Geographic Disparities and Subject-Level Divides: A Spatio-Temporal Analysis of Educational Outcomes in Somaliland

Geographic Disparities and Subject-Level Divides: A Spatio-Temporal Analysis of Educational Outcomes in Somaliland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Geographic Disparities and Subject-Level Divides: A Spatio-Temporal Analysis of Educational Outcomes in Somaliland Mukhtaar Axmed Cumar, Mustafe Khadar Abdi, Mukhtar Abdi Omar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7069860/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract This study provides a comprehensive spatio-temporal analysis of educational outcomes in Somaliland, examining geographic and subject-level disparities in student performance. The primary objective was to identify and quantify patterns of inequality in national examination results to inform evidence-based educational policy. The study employed a quantitative approach, analyzing an anonymized, individual-level dataset of Somaliland National Certificate Examination results from 2020 to 2023. The methodology involved descriptive geospatial analysis to visualize performance trends and an inferential logistic regression model to test the significance of regional and subject-level divides, including a crucial interaction term between the two. The findings reveal significant heterogeneity in academic performance across Somaliland’s administrative regions, with eastern regions consistently outperforming western regions. A clear hierarchy of subject difficulty was identified, with STEM subjects (Chemistry, Physics, Math) showing significantly lower pass rates than language and religious studies. Most critically, the analysis uncovered significant interaction effects, indicating that the magnitude of subject difficulty varies substantially by region. This suggests that national-level educational challenges are geographically concentrated and that uniform policy interventions are likely to be ineffective. The study’s originality lies in its granular, spatio-temporal examination of subject-specific educational disparities in a post-conflict, data-scarce context, providing a robust evidence base for targeted policy interventions aimed at fostering educational equity. Scientific community and society/Geography Social science/Geography Physical sciences/Mathematics and computing Educational Inequality Geospatial Analysis Spatio-Temporal Somaliland Student Performance Subject-Level Disparities Educational Policy Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The pursuit of educational equity is a central pillar of global development agendas, recognized as fundamental to fostering social cohesion, economic growth, and individual well-being (Hallinger and Kulophas 2019). However, achieving this goal is complicated by deep-seated inequalities that manifest across various social and geographical dimensions. Recent advancements in spatial methods and geospatial analysis have provided a powerful new lens for examining educational equity, access, and opportunity, moving beyond traditional metrics to reveal the nuanced ways in which "place matters" (Butler and Sinclair 2020 ; Cobb 2020 ; Kelemen, Matthews, and Breevaart 2020). By mapping educational outcomes, researchers can identify "hotspots" of underperformance and uncover the complex interplay between geography and educational disadvantage (Metwally et al. 2021 ). Understanding the spatial distribution of educational outcomes requires more than static mapping; it demands a consideration of historical and temporal contexts that shape contemporary patterns of inequality (Kelly, 2019). Longitudinal research demonstrates that the duration and timing of exposure to adverse or advantageous circumstances throughout childhood have profound effects on learning-related outcomes (Grant, Meissel, and Exeter 2023a). These spatial and temporal dynamics are particularly pronounced in low- and middle-income countries, where disparities in educational attainment are often stark (Sri 2019 ). In these contexts, geography often intersects with other forms of marginalization, creating complex layers of disadvantage that national-level policies may fail to address. The challenge of educational inequality is especially acute in sub-Saharan Africa, where spatial factors significantly influence communities' educational performance, even after accounting for other contextual variables (Frola, Delprato, and Chudgar 2024 a; Haefner and Sternberg 2020). Research across the region reveals that spatial inequality in educational access is often more pronounced in marginalized communities and in countries with lower levels of women's empowerment (Delprato, Chudgar, and Frola 2024; Frola et al. 2024a; Frola, Delprato, and Chudgar 2024 b). These findings underscore the inadequacy of using broad geographical categories like "rural-urban" and highlight the need for more granular, place-sensitive analyses to inform effective, equitable policy interventions (Somaliland NDPIII 2023 ; Takyi et al. 2019 ). In Somaliland, a nation that has made significant strides in establishing stability and governance since 1991, the education sector remains a critical area for development (ESA 2021 ; MOE&S ESSP 2022). The government has committed to expanding access and improving quality, as outlined in its Education Sector Strategic Plan (ESSP) 2022–2026. This plan acknowledges the need to address disparities affecting nomadic populations, girls, and children in rural areas (Ministry of Education & Science, 2022). Despite these commitments, significant challenges persist, including inadequate infrastructure, teacher shortages, and regional imbalances in resource allocation (Amor et al. 2018 ; MOE&S JRES 2023). The problem this study addresses is the critical lack of granular, evidence-based understanding of how educational disparities manifest both geographically and across different academic subjects in Somaliland. While national reports acknowledge regional challenges (ESA 2021 ; Hassan et al. 2024 ; Kleibert et al. 2020 ; Melesse and Obsiye 2022 ), they often lack the spatio-temporal specificity required for targeted interventions. For instance, field visit reports highlight that schools in remote regions like Badhan face severe resource constraints and that teacher quality varies significantly between urban and rural areas (Ali et al. 2025 ; Holloway and Kirby 2019; MOE&S JRES 2023; Somaliland NDPIII 2023 ). These anecdotal and broad-stroke observations point to a deeper, systemic issue of spatial inequality that has not been systematically quantified. This lack of granular analysis represents a significant gap in the literature and a barrier to effective policymaking. While broad studies map educational attainment across the Global South (Collet-Sabé 2019 ; Sri 2019 ) and others examine spatial inequality in Africa (Delprato et al., 2024), there is a dearth of research that combines geographic, temporal, and subject-level data within a single national context like Somaliland. Without this integrated perspective, it is difficult to determine whether poor performance in a specific region is a universal issue or if it is driven by challenges in particular subjects, such as STEM fields, which often require more specialized resources and teaching expertise. This study is therefore of critical importance. By providing a detailed spatio-temporal analysis of national examination results, it moves beyond anecdotal evidence to offer a robust, quantitative assessment of educational disparities in Somaliland. It provides policymakers at the Ministry of Education and Science with a precise diagnostic tool, enabling them to identify not just which regions are underperforming, but in which subjects they are struggling the most. This evidence is crucial for designing targeted, cost-effective interventions—from curriculum support and teacher training to resource allocation—that address the specific needs of different regions, thereby advancing the national goal of equitable, quality education for all. The primary objective of this study is to analyze the spatio-temporal patterns of academic performance in the Somaliland National Certificate Examination from 2020 to 2023. To achieve this, the study pursues three specific aims: (1) to describe and visualize the geographic and subject-level trends in student pass rates across Somaliland’s administrative regions; (2) to statistically model the predictors of student success, including year, gender, region, and subject; and (3) to test the central hypothesis that the effect of academic subjects on student performance varies significantly across different geographic regions, thereby identifying specific region-subject challenges that require targeted policy attention. LITERATURE REVIEW The study of educational inequality has increasingly embraced spatial and geographical perspectives to understand the complex mechanisms that produce and reproduce disparities (Beech, Larsen, and Wei 2024; Marin-Velasquez and Closson-Pitts 2019). This review synthesizes key literature across three domains. First, it outlines the conceptual and theoretical frameworks that underpin the analysis of education through a spatial lens, focusing on concepts of spatial justice and the geographies of education. Second, it reviews empirical and methodological studies that have employed geospatial and spatio-temporal analyses to investigate educational disparities in various global contexts. Finally, it synthesizes these insights to identify the critical gap this study addresses: the need for a granular, subject-specific, and spatio-temporal analysis of educational outcomes in a post-conflict setting like Somaliland. The critical geographies of education offer a powerful lens for examining how power, inequality, and social structures are inscribed in educational landscapes (Byun, Lee, and Hwang 2021a; Henry 2020 ). This perspective challenges the notion of schooling as an