prioritized disadvantaged students (Reimers, 2022; Savaş Yalçın & Koşar, 2021) to provide them with learning opportunities, recognizing that their families often lacked resources to support their education (Doyle, 2020). A similar approach was taken in Israel, the setting of this study.
The Israeli Context
Since the establishment of the State of Israel in 1948, social equality and social justice have been major concerns and guiding policy principles. Israeli citizens tend to react to social injustice (Shavit et al., 2014), and frequently the government formally addresses social injustice as a part of its policies and responsibilities. This includes the field of education, which is considered a promising setting for promoting social justice by improving children’s future prospect, well-being and social mobility (e.g., Clark et al., 2018). Providing educational opportunities for diverse students is part of the educational system’s goals, as stated in the circular of the Director-General of the MOE (2000): “[giving] equal opportunities to every boy and girl, [allowing] them to develop in their own way… and [striving] for social justice in the State of Israel”. This is vital as Israel is a socially diverse society, where there are significant differences in ethnicity and socioeconomic status (SES).
The population of Israel is almost 10 million, 75% being Jewish, and the remaining 25% comprising Muslims (17%), Christians (2%), Druze (2%), and others (4%). This diversity is reflected in the educational system. To be more responsive to the needs and cultural orientations of the different social groups, the educational system includes two distinct and segregated sectors: Jewish (including secular, Jewish-religious and Ultra-Orthodox)1 and Arab (including the Druze and the few Bedouin and Cherkasy schools).2 Schools in the Arab sector have significantly fewer resources and weaker technological infrastructure (Schejter & Tirosh, 2016; Zeedan & Hogan, 2022), less effective teacher training (Suwaed & Ali, 2016), a more rigid curriculum, and fewer learning and innovation opportunities (Kaplan & Yahia, 2017).
Additionally, Arab schools have a higher concentration of low-SES students than those in the Jewish sector (Addi-Raccah et al., 2015). Socioeconomic segregation exists within both sectors but manifests differently. In the Jewish sector, it reflects broader economic segregation between localities (Milgrom, 2015), whereas in the Arab sector, it indicates social stratification within communities. As a result, schools are highly homogeneous and socially segregated (Addi-Raccah et al., 2015). These disparities contribute to significant differences between schools. For example, 47% of the variance in reading performance on the 2018 PISA exam was between schools—far exceeding the OECD average of 29% (Ainley et al., 2022). In this context, sectoral and particularly socioeconomic disparities are closely linked to educational outcomes at all grade levels.
At the end of secondary school, at 12th grade, students undergo matriculation exams. The exams are not mandatory, but they are a normative expectation for all high school graduates and serve as a prerequisite for enrolling in higher education. The matriculation exams are a series of standardized tests in seven core school subjects—Hebrew or Arabic (depending on the school sector), English as a second language, Mathematics, History, Civic Education, Literature, and Bible/Religious Studies —as well as additional elective subjects, typically totaling eight to ten exams, administered at predetermined dates for all test-takers.
Each subject is offered at different levels, usually ranging from one to five study units, corresponding to the subject's level of difficulty and depth. Students who earn a matriculation certificate that includes advanced English (five units) and advanced mathematics (four or five units) and achieve a final average grade of at least 90 (out of 100) in all exams are eligible for an outstanding matriculation. At the school level, the percentage of students eligible for a matriculation certificate (PMC) or outstanding matriculation certificate (POMC) signals school’s success, prestige, and commitment to high-quality education and academic standards. Schools are ranked by the percentage of students eligible for a matriculation certificate, and each year, top-ranked schools are published in the media and by the MOE.3 In practice, schools vary in their PMC and POMC. In the Arab educational sector, which offers a limited range of academic subjects, particularly in advanced courses (Alhaj, 1996, in Livne, 2017), the PMC and POMC are lower than in the Jewish sector (e.g., Dadon-Golan et al., 2019; Addi-Raccah, 2022; Zeedan & Hogan, 2022; Friedlander et al., 2016). Within each sector, schools with a high SES tend to have higher PMC, and students are more likely to enroll in advanced mathematics courses (Blank et al., 2016; Friedlander et al., 2016).
Educational Inequality in Israel During COVID-19
In Israel, schools were closed intermittently from March 13th, 2020 (The Crisis Experts’ Teams: Education Team, 2020; Weissblay, 2020a) to January 28th, 2022, which resulted in a total of 33 weeks of closure (UNESCO, n. d.). However, the transition to distance learning affected more significantly disadvantaged populations, such as those of low SES and Arab population (The Crisis Experts’ Teams: Education Team, 2020; Pinson et al., 2020). These groups had limited accessibility to digital devices and internet, making it difficult for parents to assist in their children’s learning (Addi-Raccah & Seeberger Tamir, 2022; Pinson et al., 2020). Further, schools that serve students of low SES, had difficulties producing high-quality learning and maintain their routine (The Crisis Experts’ Teams: Education Team, 2020). This was a significant concern for 12th grade students who needed to graduate school.
To support students and enable them to successfully pass their matriculation exams, many schools provided resources and launched new initiatives. For instance, schools serving disadvantage population, provided computers for distance learning, and teachers visited students’ homes to help them use their computers (Shabbat, 2021). School principals encouraged students to take the matriculation exams, provided academic support (such as tutoring sessions), engaged parents to support student learning, and reallocated resources to enable more students to succeed in the exams (Addi-Raccah et al., 2023).
