On February 6, 2023, at 04:17 a.m., an earthquake with a magnitude of 7.7 occurred in Turkiye’s province of Kahramanmaras, with its epicenter in the Pazarcik district. On the same day, at 13:24, another earthquake with a magnitude of 7.6 struck, with its epicenter in the Elbistan district of Kahramanmaras. These earthquakes, which affected 11 provinces, impacted many social institutions, including education.

It can be anticipated that the impacts of disasters on education affect individuals at different levels and in different ways. This situation brings to the fore the issue of “good education for all after disasters,” or in other words, “post-disaster inclusive education,” even though individuals are affected by disasters to varying degrees. Inclusivity refers to ensuring everyone’s access to and full participation in educational environments and subsequently engaging them in an appropriate and high-quality educational process that addresses their needs (Butakor et al., 2020; Shaeffer, 2019; Stubbs, 2008). This concept is also related to how communities respond to change and diversity (Armstrong, 2008). Considering that disasters alter educational environments and, in particular, students’ needs, post-disaster inclusive education becomes even more important. Studies in this area indicate that, in the context of inclusivity in post-disaster educational processes, girls in terms of gender, refugees/those under temporary protection in terms of citizenship status, and individuals living in poverty in terms of socioeconomic status become disadvantaged groups (Basnet, 2020; Hermida, 2011; Lai et al., 2019; Nguyen & Minh Pham, 2018; Yavuz & Mizrak, 2016). As these groups may become highly marginalized during such processes, there is a need for more research and data to better understand how their post-disaster resilience can be enhanced most effectively (Hoffmann & Blecha, 2020). Moreover, when factors such as limitations regarding girls’ education (Çaliskan, 2007), constraints on refugees’ access to logistical resources (Center for Middle Eastern Strategic Studies, 2014), and higher poverty rates in rural areas compared to urban areas (Yasar & Tasar, 2019) are taken into account, it can be argued that examining post-earthquake inclusive education processes in rural areas is particularly necessary.

In the present study, we aimed to identify the variables that predict post-earthquake inclusive education in rural areas, considering earthquakes as a type of disaster. In this context, we consider schools’ post-earthquake disaster management skills to be one of the positive predictors of post-disaster inclusive education in rural areas, as these skills can facilitate access to education in emergencies (Sinclair, 2002). Another positive predictor is expected to be the Socio-Economic Development Index (SEDI) of the districts to which rural areas are administratively affiliated, because inclusivity requires engagement with broader social and economic conditions of society (Barton, 1997). Given that knowing students is crucial for inclusive education (Çelik, 2022) and that inclusive education requires a long-term process (Ainscow, 2005), we also anticipate that the continuity of administrative staff and teachers before and after the earthquake will emerge as positive predictors in this study. We hypothesize that the level of disruption to classes after the earthquake will negatively predict post-disaster inclusive education in rural areas, because students’ presence at school is highly important for their inclusion in many respects, particularly academic achievement (Halinen & Järvinen, 2008). Finally, we assume that distance from the nearest earthquake epicenter may positively explain inclusive education in rural areas, as it can influence the level of destruction caused by the earthquake. In other words, we expect that inclusive education efforts will increase as the distance from the nearest earthquake epicenter increases, because less destruction is likely to occur farther from the epicenter, thereby enabling inclusive education practices to be implemented more easily.

In this study, we employed a correlational survey design. Rural schools located in the provinces of Adana, Adiyaman, Diyarbakir, Elazig, Gaziantep, Hatay, Kahramanmaras, Kilis, Malatya, Osmaniye, and Şanliurfa that were affected by the February 6 earthquakes constituted the population of the study. We used cluster sampling to determine the schools to be included in the research. Accordingly, we included a total of 965 schools from these regions in the study. To collect the data, we developed the Post-Earthquake Inclusive Education Scale (PEIES) and the Post-Earthquake Disaster Management Scale (PEDMS). According to the results of the Exploratory Factor Analysis, both the PEIES and the PEDMS are single-factor scales, explaining 83% and 81% of the total variance, respectively. The results of the Confirmatory Factor Analysis showed that the goodness-of-fit indices were at acceptable and excellent levels. We calculated Cronbach’s alpha reliability coefficients as .97 for both scales. All these findings indicate that the measurements obtained using these scales are valid and reliable (Buyukozturk, 2012). In addition to the PEIES and the PEDMS, we prepared a School Information Form (SIF). SIF contained questions regarding administrative staff continuity, teacher continuity, and the level of disruption to lessons. We obtained the SEDIs from data provided by the Republic of Turkiye Ministry of Industry and Technology and determined the distances of the villages to the nearest earthquake epicenter using the OpenRouteService Application Programming Interface. We analyzed the data using multiple linear regression analysis. Within the scope of the assumptions of this analysis (Pallant, 2007), we achieved an adequate sample size. We examined outliers in each variable using boxplots and removed cases containing these values from the dataset. Because the correlations among the predictor variables were at most .257 (< .70), multicollinearity did not pose a problem. Since the skewness and kurtosis coefficients of all variables ranged between −1.5 and +1.5, the data exhibited a normal distribution. In addition, the scatter plot showed values remaining between −3 and +3 and forming an approximate rectangular shape, indicating that the assumption of multivariate normality in terms of residuals was met. The normal P–P plot formed a diagonal line, indicating that the assumption of linearity was satisfied. This study was supported by The Scientific and Technological Research Council of Turkiye (TUBITAK) under the 1001 – Scientific and Technological Research Projects Support Program, within the scope of the “Special Call for Universities in the Earthquake Region – BINBIRCABA.”

According to the results obtained from the study, post-earthquake disaster management, the Socio-Economic Development Index (SEDI), continuity of administrative staff, continuity of teachers, the level of disruption to classes, and distance from the nearest earthquake epicenter collectively explained 86.8% of the variance in post-earthquake inclusive education. The resulting model was statistically significant. However, the SEDI and the level of disruption to classes did not significantly predict post-earthquake inclusive education. Post-earthquake disaster management, continuity of administrative staff, and continuity of teachers emerged as positive and significant predictors. In contrast, distance from the nearest earthquake epicenter was a negative and significant predictor. Within this model, post-earthquake disaster management was the strongest predictor among all variables (β = .93). We interpret these findings to suggest that schools capable of demonstrating post-earthquake disaster management skills, even under highly adverse conditions, are able to create a foundation for an inclusive educational environment for their students in the post-earthquake period.

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