automatic social good, instead interrogating how educational systems can perpetuate existing injustices (Holloway and Kirby 2019; Di Martino, Gregorio, and Iannone 2022; Oppido, Ragozino, and Esposito De Vita 2023). A central concept within this field is spatial injustice, which occurs when the distribution of resources, opportunities, and institutions—such as schools—disproportionately harms marginalized communities (Tieken and Auldridge-Reveles 2019). School closures, for example, are not random events but often reflect and reinforce existing patterns of racial and economic segregation, limiting access to quality education for the most vulnerable populations (Boterman et al. 2019 ; Hirsh et al. 2020 ). The concept of "place" is fundamental to this analysis, moving beyond a simple container for social processes to an active force that shapes educational experiences and outcomes (Butler and Sinclair 2020 ). Place-based inquiry illuminates how local contexts—including community resources, historical legacies, and social identities—interact to create unique barriers and opportunities for students (Easterbrook and Hadden 2020; Hinduja et al. 2023 ). This perspective aligns with a feminist geopolitical approach, which calls for a multi-scalar analysis that accounts for how broad geopolitical processes are differentially experienced, negotiated, and contested in the localized spaces of schools and communities (Anne 2019 ; Nguyen 2020 ). Such a framework reveals the intricate ways in which global policies and national agendas are translated and transformed at the local level. Theorizing the relationship between educational systems and inequality requires understanding how macro-level characteristics shape micro-level outcomes. Systemic features such as standardization and stratification have been shown to have differential effects on gender, ethnic, and socioeconomic inequalities (Henry 2020 ; Zapfe and Gross 2021). However, much of the research lacks a robust theoretical explanation for these links, highlighting a critical gap (Hirsh et al. 2020 ). By integrating spatial theories, researchers can better analyze how these systemic characteristics are geographically expressed, creating a more nuanced understanding of how educational systems reproduce or challenge social stratification across space (Brekke 2020 ; Perry, Rowe, and Lubienski 2021). A growing body of empirical work leverages geospatial methods to map and analyze educational disparities. Systematic reviews demonstrate that GIS is a valuable tool for providing new insights into educational equity, access, and opportunity, particularly in areas like school choice and resource distribution (Cobb 2020 ; Kocdar, Bozkurt, and Goru Dogan 2020a). In Pakistan, for instance, district-level modeling has revealed significant spatial inequalities in education status, with poverty and infrastructure emerging as key determinants (Sajjad et al., 20(Moscovitz and Sabzalieva 2023)22). Similarly, research across sub-Saharan Africa has used geo-localized survey data to show that spatial dependence is a crucial, yet often ignored, factor in educational attainment, with inequalities being more pronounced in marginalized communities (Frola et al. 2024a; Yoon 2019 ). The temporal dimension is equally critical. Longitudinal studies consistently show that the duration and timing of exposure to socioeconomic conditions are strongly linked to educational and cognitive outcomes, underscoring the need for a life-course perspective (Grant, Meissel, and Exeter 2023b; Puttick 2023 ). The COVID-19 pandemic starkly illustrated this, as control measures and the shift to remote learning exacerbated pre-existing educational inequalities, disproportionately affecting students from disadvantaged backgrounds (Darmody et al.,(Bellibaş and Gümüş 2019) 2021; Reuge et al., 2021 ). The resulting "digital divide" is not merely about access to technology but encompasses a complex interplay of socioeconomic status, geography, and parental knowledge, creating new layers of disparity (Lythreatis et al., 2022; Martin et al., 2024). Methodologically, the field is advancing beyond simple mapping to employ more sophisticated spatio-temporal analyses. While such methods are not yet common in education research, their application in public health offers a promising model for how to track the geographic and temporal distribution of outcomes and identify high-risk areas for intervention (Byun, Lee, and Hwang 2021b; Miseliunaite, Kliziene, and Cibulskas 2022). The use of systematic reviews and bibliometric analysis has also become crucial for mapping the landscape of knowledge in related fields, such as education for sustainable development (Hallinger and Kulophas 2019; Tieken and Auldridge-Reveles 2019) and educational technology (Bozkurt 2020 ; Kocdar, Bozkurt, and Goru Dogan 2020b), identifying both dominant trends and critical research gaps. These reviews often highlight a significant imbalance, with a dearth of research originating from the Global South (Tlili et al. 2022 ; Valverde-Berrocoso et al. 2020). The literature overwhelmingly confirms that geography is not a neutral backdrop for education but an active force that shapes opportunity and outcomes. From the macro-level of global geopolitics (Chen and Yang 2024; Moscovitz and Sabzalieva 2023) to the micro-level of classroom interactions ((Ahmed, Callaghan, and Arslan 2023 ; Çop, Olorunsola, and Alola 2020; Maheshwari, Kha, and Arokiasamy 2022), space and place are central to understanding educational processes. However, despite the growing sophistication of spatial methods, a critical gap remains in applying these tools to understand subject-specific disparities within a single, data-scarce national context. While large-scale studies can map broad patterns of attainment (Rashid 2019 ; Sri 2019 ), they often lack the granularity to inform targeted, subject-level interventions. Furthermore, much of the research on educational inequality in fragile or post-conflict states focuses on broad access issues or specific themes like entrepreneurship education (Boterman et al. 2019 ; Koh and Sin 2020; Rashid 2019 ) or mental health (Alabadla et al. 2022 ; Kebede et al. 2023 ), rather than on the core academic performance of the national school system. The existing body of work on Somaliland's education sector, while valuable, consists primarily of strategic plans, sector analyses, and field reports that identify challenges in broad strokes (e.g., ESA 2021 ; MOE&S JRES 2023). These documents lack the rigorous, quantitative spatio-temporal analysis needed to pinpoint specific areas of academic weakness. This study directly addresses this gap by providing the first known spatio-temporal analysis of subject-level examination results in Somaliland, offering a new layer of evidence to guide the nation's journey toward educational equity. METHODOLOGY 3. Data and Methods This study employs a quantitative approach to analyze the spatio-temporal patterns of academic performance in Somaliland. The methodology involved two primary stages: first, a descriptive and geospatial analysis to visualize performance trends, and second, an inferential statistical model to test the significance of geographic and subject-level disparities. 3.1. Data Source and Preparation The main dataset utilized in this research comprises anonymized, individual student-level outcomes from the Somaliland National Certificate Examination, sourced from the Somaliland National Examination and Certification Board for the years spanning 2020 to 2023. This extensive dataset encompassed several crucial variables for each exam entry: the exam year, the student's gender, the administrative region, the subject, and the final grade received. The dependent variable in our analysis was binary, indicating whether a student passed or failed an exam, coded as 1 for a "Pass" and 0 for any other result. The primary independent variables were categorized as follows: Year: A categorical variable with four categories (2020, 2021, 2022, 2023), with 2020 serving as the reference category in the statistical model. Sex: A binary categorical variable (Male/Female), with "Female" as the reference category. Region: A categorical variable with six categories representing Somaliland's administrative regions: Awdal, Maroodi Jeex, Sahil, Sanaag, Sool, and Togdheer. Maroodi Jeex, which includes the capital, was chosen as the reference category for comparison. Subject: A categorical variable with 10 categories, including Arabic, Biology, Chemistry, English, Geography, History, Islamic, Math, Physics, and Somali, was used as the reference category due to its consistently high pass rates across all regions. For the geospatial analysis, we employed a shapefile of Somaliland's six administrative regions. To prepare the data for visualization, individual student results were aggregated to determine the pass rate (calculated as the total number of passed exams divided by the total number of candidates) for each unique combination of region, year, and subject. 3.2. Analytical Strategy The analysis was conducted using R version 4.x.x with the dplyr, ggplot2, and sf packages for data manipulation and visualization, and the stats package for modeling. 3.2.1. Descriptive and Geospatial Analysis To explore trends and disparities, we first generated a series of line plots to visualize pass rate trajectories over the four-year study period. These visualizations were created from two perspectives: (1) faceted by region, to compare the performance of different subjects within each geographic area (Fig. 1), and (2) faceted by subject, to compare regional performance gaps for each academic discipline (Fig. 2). To analyze the geographic distribution of overall academic achievement, we created a series of choropleth maps using the sf package (Fig. 3). For these maps, the pass rates for all subjects within a given region were averaged for each year to produce a single, comparable measure of annual regional performance. 