At the state level, the Israeli MOE restructured the composition of the matriculation exams by setting new guidelines (Vurgan, 2020). The scope of the material covered for the exams was reduced by 25%, and students were given more choices between exam questions (Weissblay, 2020b; MOE, 2020). Moreover, the division between external and internal exams changed. While pre-COVID-19, students took external standardized exams in at least seven school subjects mentioned above, during and post-COVID-19, students were required to take external exams in school subjects: English, mathematics, language (Hebrew or Arabic), and only one of the humanistic subjects (e.g., literature, Bible, citizenship, or history). Students who took advanced science classes had to take one external exam in a scientific subject (i.e., physics, chemistry, biology, or computer science) (Weissblay, 2020b; MOE, 2020).4 All other school subjects were internal and administered by the school.
The changes in the guidelines of matriculation exams were intertwined with the possible impacts of school closure. The aim of this policy was to maintain, during the challenging time of COVID-19, the PMC. However, it is important to keep in mind that since the 1980s, the MOE has implemented diverse policies to raise PMC (Ayalon & Shavit, 2004). While most of these reforms increased schools’ PMC, their implications for social gaps were more complex. We can observe a reduction in socioeconomic inequalities in terms of PMC; however, at the same time, inequalities regarding eligibility for a high-quality certificate, particularly one with an advanced mathematics or English qualification, have persisted (Addi-Raccah, 2022; Vaisbaum-Gani, 2022). Increasing gaps also occurred between the two educational sectors (Vaisbaum-Gani, 2022). This means that due the different reforms, more advantaged groups benefited from the changes in the educational policy to gain a better matriculation certificate, supporting horizontal stratification within the educational system (Ayalon & Shavit, 2004; Lucas, 2001).
Research Hypotheses
Based on the above literature, we can expect that following the COVID-19 pandemic:
(H1) Between pre-COVID-19, during COVID-19, and post-COVID-19, there will be a decrease over time in the PMC and POMC in schools.
(H2) The decrease in PMC over time will be greater (1) in schools in the Arab sector compared to those in the Jewish sector and (2) within each educational sector, in low-socioeconomic schools compared to high-socioeconomic schools.
(H3) The socioeconomic differences in schools’ POMC eligibility over time will be greater than the PMC eligibility. This will be more prominent in the Arab sector compared to the Jewish sector.
Data Sources and Sample
The data was obtained from administrative records of the MOE website,5 which are published at the school level. The data includes information on schools’ matriculation eligibility, socioeconomic level, and several characteristics of the schools and teaching staff. Based on school identification numbers, we created a dataset that merged information over four points of time: 2019 prior to COVID-19, 2020–2021 during periods of intermittent school closures, and 2022 during schools’ reopening when the pandemic subsided.6 The dataset covered all 1014 schools affiliated with the Jewish and Arab sectors.7 Complete data was available for 863 schools (85%), which had full PMC and POMC for the four time points.
Variables
Dependent Variables
1.
Percentage of students in each school eligible for a matriculation certificate- Reflects the percentage of students in the school who passed the standardized matriculation exams (PMC).
2.
Percentage of students in each school eligible for an outstanding matriculation certificate- Reflects the percentage of students who took standardized exams in four or five units of mathematics and five units of English (advance level) and whose weighted average score, based on the number of study units, was above 90 in all matriculation exams (POMC).
Central Independent Variables
1.
Time intervals—Four time points were addressed: 2019 (pre-COVID-19 as the reference category, coded 0); 2020 and 2021 (during COVID-19, coded 1 and 2, respectively); 2022 (post-COVID-19, coded 3).
2.
Educational sector—Defined as a dummy variable: 0. Jewish sector; 1. Arab sector. 25% of schools are affiliated with the Arab sector.
3.
School SES—Defined by a cultivation index,8 ranging from 1 (high SES schools) to 5 (low SES schools). The cultivation index consists of four components: Income per capita in the family (20%); Distance from Israel’s center (20%); Percentage of students who immigrated from underdeveloped countries (20%); Parents’ education level (40%). The index is aggregated at the school level. For ease of interpretation, the index was reversed, so higher values represent schools with higher SES. The average reversed SES value is 3.01 (SD = 3.1).
Controlled Variables
1.
School size—Based on the number of students per school. On average, high schools have 621 students, with substantial variation across schools (SD = 457.43). Due to this disparity, we applied a log transformation to the number of students.
2.
Median years of teachers’ seniority per school—Defined as the median years of teaching experience among all teachers in a school. This variable was computed for each school (the average median was 16.23 years; SD = 5.08).
3.
Percentage of teachers with M.A. or Ph.D. degrees—computed for each school. On average half of the school’s teachers hold a master’s or Ph.D. (M = 49%; SD = 11.8%).
These two variables—seniority and education—reflect teachers’ qualifications in schools.
4.
School structure—Defined as a dummy variable: Secondary schools (six-year schools, grades 7 to 12) = 1 and three- or four-year high schools (grades 9 to 12) = 0. Prior studies have shown that six-year secondary schools have a higher PMC than three- or four-year high schools (Addi-Raccah, 2023). In our sample, 63% of schools were six-year secondary schools.
5.
School track—Defined as a dummy variable: 0 = Technology schools; 1 = Academic schools. Academic schools are designed to prepare students for matriculation exams.
6.
Percentage of students with special needs – This variable was included in line with Israel’s inclusion policy, which requires schools to enroll students with special needs. These students typically study only part of the subjects related to the matriculation exams. On average, schools enroll 7.19% (SD = 7.54) of special needs students.
Data Analysis
To examine the hypotheses, we conducted descriptive statistical analysis and applied a two-level linear mixed model with repeated measures. According to Heck et al. (2013), this analysis examines three key questions: 1. Are there changes over time in the PMC/POMC? 2. If so, what is the shape of the change trajectory? 3. Do these changes over time vary based on schools’ SES or sectoral affiliation? Answering these questions helped us design the linear mixed model as presented in detail below.