3.2.2. Inferential Statistical Modeling To move beyond descriptive analysis and statistically test the significance of the observed patterns, we employed a logistic regression model. This model is appropriate as the outcome variable—passing an exam—is binary. The model was designed to predict the probability of a student passing as a function of year, sex, region, and subject. Crucially, the model included a two-way interaction term between Region and Subject (Region * Subject). This interaction term tests the primary hypothesis of this study: that the effect of a particular subject on a student's likelihood of passing is not constant across the country but differs significantly depending on the student's region. The results of the regression are reported as coefficients ( B ) and odds ratios (OR), which represent the multiplicative change in the odds of passing for a given predictor, holding all other variables constant. The full model specification was: Pass/Fail ~ Year + Sex + Region * Subject Results This section presents the findings from the spatio-temporal analysis of academic performance on the Somaliland National Certificate Examination from 2020 to 2023. The results are organized to first describe the visual patterns of performance across regions and subjects, followed by a statistical model that quantifies the significance of these disparities. Descriptive Analysis of Performance Trends Visual analysis of pass rates reveals significant heterogeneity in academic performance, both within and between Somaliland's administrative regions. Figure 1 illustrates the performance trajectories for 10 core subjects within each of the six regions. A clear hierarchy of subject difficulty is evident across most regions, with language and religious studies (e.g., Somali, Islamic, Arabic) consistently showing higher pass rates than science and mathematics subjects (e.g., Chemistry, Physics, Math). Performance in the Awdal and Maroodi Jeex regions appears more volatile, with wider spreads in subject performance, whereas the eastern regions of Sool and Sanaag exhibit more compressed and consistently high pass rates, albeit with a slight decline over the period. To examine regional disparities more directly, Fig. 2 presents the performance of each region faceted by subject. These plots highlight a persistent and often substantial performance gap between regions, particularly in STEM subjects. For example, in Chemistry, Math, and Physics, the pass rates in Maroodi Jeex are consistently and significantly lower than in all other regions. This gap narrowed slightly over the four-year period but remained pronounced in 2023. In contrast, subjects such as Somali and Biology show much smaller, though still noticeable, differences in performance across regions. The overall spatial pattern of academic achievement is summarized in Fig. 3, which displays the average pass rate across all subjects for each region and year. The choropleth maps reveal a distinct spatial gradient, with the eastern regions (Sool, Sanaag) and coastal region (Sahil) consistently outperforming the western regions of Awdal and Maroodi Jeex. Over the four-year period, Maroodi Jeex consistently registered the lowest average pass rates, a pattern that intensified from 2021 to 2023. Statistical Modeling of Performance Predictors To statistically test the significance of these observed trends, a logistic regression model was estimated to predict the probability of a student passing a given exam. The model included year, student sex, region, and subject as main effects, along with an interaction term between region and subject to assess whether subject-specific performance gaps varied geographically. The results of the model are presented in Table 1 . Table 1 Logistic Regression Predicting the Probability of Passing an Examination Predictor B SE OR p Main Effects Intercept 1.38 0.02 3.98 < .001 Year (Ref: 2020) 2021 -0.44 0.01 0.64 < .001 2022 0.16 0.01 1.17 < .001 2023 0.32 0.01 1.38 < .001 Sex (Ref: Female) Male 0.00 0.01 1.00 .764 Region (Ref: Maroodi Jeex) Awdal -0.05 0.03 0.95 .136 Sahil 2.02 0.11 7.54 < .001 Sanaag 2.29 0.10 9.88 < .001 Sool 2.86 0.17 17.46 < .001 Togdheer 0.38 0.03 1.47 < .001 Subject (Ref: Islamic) Arabic 0.35 0.02 1.41 < .001 Biology 0.40 0.02 1.49 < .001 Chemistry -1.00 0.02 0.37 < .001 English 0.53 0.02 1.70 < .001 Geography 0.21 0.02 1.23 < .001 History 1.82 0.04 6.15 < .001 Math -1.30 0.02 0.27 < .001 Physics -0.80 0.02 0.45 < .001 Somali 1.79 0.04 5.97 < .001 Interaction Effects (Region x Subject) Awdal x Math 1.04 0.05 2.83 < .001 Sanaag x English -0.87 0.13 0.42 < .001 Sool x Geography -1.46 0.20 0.23 < .001 Togdheer x Biology 1.92 0.09 6.82 < .001 Note : B = logistic regression coefficient; SE = Standard Error; OR = Odds Ratio. Only a selection of significant interaction terms are presented for brevity. The model revealed significant effects for year, region, and subject. Compared to the reference year 2020, the odds of passing were significantly lower in 2021 but significantly higher in 2022 and 2023. No statistically significant difference in the odds of passing was found between male and female students ( B = 0.003, p = .764). The main effects for subjects confirmed the visual hierarchy observed in Fig. 1; compared to the reference subject of Islamic studies, students had significantly lower odds of passing Chemistry ( OR = 0.37), Math ( OR = 0.27), and Physics ( OR = 0.45), and significantly higher odds of passing History ( OR = 6.15) and Somali ( OR = 5.97). Most critically, the model identified highly significant interaction effects between region and subject, indicating that the magnitude of subject difficulty is not uniform across Somaliland. For instance, the significant negative interaction for Region_Names Sanaag: Subject English ( B = -0.87, p < .001) shows that the performance disadvantage in English (relative to Islamic studies) is significantly more severe for students in Sanaag than for their peers in the reference region of Maroodi Jeex. Conversely, the significant positive interaction for Region_ Names Togdheer: Subject Math ( B = 1.94, p < .001) indicates that the performance gap between Math and Islamic studies is substantially smaller in Togdheer compared to Maroodi Jeex. These findings provide robust statistical evidence that academic challenges are geographically concentrated and that interventions must consider the unique subject-specific needs of each region. DISCUSSION This study sought to provide a granular, spatio-temporal analysis of educational outcomes in Somaliland, and the findings reveal a complex landscape of inequality. The results confirm that while progress is being made, significant disparities persist along both geographic and subject-level lines. This discussion interprets these findings in relation to the existing literature, explores their theoretical implications, and synthesizes their contribution to understanding educational equity in post-conflict contexts. The descriptive and geospatial analyses clearly support the central premise of contemporary educational geography: place matters profoundly (Butler & Sinclair, 2020 ). The distinct spatial gradient, with eastern regions like Sool and Sanaag consistently outperforming the western capital region of Maroodi Jeex, challenges simplistic urban-rural dichotomies. This finding aligns with research from other developing contexts, such as Pakistan, which has uncovered significant subnational variation in educational status that does not always map neatly onto urban development (Sajjad et al., 2022). This geographic disparity suggests that factors beyond simple infrastructure, such as local governance, community engagement, or historical educational investment, may be at play (Takyi et al., 2019 ). The volatility in performance in the western regions, compared to the stability in the east, further suggests differing levels of systemic resilience and resource consistency. The stark hierarchy of subject difficulty, with STEM subjects consistently showing the lowest pass rates, is a critical finding with significant implications for Somaliland's development. This pattern is not uncommon, but its severity points to systemic weaknesses in resource-intensive educational areas (Brekke, 2020 ). The struggle in subjects like Chemistry, Math, and Physics likely reflects a shortage of qualified teachers, a lack of laboratory facilities, and inadequate teaching materials—challenges frequently noted in Somaliland's education sector reports (Ministry of Education & Science, 2021; IESCOL, February 2023, FIELD VISITS REPORT). This subject-level divide poses a direct threat to the nation's capacity to build the human capital required for a modern, diversified economy, a key goal of its national vision (Ministry of Education & Science, 2022). The most significant contribution of this study lies in the inferential findings from the logistic regression model, particularly the interaction effects between region and subject. The results move beyond simply stating that geography and subject matter, to demonstrating that how much they matter depends on their intersection. For example, the finding that the performance disadvantage in English is more severe in Sanaag than in Maroodi Jeex provides precise, actionable evidence for policymakers. It suggests that a generic, nationwide intervention to improve English instruction would be inefficient. Instead, resources should be targeted to regions where the challenge is most acute, a principle that aligns with calls for place-sensitive and context-specific policy (Zapfe & Gross, 2021). Conversely, the positive interaction showing that the performance gap between Math and Islamic studies is smaller in Togdheer than in Maroodi Jeex is equally insightful. This suggests the presence of localized strengths or effective practices in Togdheer's teaching of mathematics that could be studied