Findings
Descriptive Statistics
For testing the first hypotheses, we examined whether the means of PMC and POMC differed across time. Analysis of variance revealed a significant difference between the four time points: pre-COVID-19 (2019: M = 75.35; SD = 23.05), during COVID-19 (2020: M = 79.51; SD = 21.04; 2021: M = 81.83; SD = 19.29) and a smaller increase post-COVID-19 (2022: M = 81.68; SD = 18.48; F(3,860) = 104.88, p < 0.01). A significant similar trend was found for POMC (F(3,860) = 147.03, p < 0.01): in 2019: M = 8.54, SD = 10.36, during pre-COVID-19 in 2020: M = 11.01, SD = 12.21 and in 2021: M = 13.11 SD = 13.87, followed by a smaller change in 2022: M = 13.04, SD = 13.13. Furthermore, schools varied in their growth rates (for PMC: F = 151.24, df = 3,2598, p < 0.01; for POMC: F = 171.17, df = 3,2598, p < 0.01), with significant linear and quadratic patterns, which were considered when testing our research hypotheses.
To test hypotheses H2 and H3, longitudinal data with repeated measures were analyzed using SPSS Linear Mixed Models, conceptualized as a two-level analysis. Several tests were conducted to assess the model’s fit to the data. Given the distinct separation between the Jewish and Arab sectors, we examined whether a single model or separate sector-specific models would better fit the data. For this purpose, we first compared the Jewish and Arab sectors in relation to the research variables. As expected from (H2) and shown in Fig. 1, PMC was higher in Jewish sector schools than in Arab sector schools (F = 74.804, df = 1,862; p < 0.001). However, contrary to (H2), during COVID-19 (2020–2021), PMC increased in both sectors, narrowing the gap (F = 36.642; df = 1,862; p < 0.001), mainly due to a steeper rise in Arab sector schools (from 63.62% to 74.10%).
Fig. 1
Trends over time of PMC by educational sector (2019–2022)
Regarding POMC, Fig. 2 shows that schools in the Jewish sector maintained significantly higher scores across all years (F = 8.311, df = 1,862; P < 0.001). Over time, both sectors show an increasing trend in POMC, followed by a slight stabilization in 2022, with a consistent gap between the Jewish and Arab sectors (F = 3.629, df = 1,862; p < 0.06).
Fig. 2
Trends over time of POMC by educational sector (2019–2022)
School SES, the central variable in this study, showed a prominent difference between sectors. School SES was higher in the Jewish sector (M = 3.51; M = 1.68) with a left-skewed distribution (− 0.444), whereas in the Arab sector, it had a right-skewed distribution (1.478). Comparing sector-specific models to a single combined model, we found better goodness-of-fit indices for the sector-specific models. Based on these findings, we conducted separate analyses for each sector.
In addition, based on repeated measures analyses, we found changes over time in all school variables except for school SES, underscoring the need to consider time-varying variables. However, within each sector, their predictive effects on PMC and POMC were insignificant; they did not contribute to the explained variance or improve the model. Accordingly, for parsimony, these variables were excluded.
Therefore, for each educational sector, our model included two levels. At Level 1, we specified within-school growth rates (repeated measures) in PMC (or POMC) to control for within-school variance. At Level 2, we examined the random variation in school intercepts (i.e., school-level differences in outcome) and time slopes (i.e., variability in growth trajectories across schools), with school SES and other school characteristics which were included as control variables. The following equation presents this model:
The \(\text{intercept parameter} {(\beta }_{00}) \text{ represents the}\) initial PMC (or POMC) at a school. The slope parameters (\(\beta\)10 and \(\beta\)20) indicate the predicted change over a specified time interval. Specifically, \(\beta\)10, the linear component, represents the yearly growth rate for each school, describing the rate of change per unit of time. \(\beta\)20, the quadratic component, captures acceleration or deceleration in the rate of change over time, defining the nonlinear growth trajectory within schools. The explanatory model for school growth rates includes a randomly varying component (Heck et al., 2013): The intercept (\({\beta }_{00})\) and the linear time component (\({\beta }_{10}\)) vary randomly across schools (\({u}_{0i}, {u}_{1i}tim{e}_{ti}, \text{ respectively})\). The time slope determines whether the outcome means remain equivalent across different time points and assesses whether schools differ in their growth trajectories at each time interval t. Additionally, the equation includes a vector of control variables (\(\sum {\beta }_{0x}{X}_{i}\)), which are centered around their sector means, along with the remaining unexplained variance\({(\varepsilon }_{ti})\). To test the hypothesis that schools’ growth trajectories vary by SES within each sector, we also included interactions between SES and time (\({\beta }_{13}SE{S}_{i}*tim{e}_{ti} ; {\beta }_{14}SE{S}_{i}*quadtim{e}_{ti}\)).
PMC and POMC in the Jewish Sectors
Focusing on the Jewish sector, in Table 1, the null model in column 1 indicates that 7.6% of the PMC variance stems from within-school differences (residual/repeated measures), while 92.4% is attributed to between-school differences.9 That is, differences in PMC between schools are far more pronounced than those within schools.
Table 1
Results of two-level linear mixed models with repeated measures in the Jewish sector
PMC
POMC
1-Null model
2
3
4-Null model
5
6
Estimate
Estimate
Estimate
Estimate
Estimate
Estimate
Intercept
85.794*
79.129*
73.098*
8.940*
8.950
8.248*
Time
4.911*
4.833*
3.616
3.622*
Time quadratic
− 1.065*
− 1.031*
− 0.660
−0.662*
School structure
8.906*
0.796
School track
−0.585
0.017
School size
5.136*
0.488
Median years of teachers’ seniority per school
−0.037
0.110
Percentage of teachers with M.A. or Ph.D. degrees
0.171*
0.124*
Percentage of students with special needs
− 3.143*
−1.254*
School SES
5.019*
3.468*
Time*school SES
− 1.708*
1.248*
Time quadratic*school SES
0.316*
− 0.239*
Unexplained variance
Residual
37.059*
34.847*
34.475*
23.371*
22.554
22.374*
Random intercept
437.491*
407.068*
212.456*
93.975*
93.889
55.286*
Random time slop
10.613*
8.117*
7.118*
3.526*
1.021*
0.548
*P < 0.05
In column 2, we examined the variance between schools across time, showing that PMC increases over time (4.911), though at a diminishing rate (time quadratic = − 1.065). Time accounted for only 6.9% of between-school differences, and 23.5% of the variance in schools’ trajectories over time.
Column 3 introduces school variables, including SES and its interaction with time. The variables were centered around the sector mean, representing a typical school in the Jewish sector. Time and the quadratic term remain significant. Higher PMCs were found in large schools, those with a high percentage of teachers holding advanced academic degrees, and six-year secondary schools. In contrast, a higher proportion of students with special needs was significantly and negatively associated with PMC. Median years of teachers’ seniority and school type (technology/academic) were not significant. SES predicted PMC, with higher SES associated with higher PMC (5.019; p < 0.01). Over time, SES-related differences decreased (− 1.708; p < 0.01) due to a steeper PMC increase in low-SES schools. This trend slightly slowed (0.316; p < 0.01), as shown in Fig. 3, which depicts predicted PMC in relatively low-SES (below 1 SD) and high-SES (above 1 SD) typical Jewish-sector schools.10
Fig. 3
Trends over time of PMC by school SES in the Jewish sector (2019–2022)
The predicted SES gap was larger pre-COVID-19 (66.22% vs. 79.97%) than during COVID-19 (71.93% vs. 81.87% in 2020; 74.72% vs. 82.57% in 2021) and continued narrowing after schools reopened (74.57% vs. 82.07 in 2022). Contrary to (H2), the gap decreased from 13.75% in 2019 to 7.5% in 2022 (a 45% reduction), driven by a steeper PMC increase in relatively low-SES schools (from 66.22% to 74.54%) compared to high-SES schools (from 79.97% to 82.07%).
The school variables including time explained 32.9%11 of the variance in schools’ trajectories over time and 51.4% of the between-school variance in PMC.
For POMC, the null model in column 4 showed that 80.6% of the variance was between schools and 19.4% within schools. Column 5 of Table 1 revealed similar trends to PMC, with POMC increasing over time but at a decreasing rate (time = 3.616, quadratic term = − 0.660). Compared to 2019, POMC rose by 2.959% in 2020, 4.596% in 2021, and 4.908% in 2022. Column 6 included school variables and the time-SES interaction. The findings showed that the percentage of teachers with MA or PhD degrees and the percentage of students with special needs had significant effects, similar to PMC. However, unlike PMC, POMC exhibited a different SES-time interaction. Higher school SES was associated with higher POMC (3.468), and over time, the advantage of high-SES over low-SES schools increased (1.248) at a moderate rate (− 0.239). No other variables had significant effects. The predicted POMC in a typical low-SES (below 1 SD) and high-SES (above 1 SD) Jewish school is presented in Fig. 4.
Fig. 4
Trends over time of POMC by school SES in the Jewish sector (2019–2022)
The predicted POMC in a typical low-SES school was 3.50% pre-COVID-19, compared to 13.00% in high-SES schools. By 2022, the SES gap widened, with predicted percentages of 6.22% for low-SES schools and 20.09% for high-SES schools. In other words, changes over time were steeper in high-SES schools. The SES gap grew by 46%, increasing from 9.5% (in 2019) to 13.87% (in 2022). These findings highlight SES differences in POMC over time compared to PMC, aligning with (H3). All the variables explained 46.3% of the variance in the trajectory over time and 84.4% of the between-school variance in POMC.
PMC and POMC in the Arab Sector
For the Arab sector, the null model in Table 2 showed that 95.22% of the variance in PMC was between schools, while only 4.78% was within schools.
Table 2
Results of Two-Level Linear Mixed Models with Repeated Measures in the Arab Sector
PMC
POMC
1-Null model
2
3
4-Null model
5
6
Estimate
Estimate
Estimate
Estimate
Estimate
Estimate
Intercept
73.134*
62.968*
63.965*
5.204*
6.600*
6.977*
Time
7.006*
6.820*
2.482*
2.648*
Time quadratic
− 1.128*
− 1.077*
− 0.399*
− 0.445*
School structure
− 1.187
− 0.609
School track
− 0.649
0.332
School size
8.655*
0.853
Median years of teachers’ seniority per school
− 0.049
0.125
Percentage of teachers with M.A. or Ph.D. degrees
0.349*
0.122*
Percentage of students with special needs
− 4.463*
− 1.952*
School SES
11.676*
6.383*
Time*school SES
− 1.819*
2.654*
Time quadratic*school SES
0.195
− 0.586*
Unexplained variance
Residual
31.968*
29.967*
30.173*
12.544*
12.407*
11.933*
Random intercept
612.812*
537.442*
296.717*
108.829*
105.458*
36.335*
Random time slop
24.248*
11.270*
9.685*
3.185*
1.533*
0.899*
P < 0.05
The data in column 2 indicated an increase in PMC over time (7.006), though this growth moderated (− 1.128). The average PMC was 62.968% in 2019, 68.846% in 2020, 72.468% in 2021, and 73.834% in 2022.