and potentially scaled to other regions. This finding supports the idea that educational systems are not monolithic and that solutions to inequality can often be found by identifying and understanding positive deviance within the system itself (Henry, 2020 ). The lack of a significant gender disparity in overall pass rates is an encouraging finding, though it warrants further investigation at the subject level to ensure this equity holds across all disciplines, particularly in STEM fields where gender gaps are common globally. Theoretically, these findings reinforce the principles of spatial justice and the need for a multi-scalar analysis of education (Nguyen, 2020 ; Tieken & Auldridge-Reveles, 2019). The study demonstrates that educational inequality is not a flat, national phenomenon but a textured, geographically contingent reality. The interaction effects provide empirical weight to the argument that educational policies must be designed with a deep understanding of local contexts and the specific challenges faced by different communities (Beech et al., 2024). By providing a detailed map of these intersecting disparities, this study offers a new level of evidence that can help Somaliland move from broad strategic goals to targeted, effective, and equitable educational interventions. Policy Implications The findings of this study offer direct implications for educational policy in Somaliland. First, regional disparities necessitate a shift from a one-size-fits-all policy to targeted regional investment. Resources for teacher training, materials, and infrastructure should be disproportionately allocated to western regions, particularly Maroodi Jeex, which shows the lowest performance. Second, the subject-level divide, especially underperformance in STEM subjects, requires specific curriculum and pedagogical interventions. The Ministry of Education and Science should review the STEM curriculum, focusing on relevance, accessibility, and availability of teaching aids and laboratory equipment. A national strategy for recruiting and incentivizing qualified STEM teachers is essential to build capacity in these fields. Third, the interaction effects between region and subject provide precise guidance for policy. For subjects like English in Sanaag, where performance gaps are severe, targeted support programs should be developed, including professional development for teachers and supplementary learning materials. For subjects like Math in Togdheer, where performance is strong, the Ministry should study successful local practices that can be adapted to other regions. Conclusion This study provides a rigorous, spatio-temporal analysis of educational outcomes in Somaliland, revealing a complex landscape of inequality defined by both geography and academic subject. By analyzing four years of national examination data, the research moves beyond broad generalizations to offer specific, evidence-based insights into the nature of educational disparities. The findings confirm that a student's likelihood of success is significantly shaped not only by the subject they study but also by the region in which they are educated. The persistent underperformance of western regions and the nationwide struggle in STEM subjects represent major obstacles to Somaliland's development goals. The study's most critical contribution is the identification of significant interaction effects, demonstrating that educational challenges are not uniform but are geographically concentrated and subject-specific. This evidence fundamentally challenges the efficacy of uniform national policies and underscores the need for a more nuanced, data-driven, and targeted approach. By providing a detailed map of these intersecting disparities, this research equips policymakers with the evidence needed to allocate resources more effectively, design context-sensitive interventions, and ultimately foster a more equitable and effective education system for all of Somaliland's children. Study Limitations This study has several limitations that should be acknowledged. First, the analysis relies on examination data, which measures a specific form of academic performance but does not capture other crucial aspects of educational quality, such as student well-being or the development of non-cognitive skills. Second, the dataset, while comprehensive in its coverage of exam takers, lacks granular school-level or teacher-level variables. Consequently, the study can identify where disparities exist but cannot definitively explain the causal mechanisms, such as differences in school resources, teacher qualifications, or pedagogical practices. Third, the analysis is limited to the administrative regions as the unit of spatial analysis and does not capture potential intra-regional disparities, such as those between urban and rural areas within a single region. Finally, while the study identifies trends over a four-year period, this timeframe may not be sufficient to capture the full impact of longer-term policy changes or educational reforms. Recommendations In light of the findings, the following suggestions are presented to the Somaliland Ministry of Education and Science: Implement Region-Specific Funding: Revise the budget to allocate resources more effectively, with a focus on the western regions, particularly Maroodi Jeex, to enhance school infrastructure and support for teachers. Initiate a National STEM Program: Formulate a plan to boost performance in Math, Physics, and Chemistry by reviewing the curriculum, providing standardized laboratory kits, and offering specialized training for teachers. Create Regional Centers: Designate successful regions and schools in difficult subjects (such as Math in Togdheer) as hubs for peer learning and the sharing of best practices. Improve Data Analysis: Strengthen the capabilities of Regional Education Offices to evaluate student performance data, enabling more responsive local planning and interventions. Recommendation for Future Research The findings open several important avenues for future research. First, qualitative case studies are needed to understand causes of the observed disparities. Research should examine high- and low-performing regions to explore school-level, teacher-level, and community factors contributing to divergent outcomes. Second, future research should link student performance data with teacher qualifications, deployment, and school resources to enable robust causal analysis of inequality drivers. Finally, a longitudinal study tracking students through their educational journey would provide insights into educational inequality dynamics and critical intervention points. Declarations Ethics Statement This article does not contain any studies with human participants performed by any of the authors. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution M.A.C. and J.A.A. conceptualized the study, curated the data, and wrote the manuscript text. M.A.C. and A.H.M. conducted the formal analysis and managed the software, with support from M.K.A. J.A.A. performed the validation. M.A.O. provided editing and proofreading. All authors reviewed and approved the final manuscript. Data Availability The individual-level student examination data analysed during the current study were obtained from the Somaliland National Examination and Certification Board. Due to the sensitive and confidential nature of these educational records, restrictions apply to the availability of these data, and they are not publicly available. Access to the data may be considered upon reasonable request to the Somaliland National Examination and Certification Board, subject to their data sharing policies and approval. References Ahmed, Fawad, Deborah Callaghan, and Ahmad Arslan. 2023. “A Multilevel Conceptual Framework on Green Practices: Transforming Pol into Actionable Leadership and Employee Behavior.” Scandinavian Journal of Psychology 65(3):381–93. doi:10.1111/sjop.12981. Alabadla, Mustafa, Fatimah Sidi, Iskandar Ishak, Hamidah Ibrahim, Lilly Suriani Affendey, Zafienas Che Ani, Marzanah A. 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06:21:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168166,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7069860/v1/5a918286-7524-4d80-a9d9-e6eec7533a93.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geographic Disparities and Subject-Level Divides: A Spatio-Temporal Analysis of Educational Outcomes in Somaliland","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe pursuit of educational equity is a central pillar of global development agendas, recognized as fundamental to fostering social cohesion, economic growth, and individual well-being (Hallinger and Kulophas 2019). However, achieving this goal is complicated by deep-seated inequalities that manifest across various social and geographical dimensions. Recent advancements in spatial methods and geospatial analysis have provided a powerful new lens for examining educational equity, access, and opportunity, moving beyond traditional metrics to reveal the nuanced ways in which \"place matters\" (Butler and Sinclair \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cobb \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kelemen, Matthews, and Breevaart 2020). By mapping educational outcomes, researchers can identify \"hotspots\" of underperformance and uncover the complex interplay between geography and educational disadvantage (Metwally et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding the spatial distribution of educational outcomes requires more than static mapping; it demands a consideration of historical and temporal contexts that shape contemporary patterns of inequality (Kelly, 2019). Longitudinal research demonstrates that the duration and timing of exposure to adverse or advantageous circumstances throughout childhood have profound effects on learning-related outcomes (Grant, Meissel, and Exeter 2023a). These spatial and temporal dynamics are particularly pronounced in low- and middle-income countries, where disparities in educational attainment are often stark (Sri \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In these contexts, geography often intersects with other forms of marginalization, creating complex layers of disadvantage that national-level policies may fail to address.\u003c/p\u003e\u003cp\u003eThe challenge of educational inequality is especially acute in sub-Saharan Africa, where spatial factors significantly influence communities' educational performance, even after accounting for other contextual variables (Frola, Delprato, and Chudgar \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea; Haefner and Sternberg 2020). Research across the region reveals that spatial inequality in educational access is often more pronounced in marginalized communities and in countries with lower levels of women's empowerment (Delprato, Chudgar, and Frola 2024; Frola et al. 2024a; Frola, Delprato, and Chudgar \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb). These findings underscore the inadequacy of using broad geographical categories like \"rural-urban\" and highlight the need for more granular, place-sensitive analyses to inform effective, equitable policy interventions (Somaliland NDPIII \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Takyi et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Somaliland, a nation that has made significant strides in establishing stability and governance since 1991, the education sector remains a critical area for development (ESA \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; MOE\u0026amp;S ESSP 2022). The government has committed to expanding access and improving quality, as outlined in its Education Sector Strategic Plan (ESSP) 2022\u0026ndash;2026. This plan acknowledges the need to address disparities affecting nomadic populations, girls, and children in rural areas (Ministry of Education \u0026amp; Science, 2022). Despite these commitments, significant challenges persist, including inadequate infrastructure, teacher shortages, and regional imbalances in resource allocation (Amor et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; MOE\u0026amp;S JRES 2023).\u003c/p\u003e\u003cp\u003eThe problem this study addresses is the critical lack of granular, evidence-based understanding of how educational disparities manifest both geographically and across different academic subjects in Somaliland. While national reports acknowledge regional challenges (ESA \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hassan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kleibert et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Melesse and Obsiye \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), they often lack the spatio-temporal specificity required for targeted interventions. For instance, field visit reports highlight that schools in remote regions like Badhan face severe resource constraints and that teacher quality varies significantly between urban and rural areas (Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Holloway and Kirby 2019; MOE\u0026amp;S JRES 2023; Somaliland NDPIII \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These anecdotal and broad-stroke observations point to a deeper, systemic issue of spatial inequality that has not been systematically quantified.\u003c/p\u003e\u003cp\u003eThis lack of granular analysis represents a significant gap in the literature and a barrier to effective policymaking. While broad studies map educational attainment across the Global South (Collet-Sab\u0026eacute; \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sri \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and others examine spatial inequality in Africa (Delprato et al., 2024), there is a dearth of research that combines geographic, temporal, and subject-level data within a single national context like Somaliland. Without this integrated perspective, it is difficult to determine whether poor performance in a specific region is a universal issue or if it is driven by challenges in particular subjects, such as STEM fields, which often require more specialized resources and teaching expertise.\u003c/p\u003e\u003cp\u003eThis study is therefore of critical importance. By providing a detailed spatio-temporal analysis of national examination results, it moves beyond anecdotal evidence to offer a robust, quantitative assessment of educational disparities in Somaliland. It provides policymakers at the Ministry of Education and Science with a precise diagnostic tool, enabling them to identify not just which regions are underperforming, but in which subjects they are struggling the most. This evidence is crucial for designing targeted, cost-effective interventions\u0026mdash;from curriculum support and teacher training to resource allocation\u0026mdash;that address the specific needs of different regions, thereby advancing the national goal of equitable, quality education for all.\u003c/p\u003e\u003cp\u003eThe primary objective of this study is to analyze the spatio-temporal patterns of academic performance in the Somaliland National Certificate Examination from 2020 to 2023. To achieve this, the study pursues three specific aims: (1) to describe and visualize the geographic and subject-level trends in student pass rates across Somaliland\u0026rsquo;s administrative regions; (2) to statistically model the predictors of student success, including year, gender, region, and subject; and (3) to test the central hypothesis that the effect of academic subjects on student performance varies significantly across different geographic regions, thereby identifying specific region-subject challenges that require targeted policy attention.\u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cp\u003eThe study of educational inequality has increasingly embraced spatial and geographical perspectives to understand the complex mechanisms that produce and reproduce disparities (Beech, Larsen, and Wei 2024; Marin-Velasquez and Closson-Pitts 2019). This review synthesizes key literature across three domains. First, it outlines the conceptual and theoretical frameworks that underpin the analysis of education through a spatial lens, focusing on concepts of spatial justice and the geographies of education. Second, it reviews empirical and methodological studies that have employed geospatial and spatio-temporal analyses to investigate educational disparities in various global contexts. Finally, it synthesizes these insights to identify the critical gap this study addresses: the need for a granular, subject-specific, and spatio-temporal analysis of educational outcomes in a post-conflict setting like Somaliland.\u003c/p\u003e\u003cp\u003eThe critical geographies of education offer a powerful lens for examining how power, inequality, and social structures are inscribed in educational landscapes (Byun, Lee, and Hwang 2021a; Henry \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This perspective challenges the notion of schooling as an automatic social good, instead interrogating how educational systems can perpetuate existing injustices (Holloway and Kirby 2019; Di Martino, Gregorio, and Iannone 2022; Oppido, Ragozino, and Esposito De Vita 2023). A central concept within this field is spatial injustice, which occurs when the distribution of resources, opportunities, and institutions—such as schools—disproportionately harms marginalized communities (Tieken and Auldridge-Reveles 2019). School closures, for example, are not random events but often reflect and reinforce existing patterns of racial and economic segregation, limiting access to quality education for the most vulnerable populations (Boterman et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hirsh et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe concept of \"place\" is fundamental to this analysis, moving beyond a simple container for social processes to an active force that shapes educational experiences and outcomes (Butler and Sinclair \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Place-based inquiry illuminates how local contexts—including community resources, historical legacies, and social identities—interact to create unique barriers and opportunities for students (Easterbrook and Hadden 2020; Hinduja et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This perspective aligns with a feminist geopolitical approach, which calls for a multi-scalar analysis that accounts for how broad geopolitical processes are differentially experienced, negotiated, and contested in the localized spaces of schools and communities (Anne \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nguyen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such a framework reveals the intricate ways in which global policies and national agendas are translated and transformed at the local level.\u003c/p\u003e\u003cp\u003eTheorizing the relationship between educational systems and inequality requires understanding how macro-level characteristics shape micro-level outcomes. Systemic features such as standardization and stratification have been shown to have differential effects on gender, ethnic, and socioeconomic inequalities (Henry \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zapfe and Gross 2021). However, much of the research lacks a robust theoretical explanation for these links, highlighting a critical gap (Hirsh et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By integrating spatial theories, researchers can better analyze how these systemic characteristics are geographically expressed, creating a more nuanced understanding of how educational systems reproduce or challenge social stratification across space (Brekke \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perry, Rowe, and Lubienski 2021).