In Column 3, variables were added after being centered around the sector mean. A similar pattern of significant effects was found as in the Jewish sector. However, in the Arab sector, SES-related differences over time remained more pronounced than in the Jewish sector (SES = 11.676, time = − 1.819, quadratic time = 0.195), as shown in Fig. 5.
Fig. 5
Trends over time of PMC by school SES in the Arab sector (2019–2022)
Figure 5 shows that in 2019, the gap between a typical low-SES Arab school (1 SD below the SES average) and a high-SES school (1 SD above) was 23% (75.52–52.41), decreasing to 15.78% (82.62–66.84) in 2022—a 34% reduction. All variables explained 51.6% of the between-school variance and 60.0% of the variance in over-time growth.
For POMC, the null model shows that 89.9% of the POMC variance is between schools and 10.1% within schools. Further, columns 5 and 6 in Table 2 indicate similar trends for POMC, which increased over time but at a slowing rate. Based on column 5, compared to 2019, POMC increased by 2.083% in 2020, 3.368% in 2021, and 3.855% in 2022. All variables in column 6, similarly influenced POMC as PMC, except for school size, which was not significant. It was found that high-SES schools had higher POMC than low-SES schools (6.383). Further, the SES gap widened over time (time*school SES = 2.654, quadratic term*school SES = − 0.586). Figure 6 illustrates the predicted POMC in typical low-SES (1 SD below average) and high-SES (above 1SD average) schools in the years 2019 to 2022.
Fig. 6
Trends over time of POMC by school SES in the Arab sector (2019–2022)
Figure 6 shows an increasing SES gap over time. While the gap was 13.53% pre-COVID-19, it grew to 18.58% in 2022—a 37% increase. Low-SES schools maintained consistently low POMC levels, while high-SES schools saw a significant rise (from 13.74% to 20.32%). In fact, high SES schools in the Arab sector did as well as high SES Jewish schools. Compared to the null model, all variables explained 66.6% of the between-school variance and 71.8% of the variance in growth over time, which is a considerable percentage.
Overall, the findings indicate that during COVID-19, PMC and POMC increased across all schools, compared to 2019.These findings reject (H1). Over time, SES gaps in PMC narrowed in both sectors, while SES gaps in POMC widened. This trend was more pronounced in the Arab sector. These findings seem to partially support (H2) and (H3).
Conclusions
This study examined the patterns of inequality in the Israeli educational system during COVID-19 as a period of crisis. Accordingly, we examined the percentage of students in school who are eligible for a matriculation certificate (PMC) or eligible for an outstanding matriculation certificate (POMC), in four different time points: pre-COVID-19 (2019) when schools functioned regularly, during COVID-19 (2020–2021) when schools were closed intermittently due COVID-19 restrictions, and post-COVID-19 (2022), when schools reopened.
Based on the risk society approach and its critics, we proposed three hypotheses. Since risks are assumed to affect all social groups equally (Beck, 1992; Curran, 2013), we hypothesized (H1) that the pandemic, as a global crisis disrupting educational systems, would decrease schools’ PMC and POMC. This hypothesis was not confirmed. Instead, during COVID-19, there was an increase in PMC and POMC. One possible explanation is related to the adaptation of matriculation policy during COVID-19, which may have created more favorable testing condition with less strict supervision, that made POMC and particularly PMC easier to pass. The matriculation policy probably gave schools the opportunity to encourage students to participate in the matriculation examinations. In this context, the matriculation policy during COVID-19 helped to temporarily boost PMC and POMC, as by 2022 the increase in school growth rate stalled.
The second and third hypotheses (H2, H3) suggest that the negative effects of school disruption would be more evident among disadvantaged groups than among advantaged groups. This hypothesis was based on Curran’s (2016) idea of inequality in the distribution of bads. The findings partially supported these hypotheses and revealed complex trends depending on the types of matriculation certificate and educational sector.
Contrary to (H2), socially disadvantaged schools (i.e., affiliated with the Arab sector, and relatively low-SES schools within each sector), managed to improve PMC, thus narrowing the social gap. This finding aligns with a prior study in which school principals reported that, during COVID-19, policy changes were perceived as an opportunity to enhance school outcomes (Addi-Raccah, et al., 2023). As such, while schools have equalizing potential during regular operation (Downey et al., 2004), when schools do not fully operate, such as during the COVID-19 crisis, this potential requires an adaptive policy intervention to prevent widening inequalities. Changes in the matriculation exam guidelines can be seen as such an intervention, albeit to a limited extent, as high-SES schools were simultaneously found to increase the POMC. This widened sectorial gaps and, within each sector, increased SES gaps especially in the Arab sector, supporting (H3). While school SES appears to be a central variable associated with PMC and POMC, over time, it had a differential impact on PMC compared to POMC.
Hence, alongside the decrease in quantitative or vertical stratification (i.e., PMC) between schools, there was an increase in qualitative or horizontal stratification based on the quality of matriculation certificates (i.e., POMC), as suggested by Lucas (2009) in ‘regular’ times (Ayalon & Shavit, 2004).
Apparently, during COVID-19, more advantaged schools were able to capitalize on the reduced number of external tests and the decreased scope of math and English learning materials, prioritizing these subjects to increase POMC. Further, these schools may have provided distinctive ‘qualitative’ experiences and opportunities that helped students achieve an outstanding matriculation certificate (Lucas, 2001). For example, they may have provided additional learning support in mathematics and English over other subjects, or benefited from school closures, which allowed students to better focus on their studies as extracurricular activities were limited (Addi-Raccah et al., 2023). Indeed, the ‘opportunity to learn’ and the resources provided by schools during and after COVID-19 appeared to be more beneficial for advantaged social groups, as documented in other studies (e.g., Haelermans et al., 2022).