\u003c/p\u003e\u003cp\u003eA growing body of empirical work leverages geospatial methods to map and analyze educational disparities. Systematic reviews demonstrate that GIS is a valuable tool for providing new insights into educational equity, access, and opportunity, particularly in areas like school choice and resource distribution (Cobb \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kocdar, Bozkurt, and Goru Dogan 2020a). In Pakistan, for instance, district-level modeling has revealed significant spatial inequalities in education status, with poverty and infrastructure emerging as key determinants (Sajjad et al., 20(Moscovitz and Sabzalieva 2023)22). Similarly, research across sub-Saharan Africa has used geo-localized survey data to show that spatial dependence is a crucial, yet often ignored, factor in educational attainment, with inequalities being more pronounced in marginalized communities (Frola et al. 2024a; Yoon \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe temporal dimension is equally critical. Longitudinal studies consistently show that the duration and timing of exposure to socioeconomic conditions are strongly linked to educational and cognitive outcomes, underscoring the need for a life-course perspective (Grant, Meissel, and Exeter 2023b; Puttick \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The COVID-19 pandemic starkly illustrated this, as control measures and the shift to remote learning exacerbated pre-existing educational inequalities, disproportionately affecting students from disadvantaged backgrounds (Darmody et al.,(Bellibaş and Gümüş 2019) 2021; Reuge et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The resulting \"digital divide\" is not merely about access to technology but encompasses a complex interplay of socioeconomic status, geography, and parental knowledge, creating new layers of disparity (Lythreatis et al., 2022; Martin et al., 2024).\u003c/p\u003e\u003cp\u003eMethodologically, the field is advancing beyond simple mapping to employ more sophisticated spatio-temporal analyses. While such methods are not yet common in education research, their application in public health offers a promising model for how to track the geographic and temporal distribution of outcomes and identify high-risk areas for intervention (Byun, Lee, and Hwang 2021b; Miseliunaite, Kliziene, and Cibulskas 2022). The use of systematic reviews and bibliometric analysis has also become crucial for mapping the landscape of knowledge in related fields, such as education for sustainable development (Hallinger and Kulophas 2019; Tieken and Auldridge-Reveles 2019) and educational technology (Bozkurt \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kocdar, Bozkurt, and Goru Dogan 2020b), identifying both dominant trends and critical research gaps. These reviews often highlight a significant imbalance, with a dearth of research originating from the Global South (Tlili et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Valverde-Berrocoso et al. 2020).\u003c/p\u003e\u003cp\u003eThe literature overwhelmingly confirms that geography is not a neutral backdrop for education but an active force that shapes opportunity and outcomes. From the macro-level of global geopolitics (Chen and Yang 2024; Moscovitz and Sabzalieva 2023) to the micro-level of classroom interactions ((Ahmed, Callaghan, and Arslan \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Çop, Olorunsola, and Alola 2020; Maheshwari, Kha, and Arokiasamy 2022), space and place are central to understanding educational processes. However, despite the growing sophistication of spatial methods, a critical gap remains in applying these tools to understand subject-specific disparities within a single, data-scarce national context. While large-scale studies can map broad patterns of attainment (Rashid \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sri \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), they often lack the granularity to inform targeted, subject-level interventions.\u003c/p\u003e\u003cp\u003eFurthermore, much of the research on educational inequality in fragile or post-conflict states focuses on broad access issues or specific themes like entrepreneurship education (Boterman et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Koh and Sin 2020; Rashid \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or mental health (Alabadla et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kebede et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), rather than on the core academic performance of the national school system. The existing body of work on Somaliland's education sector, while valuable, consists primarily of strategic plans, sector analyses, and field reports that identify challenges in broad strokes (e.g., ESA \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; MOE\u0026amp;S JRES 2023). These documents lack the rigorous, quantitative spatio-temporal analysis needed to pinpoint specific areas of academic weakness. This study directly addresses this gap by providing the first known spatio-temporal analysis of subject-level examination results in Somaliland, offering a new layer of evidence to guide the nation's journey toward educational equity.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003e\u003cb\u003e3. Data and Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study employs a quantitative approach to analyze the spatio-temporal patterns of academic performance in Somaliland. The methodology involved two primary stages: first, a descriptive and geospatial analysis to visualize performance trends, and second, an inferential statistical model to test the significance of geographic and subject-level disparities.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.1. Data Source and Preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe main dataset utilized in this research comprises anonymized, individual student-level outcomes from the Somaliland National Certificate Examination, sourced from the Somaliland National Examination and Certification Board for the years spanning 2020 to 2023. This extensive dataset encompassed several crucial variables for each exam entry: the exam year, the student's gender, the administrative region, the subject, and the final grade received. The dependent variable in our analysis was binary, indicating whether a student passed or failed an exam, coded as 1 for a \"Pass\" and 0 for any other result. The primary independent variables were categorized as follows: Year: A categorical variable with four categories (2020, 2021, 2022, 2023), with 2020 serving as the reference category in the statistical model. Sex: A binary categorical variable (Male/Female), with \"Female\" as the reference category. Region: A categorical variable with six categories representing Somaliland's administrative regions: Awdal, Maroodi Jeex, Sahil, Sanaag, Sool, and Togdheer. Maroodi Jeex, which includes the capital, was chosen as the reference category for comparison. Subject: A categorical variable with 10 categories, including Arabic, Biology, Chemistry, English, Geography, History, Islamic, Math, Physics, and Somali, was used as the reference category due to its consistently high pass rates across all regions. For the geospatial analysis, we employed a shapefile of Somaliland's six administrative regions. To prepare the data for visualization, individual student results were aggregated to determine the pass rate (calculated as the total number of passed exams divided by the total number of candidates) for each unique combination of region, year, and subject.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2. Analytical Strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analysis was conducted using R version 4.x.x with the dplyr, ggplot2, and sf packages for data manipulation and visualization, and the stats package for modeling.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2.1. Descriptive and Geospatial Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore trends and disparities, we first generated a series of line plots to visualize pass rate trajectories over the four-year study period. These visualizations were created from two perspectives: (1) faceted by region, to compare the performance of different subjects within each geographic area (Fig.\u0026nbsp;1), and (2) faceted by subject, to compare regional performance gaps for each academic discipline (Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eTo analyze the geographic distribution of overall academic achievement, we created a series of choropleth maps using the sf package (Fig.\u0026nbsp;3). For these maps, the pass rates for all subjects within a given region were averaged for each year to produce a single, comparable measure of annual regional performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2.2. Inferential Statistical Modeling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo move beyond descriptive analysis and statistically test the significance of the observed patterns, we employed a logistic regression model. This model is appropriate as the outcome variable—passing an exam—is binary. The model was designed to predict the probability of a student passing as a function of year, sex, region, and subject.\u003c/p\u003e\u003cp\u003eCrucially, the model included a two-way interaction term between Region and Subject (Region * Subject). This interaction term tests the primary hypothesis of this study: that the effect of a particular subject on a student's likelihood of passing is not constant across the country but differs significantly depending on the student's region. The results of the regression are reported as coefficients (\u003cem\u003eB\u003c/em\u003e) and odds ratios (OR), which represent the multiplicative change in the odds of passing for a given predictor, holding all other variables constant. The full model specification was:\u003c/p\u003e\u003cp\u003ePass/Fail ~ Year + Sex + Region * Subject\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section presents the findings from the spatio-temporal analysis of academic performance on the Somaliland National Certificate Examination from 2020 to 2023. The results are organized to first describe the visual patterns of performance across regions and subjects, followed by a statistical model that quantifies the significance of these disparities.\u003c/p\u003e\n\u003ch3\u003eDescriptive Analysis of Performance Trends\u003c/h3\u003e\n\u003cp\u003eVisual analysis of pass rates reveals significant heterogeneity in academic performance, both within and between Somaliland's administrative regions. Figure\u0026nbsp;1 illustrates the performance trajectories for 10 core subjects within each of the six regions. A clear hierarchy of subject difficulty is evident across most regions, with language and religious studies (e.g., Somali, Islamic, Arabic) consistently showing higher pass rates than science and mathematics subjects (e.g., Chemistry, Physics, Math). Performance in the Awdal and Maroodi Jeex regions appears more volatile, with wider spreads in subject performance, whereas the eastern regions of Sool and Sanaag exhibit more compressed and consistently high pass rates, albeit with a slight decline over the period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo examine regional disparities more directly, Fig.\u0026nbsp;2 presents the performance of each region faceted by subject. These plots highlight a persistent and often substantial performance gap between regions, particularly in STEM subjects. For example, in Chemistry, Math, and Physics, the pass rates in Maroodi Jeex are consistently and significantly lower than in all other regions. This gap narrowed slightly over the four-year period but remained pronounced in 2023. In contrast, subjects such as Somali and Biology show much smaller, though still noticeable, differences in performance across regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe overall spatial pattern of academic achievement is summarized in Fig.\u0026nbsp;3, which displays the average pass rate across all subjects for each region and year. The choropleth maps reveal a distinct spatial gradient, with the eastern regions (Sool, Sanaag) and coastal region (Sahil) consistently outperforming the western regions of Awdal and Maroodi Jeex. Over the four-year period, Maroodi Jeex consistently registered the lowest average pass rates, a pattern that intensified from 2021 to 2023.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical Modeling of Performance Predictors\u003c/h3\u003e\n\u003cp\u003eTo statistically test the significance of these observed trends, a logistic regression model was estimated to predict the probability of a student passing a given exam. The model included year, student sex, region, and subject as main effects, along with an interaction term between region and subject to assess whether subject-specific performance gaps varied geographically. The results of the model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression Predicting the Probability of Passing an Examination\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYear (Ref: 2020)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (Ref: Female)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion (Ref: Maroodi Jeex)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAwdal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSahil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanaag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTogdheer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubject (Ref: Islamic)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArabic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemistry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnglish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInteraction Effects (Region x Subject)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAwdal x Math\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSanaag x English\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSool x Geography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTogdheer x Biology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e:\u003cem\u003eB\u0026thinsp;=\u0026thinsp;logistic regression coefficient; SE\u0026thinsp;=\u0026thinsp;Standard Error; OR\u0026thinsp;=\u0026thinsp;Odds Ratio. Only a selection of significant interaction terms are presented for brevity.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe model revealed significant effects for year, region, and subject. Compared to the reference year 2020, the odds of passing were significantly lower in 2021 but significantly higher in 2022 and 2023. No statistically significant difference in the odds of passing was found between male and female students (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.764). The main effects for subjects confirmed the visual hierarchy observed in Fig.\u0026nbsp;1; compared to the reference subject of Islamic studies, students had significantly lower odds of passing Chemistry (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37), Math (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27), and Physics (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45), and significantly higher odds of passing History (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.15) and Somali (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.97).\u003c/p\u003e\u003cp\u003eMost critically, the model identified highly significant interaction effects between region and subject, indicating that the magnitude of subject difficulty is not uniform across Somaliland. For instance, the significant negative interaction for Region_Names Sanaag: Subject English (\u003cem\u003eB\u003c/em\u003e = -0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) shows that the performance disadvantage in English (relative to Islamic studies) is significantly more severe for students in Sanaag than for their peers in the reference region of Maroodi Jeex. Conversely, the significant positive interaction for Region_ Names Togdheer: Subject Math (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) indicates that the performance gap between Math and Islamic studies is substantially smaller in Togdheer compared to Maroodi Jeex. These findings provide robust statistical evidence that academic challenges are geographically concentrated and that interventions must consider the unique subject-specific needs of each region.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study sought to provide a granular, spatio-temporal analysis of educational outcomes in Somaliland, and the findings reveal a complex landscape of inequality. The results confirm that while progress is being made, significant disparities persist along both geographic and subject-level lines. This discussion interprets these findings in relation to the existing literature, explores their theoretical implications, and synthesizes their contribution to understanding educational equity in post-conflict contexts.\u003c/p\u003e\u003cp\u003eThe descriptive and geospatial analyses clearly support the central premise of contemporary educational geography: place matters profoundly (Butler \u0026amp; Sinclair, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The distinct spatial gradient, with eastern regions like Sool and Sanaag consistently outperforming the western capital region of Maroodi Jeex, challenges simplistic urban-rural dichotomies. This finding aligns with research from other developing contexts, such as Pakistan, which has uncovered significant subnational variation in educational status that does not always map neatly onto urban development (Sajjad et al., 2022). This geographic disparity suggests that factors beyond simple infrastructure, such as local governance, community engagement, or historical educational investment, may be at play (Takyi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The volatility in performance in the western regions, compared to the stability in the east, further suggests differing levels of systemic resilience and resource consistency.\u003c/p\u003e\u003cp\u003eThe stark hierarchy of subject difficulty, with STEM subjects consistently showing the lowest pass rates, is a critical finding with significant implications for Somaliland's development. This pattern is not uncommon, but its severity points to systemic weaknesses in resource-intensive educational areas (Brekke, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The struggle in subjects like Chemistry, Math, and Physics likely reflects a shortage of qualified teachers, a lack of laboratory facilities, and inadequate teaching materials\u0026mdash;challenges frequently noted in Somaliland's education sector reports (Ministry of Education \u0026amp; Science, 2021; IESCOL, February 2023, FIELD VISITS REPORT). This subject-level divide poses a direct threat to the nation's capacity to build the human capital required for a modern, diversified economy, a key goal of its national vision (Ministry of Education \u0026amp; Science, 2022).\u003c/p\u003e\u003cp\u003eThe most significant contribution of this study lies in the inferential findings from the logistic regression model, particularly the interaction effects between region and subject. The results move beyond simply stating that geography and subject matter, to demonstrating that how much they matter depends on their intersection. For example, the finding that the performance disadvantage in English is more severe in Sanaag than in Maroodi Jeex provides precise, actionable evidence for policymakers. It suggests that a generic, nationwide intervention to improve English instruction would be inefficient. Instead, resources should be targeted to regions where the challenge is most acute, a principle that aligns with calls for place-sensitive and context-specific policy (Zapfe \u0026amp; Gross, 2021).