As such, regarding POMC, contrary to Beck’s view that crises affect all equally, reducing the role of SES, this study supports Curran’s argument that exposure to risks exacerbates SES-based inequality. Schools serving students from advantaged backgrounds may be better equipped to manage risks (e.g., Constantinou, 2021; Curran, 2016). Moreover, while a national-level policy was implemented to mitigate school disruption, differences persisted and even increased based on SES and educational sector. In this sense, although schools are often framed as mechanisms for equalizing opportunities, they may also maintain and reinforce pre-existing differences depending on contextual factors. Thus, while crises can serve as critical moments for reassessing educational opportunities, this reassessment must be conducted through the lens of diverse social contexts and structural inequalities that shape school outcomes.
At a time when crises and manufactured risks have become common in many societies, the measures taken to navigate these crises can inadvertently reinforce social structures and inequalities. However, crises can act as catalysts for social change. Following Beck, institutions such as schools must be adaptive and inclusive, embracing uncertainty and developing new modes of thinking, organizing, and acting that prioritize long-term sustainability and social responsibility. This can help mitigate unintended consequences (Beck, 1999) and ensure a fair and just society.
Future research should examine the practices schools used to provide opportunities for all students. For example, it would be valuable to investigate whether school staff played a significant role in affecting student outcomes, and in increasing school PMC and POMC and whether their efforts varied between high-SES and low-SES schools. Other studies could examine eligibility for matriculation certificate at the student level. Additionally, it would be interesting to follow the education system’s trajectory in the years after 2022, which may also be fraught with crises (e.g. the ‘Iron Swords’ war that disrupts learning in all schools).
Implications
This study highlights the importance of bringing research evidence into policy discussions on education and inequality in the context of the matriculation certificate. The study shows two aspects in this regard. The first aspect concerns the nature of matriculation exams. Greater flexibility in the exams can lead to higher PMC and reduce the social gap between schools. However, the outstanding matriculation certificate seems to preserve inequality and even increase it. In view of the significance of the matriculation certificate in Israel, looking at the school practices that enable schools to improve the PMC and POMC in different social contexts can be effective. The second aspect concerns the COVID-19 crisis, emphasizing how educational policy that allows schools to make decisions (e.g., choosing internal exams) influences educational opportunities and shapes social stratification. This highlights the need for greater school autonomy to better address individual school needs and preferences.
Declarations
Conflict of Interest
The authors have no competing interests to declare that are relevant to the content of this article.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The Ultra-Orthodox education was not included as only a few schools prepared their students for matriculation exams. These schools are also very distinct in their curriculum, teacher training, and school organization compared to other educational sectors.
Overall, across all school levels, only 1.5% of the Arab student body (6,664 out of more than 453,000) study in Jewish schools. This percentage is even lower in high school (Weininger & Worgan (2024). Frameworks Integrating Jewish and Arab Students in the Education System. Knesset – Research and Information Center).
PMC and POMC, which acknowledge percentage of students who passed the matriculation exams or achieved a defined criterion (such as five units of mathematics), are comparable over the years.
Tirosh (2022) mentions that this raises questions about the reliability of matriculation exams and increases concerns among university heads. It should also be noted that this increase did not significantly reduce the disparity between the periphery and the center. Moreover, while external matriculation exam grades were determined by a fixed formula and changes that occurred over time could have been examined, a formula for determining internal exam scores was not published. The MOE used a formula to check the grades. It clarified that if gaps were found between the formula grades and those reported by the school, the ministry would monitor them and weigh them accordingly. This formula was not published, nor was the number of cases in which grades dropped (Weissblay, 2020b).
The null model included only random components (the intercept and time). This enables the computation of the partitioning of the overall variance of the outcome variables into between-school and within-school components (repeated measures of PMC/POMC).
A typical school was defined as an academic high school (not a six-year secondary school) with average characteristics representative of its respective sector.
Compute as: ((10.613 − 7.118)/10.613). The explained variance of the null model minus the unexplained variance after including variables, divided by the unexplained variance of the null.
Addi-Raccah, A. (2022). School credentials: A matter of educational sector, school and urban locality’s socio-economic contexts. Sage, 54(1). 3–28. https://doi.org/10.1177/00131245211004548CrossRef
Addi-Raccah, A. (2023) School structure and matriculation eligibility Is it worth attending a six-year secondary school? In Y. Harpaz, & E. Horvitz (Eds.) Who will save middle school? (pp.404–429) The Branco Weiss Institution and Kinneret, Zmora, Dvir Publishing House, Ltd. (Hebrew).
Addi-Raccah, A., Grinshtain, Y., & Bahak, H. (2015). Inequality and Education Connections with Growth in Socio-Economic Inequality. Initiative for Applied Education Research Israel Academy of Sciences and Humanities.
Addi-Raccah, A. & Seeberger Tamir, N. (2022). Mothers as teachers to their children: Lessons learned during the COVID-19 pandemic. Journal of Family Studies, 29(3), 1379–1402. https://doi.org/10.1080/13229400.2022.2048964CrossRef
Agostinelli, F., Doepke, M., Sorrenti, G., & Zilibotti, F. (2022). When the great equalizer shuts down: Schools, peers, and parents in pandemic times. Journal of Public Economics,206(104574), 1–10. https://doi.org/10.1016/j.jpubeco.2021.104574CrossRef
Ainley, J., Macaskill, G., & Thomson, S. (2022). Within and between school variation in achievement on the Programme for International Student Assessment (PISA) in Australia: PISA Australia Technical Paper. Retrieved April 26, 2025, from https://research.acer.edu.au/cgi/viewcontent.cgi?article=1055&context=ozpisa
Andrew, A., Cattan, S., Costa-Dias, M., Farquharson, C., Kraftman, L., Krutikova, S., & Sevilla, A. (2020). Learning during the lockdown: Real-time data on children’s experiences during home learning. Institute for Fiscal Studies. https://doi.org/10.1920/BN.IFS.2020.BN0288CrossRefPubMed
Ayalon, H., & Shavit, Y. (2004). Educational reforms and inequalities in Israel: The MMI hypothesis revisited. Sociology of Education,77(2), 103–120. https://doi.org/10.1177/003804070407700201CrossRef
Beck, U. (1992). Risk society: towards a new modernity. Sage Publications.
Beck, U. (1999). World risk society. Polity Press.
Beck, U. (2013). Why ‘class’ is too soft a category to capture the explosiveness of social inequality at the beginning of the twenty-first century. The British Journal of Sociology,64(1), 63–74. https://doi.org/10.1111/1468-4446.12005CrossRefPubMed
Beck, U., Bonss, W., & Lau, C. (2003). The theory of reflexive modernization: Problematic, hypotheses and research programme. Theory, Culture & Society,20(2), 1–33. https://doi.org/10.1177/0263276403020002001CrossRef
Borg, C. & Mayo, P. (2022). The lost generation? Educational contingency in viral times: Malta and beyond. I. Accioly and D. Macedo (Eds.) Equality and Justice in the New Normal. Bloomsbury Academic, (pp. 99–111).
Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, R. S., Al-Freih, M., Pete, J., Olcott, D., Rodes, V., Aranciaga, I., Bali, M., Alvarez, V. A., Roberts, J., Pazurek, A., Raffaghelli, J. E., Panagiotou, N., De Coëtlogon, P., Shahadu, S., Brown, M., Asino, T. I., Tumwesige, J., Ramirez Reyes, T., Barrios Ipenza, E., Ossiannilsson, E., Bond, M., Belhamel, K., Irvine, V., Sharma, R. C., Adam, T., Janssen, B., Sklyarova, T., Olcott, N., Ambrosino, A., Lazou, C., Mocquet, B., Mano, M., & Paskevicius, M. (2020). A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1–126.
Clark, A. E., Flèche, S., Layard, R., Powdthavee, N., & Ward, G. (2018). The origins of happiness: The science of well-being over the life course. Princeton University Press.
Curran, D. (2016). Risk, power, and inequality in the 21st century. Palgrave Macmillan.CrossRef
Dadon-Golan, Z., Ben-David Hadar, I., & Klein, J. (2019). Equity in education: The Israeli case. International Journal of Educational Management,33(7), 1670–1685. https://doi.org/10.1108/IJEM-09-2018-0291CrossRef
Downey, D. B., Von Hippel, P. T., & Broh, B. A. (2004). Are schools the great equalizer? Cognitive inequality during the summer months and the school year. American Sociological Review,69(5), 613–635. https://doi.org/10.1177/000312240406900501CrossRef
Engzell, P., Frey, A., & Verhagen, M. D. (2021). Learning loss due to school closures during the COVID-19 pandemic. Proceedings of the National Academy of Sciences—PNAS, 118(17), 1–7. https://doi.org/10.1073/pnas.2022376118
Friedlander, D., Okun, B. S., & Goldscheider, C. (2016). Ethno-religious hierarchy in educational achievement and socioeconomic status in Israel: A historical perspective. In N. Khattab, S. Miaari, & H. Stier (Eds.), Socioeconomic inequality in Israel (pp. 97–121). Springer.
Frohn, J. (2021). Troubled schools in troubled times: How COVID-19 affects educational inequalities and what measures can be taken. European Educational Research Journal EERJ,20(5), 667–683. https://doi.org/10.1177/14749041211020974CrossRef
Gore, J., Fray, L., Miller, A., Harris, J., & Taggart, W. (2021). The impact of COVID-19 on student learning in New South Wales primary schools: An empirical study. The Australian Educational Researcher,48(4), 605–637. https://doi.org/10.1007/s13384-021-00436-wCrossRefPubMed
Goudeau, S., Sanrey, C., Stanczak, A., Manstead, A., & Darnon, C. (2021). Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nature Human Behaviour,5(10), 1273–1281. https://doi.org/10.1038/s41562-021-01212-7CrossRefPubMed
Haelermans, C., Korthals, R., Jacobs, M., de Leeuw, S., Vermeulen, S., van Vugt, L., Aarts, B., Prokic-Breuer, T., van der Velden, R., van Wetten, S., & de Wolf, I. (2022). Sharp increase in inequality in education in times of the COVID-19-pandemic. PLoS ONE,17(2), 1–37. https://doi.org/10.1371/journal.pone.0261114CrossRef
Kaplan, A., & Yahia, Y. (2017). High school students’ academic causal attributions in the cultural-political context of the Arab school system in Israel. Intercultural Education,28(1), 60–74. https://doi.org/10.1080/14675986.2016.1271558CrossRef
Kuhfeld, M., Soland, J., & Lewis, K. (2022). Test score patterns across three COVID-19-impacted school years. Educational Researcher,51(7), 500–506. https://doi.org/10.3102/0013189X221109178CrossRef
Liao, H., Ma, S., & Xue, H. (2022). Does school shutdown increase inequality in academic performance? Evidence from COVID-19 pandemic in China. China Economic Review,75(101847), 1–19. https://doi.org/10.1016/j.chieco.2022.101847CrossRef
Livne, I. (2017). Tracking within a track: De facto paving in the theoretical track in secondary education in Israel. In: R. Arviv-Elyashiv, Y. Feniger, & Y. Shavit (Eds.), A chance for a change? Theory and research on equality of educational opportunities (pp. 83–109). MOFET Institute. (Hebrew).
Lucas, S. R. (2001). Effectively maintained inequality: Education transitions, track mobility, and social background effects. American Journal of Sociology,106(6), 1642–1690. https://doi.org/10.1086/321300CrossRef
Lucas, S. R. (2009). Stratification theory, socioeconomic background, and educational attainment: A formal analysis. Rationality and Society,21(4), 459–511. https://doi.org/10.1177/1043463109348987CrossRef
Ludewig, U., Kleinkorres, R., Schaufelberger, R., Schlitter, T., Lorenz, R., König, C., Frey, A., & McElvany, N. (2022). COVID-19 pandemic and student reading achievement: Findings from a school panel study. Frontiers in Psychology,13(876485), 1–15. https://doi.org/10.3389/fpsyg.2022.876485CrossRef
Pietrocola, M., Rodrigues, E., Bercot, F., & Schnorr, S. (2020). Risk society and science education: Lessons from the Covid-19 Pandemic. Science & Education,30(2), 209–233. https://doi.org/10.1007/s11191-020-00176-wCrossRef
Pinson, H., Abu-Rabia, S., Ben-David Hadar, I., Dadon, Z., Kafri, Y., & Foris, M. (2020). Thinking group: gaps and inequality in distance learning. The Chief Scientist of the Ministry of Education. (Hebrew). Retrieved April 26, 2025, from https://meyda.education.gov.il/files/LishcatMadaan/gapsandinequality2020.pdf
The Crisis Experts' Teams: Education Team. (2020). Position paper: Covid-19 crisis and its influence on the Israeli education system. The crisis experts' teams: education team. (Hebrew).
Savaş Yalçın, M., & Koşar, D. (2021). Bibliometric analysis of research on (in)equality of opportunities in education. Cumhuriyet International Journal of Education, 10(3). 1194–1213. https://doi.org/10.30703/cije.822317
Schejter, A., & Tirosh, N. (2016). The digital divide in Israel. In A. Schejter & N. Tirosh (Eds.), A justice-based approach for new media policy (pp. 63–69). Springer International Publishing.CrossRef
Schult, J., Mahler, N., Fauth, B., & Lindner, M. A. (2022). Long-term consequences of repeated school closures during the COVID-19 pandemic for reading and mathematics competencies. Frontiers in Education, 13(Lausanne), 1–13. https://doi.org/10.3389/feduc.2022.867316
Schult, J., Mahler, N., Fauth, B., & Lindner, M. A. (2021). Did students learn less during the COVID-19 pandemic? Reading and mathematics competencies before and after the first pandemic wave. School Effectiveness and School Improvement,33(4), 544–563. https://doi.org/10.1080/09243453.2022.2061014CrossRef
Schuurman, T. M., Henrichs, L. F., Schuurman, N. K., Polderdijk, S., & Hornstra, L. (2023). Learning loss in vulnerable student populations after the first Covid-19 school closure in the Netherlands. Scandinavian Journal of Educational Research,67(2), 309–326. https://doi.org/10.1080/00313831.2021.2006307CrossRef
Shabbat, R. (2021). The teachers arrived at the students’ houses to prepare them for the Matriculation. Etrog: A Journal on education, Judaism and Social Issues,88, 6–9. (Hebrew).
Shavit, T., Lahav, E., & Shahrabani, S. (2014). What affects the decision to take an active part in social justice protests? The impacts of confidence in society, time preference and interest in politics. Journal of Behavioral and Experimental Economics,52, 52–63. https://doi.org/10.1016/j.socec.2014.06.004CrossRef
Sullivan, A., spsampsps Whitty, G. (2007). Social Inequalities and Education Policy in England. In: Teese, R., Lamb, S., Duru-Bellat, M., Helme, S. (eds) International Studies in Educational Inequality, Theory and Policy. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5916-2_3
Suwaed, M., & Ali, N. (2016). Education, identity, and ideology: The Islamic movement and Moslem religious education in Israel. Social Identities,22(4), 426–449. https://doi.org/10.1080/13504630.2015.1128811CrossRef
The Circular of the Director General of the Ministry of Education. (2000). 61(2). (Hebrew).
Vaisbaum-Gani, Y. (2022). Sociological implications of the “High Five” national plan, to promote mathematics, on equal opportunities in Education. [Thesis Submitted for the MA Degree of Humanities]. Tel-Aviv University (Hebrew).
Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., Cockburn, J., McDonald, H. I., MacKenna, B., Tomlinson, L., Douglas, I. J., Rentsch, C. T., Mathur, R., Wong, A. Y. S., Grieve, R., … Goldacre, B. (2020). Factors associated with COVID-19-related death using OpenSAFELY. Nature (London), 584(7821), 430–436. https://doi.org/10.1038/s41586-020-2521-4
Zeedan, R., & Hogan, R. E. (2022). The correlation between budgets and matriculation exams: The case of Jewish and Arab schools in Israel. Education Sciences,12(8), 1–24. https://doi.org/10.3390/educsci12080545CrossRef
Zierer, K. (2021). Effects of pandemic-related school closures on pupils’ performance and learning in selected countries. Education Sciences,11(6), 1–12. https://doi.org/10.3390/educsci11060252CrossRef