\u003c/p\u003e\u003cp\u003eConversely, the positive interaction showing that the performance gap between Math and Islamic studies is smaller in Togdheer than in Maroodi Jeex is equally insightful. This suggests the presence of localized strengths or effective practices in Togdheer's teaching of mathematics that could be studied and potentially scaled to other regions. This finding supports the idea that educational systems are not monolithic and that solutions to inequality can often be found by identifying and understanding positive deviance within the system itself (Henry, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The lack of a significant gender disparity in overall pass rates is an encouraging finding, though it warrants further investigation at the subject level to ensure this equity holds across all disciplines, particularly in STEM fields where gender gaps are common globally.\u003c/p\u003e\u003cp\u003eTheoretically, these findings reinforce the principles of spatial justice and the need for a multi-scalar analysis of education (Nguyen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tieken \u0026amp; Auldridge-Reveles, 2019). The study demonstrates that educational inequality is not a flat, national phenomenon but a textured, geographically contingent reality. The interaction effects provide empirical weight to the argument that educational policies must be designed with a deep understanding of local contexts and the specific challenges faced by different communities (Beech et al., 2024). By providing a detailed map of these intersecting disparities, this study offers a new level of evidence that can help Somaliland move from broad strategic goals to targeted, effective, and equitable educational interventions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePolicy Implications\u003c/h2\u003e\u003cp\u003eThe findings of this study offer direct implications for educational policy in Somaliland. First, regional disparities necessitate a shift from a one-size-fits-all policy to targeted regional investment. Resources for teacher training, materials, and infrastructure should be disproportionately allocated to western regions, particularly Maroodi Jeex, which shows the lowest performance. Second, the subject-level divide, especially underperformance in STEM subjects, requires specific curriculum and pedagogical interventions. The Ministry of Education and Science should review the STEM curriculum, focusing on relevance, accessibility, and availability of teaching aids and laboratory equipment. A national strategy for recruiting and incentivizing qualified STEM teachers is essential to build capacity in these fields. Third, the interaction effects between region and subject provide precise guidance for policy. For subjects like English in Sanaag, where performance gaps are severe, targeted support programs should be developed, including professional development for teachers and supplementary learning materials. For subjects like Math in Togdheer, where performance is strong, the Ministry should study successful local practices that can be adapted to other regions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a rigorous, spatio-temporal analysis of educational outcomes in Somaliland, revealing a complex landscape of inequality defined by both geography and academic subject. By analyzing four years of national examination data, the research moves beyond broad generalizations to offer specific, evidence-based insights into the nature of educational disparities. The findings confirm that a student's likelihood of success is significantly shaped not only by the subject they study but also by the region in which they are educated. The persistent underperformance of western regions and the nationwide struggle in STEM subjects represent major obstacles to Somaliland's development goals.\u003c/p\u003e\u003cp\u003eThe study's most critical contribution is the identification of significant interaction effects, demonstrating that educational challenges are not uniform but are geographically concentrated and subject-specific. This evidence fundamentally challenges the efficacy of uniform national policies and underscores the need for a more nuanced, data-driven, and targeted approach. By providing a detailed map of these intersecting disparities, this research equips policymakers with the evidence needed to allocate resources more effectively, design context-sensitive interventions, and ultimately foster a more equitable and effective education system for all of Somaliland's children.\u003c/p\u003e\n\u003ch3\u003eStudy Limitations\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations that should be acknowledged. First, the analysis relies on examination data, which measures a specific form of academic performance but does not capture other crucial aspects of educational quality, such as student well-being or the development of non-cognitive skills. Second, the dataset, while comprehensive in its coverage of exam takers, lacks granular school-level or teacher-level variables. Consequently, the study can identify where disparities exist but cannot definitively explain the causal mechanisms, such as differences in school resources, teacher qualifications, or pedagogical practices. Third, the analysis is limited to the administrative regions as the unit of spatial analysis and does not capture potential intra-regional disparities, such as those between urban and rural areas within a single region. Finally, while the study identifies trends over a four-year period, this timeframe may not be sufficient to capture the full impact of longer-term policy changes or educational reforms.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003eIn light of the findings, the following suggestions are presented to the Somaliland Ministry of Education and Science: Implement Region-Specific Funding: Revise the budget to allocate resources more effectively, with a focus on the western regions, particularly Maroodi Jeex, to enhance school infrastructure and support for teachers. Initiate a National STEM Program: Formulate a plan to boost performance in Math, Physics, and Chemistry by reviewing the curriculum, providing standardized laboratory kits, and offering specialized training for teachers. Create Regional Centers: Designate successful regions and schools in difficult subjects (such as Math in Togdheer) as hubs for peer learning and the sharing of best practices. Improve Data Analysis: Strengthen the capabilities of Regional Education Offices to evaluate student performance data, enabling more responsive local planning and interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRecommendation for Future Research\u003c/h2\u003e\u003cp\u003eThe findings open several important avenues for future research. First, qualitative case studies are needed to understand causes of the observed disparities. Research should examine high- and low-performing regions to explore school-level, teacher-level, and community factors contributing to divergent outcomes. Second, future research should link student performance data with teacher qualifications, deployment, and school resources to enable robust causal analysis of inequality drivers. Finally, a longitudinal study tracking students through their educational journey would provide insights into educational inequality dynamics and critical intervention points.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics Statement\u003c/h2\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.A.C. and J.A.A. conceptualized the study, curated the data, and wrote the manuscript text. M.A.C. and A.H.M. conducted the formal analysis and managed the software, with support from M.K.A. J.A.A. performed the validation. M.A.O. provided editing and proofreading. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe individual-level student examination data analysed during the current study were obtained from the Somaliland National Examination and Certification Board. Due to the sensitive and confidential nature of these educational records, restrictions apply to the availability of these data, and they are not publicly available. Access to the data may be considered upon reasonable request to the Somaliland National Examination and Certification Board, subject to their data sharing policies and approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmed, Fawad, Deborah Callaghan, and Ahmad Arslan. 2023. \u0026ldquo;A Multilevel Conceptual Framework on Green Practices: Transforming Pol into Actionable Leadership and Employee Behavior.\u0026rdquo; \u003cem\u003eScandinavian Journal of Psychology\u003c/em\u003e 65(3):381\u0026ndash;93. doi:10.1111/sjop.12981.\u003c/li\u003e\n \u003cli\u003eAlabadla, Mustafa, Fatimah Sidi, Iskandar Ishak, Hamidah Ibrahim, Lilly Suriani Affendey, Zafienas Che Ani, Marzanah A. Jabar, Umar Ali Bukar, Navin Kumar Devaraj, Ahmad Sobri Muda, Anas Tharek, Noritah Omar, and M. 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Matthews, and Kimberley Breevaart. 2020. \u0026ldquo;Leading Day-to-Day: A Review of the Daily Causes and Consequences of L Behaviors.\u0026rdquo; \u003cem\u003eThe Leadership Quarterly\u003c/em\u003e 31(1):101344. doi:10.1016/j.leaqua.2019.101344.\u003c/li\u003e\n \u003cli\u003eKleibert, Jana M., Alice Bob\u0026eacute;e, Tim Rottleb, and Marc Schulze. 2020. \u0026ldquo;Transnational Education Zones: Towards an Urban Political Economy of \u0026lsquo; Education Cities.\u0026rsquo;\u0026rdquo; \u003cem\u003eUrban Studies\u003c/em\u003e 58(14):2845\u0026ndash;62. doi:10.1177/0042098020962418.\u003c/li\u003e\n \u003cli\u003eKocdar, Serpil, Aras Bozkurt, and Tulay Goru Dogan. 2020a. \u0026ldquo;Engineering through Distance Education in the Time of the Fourth Indus Revolution: Reflections from Three Decades of Peer Reviewed Stud.\u0026rdquo; \u003cem\u003eComputer Applications in Engineering Education\u003c/em\u003e 29(4):931\u0026ndash;49. doi:10.1002/cae.22367.\u003c/li\u003e\n \u003cli\u003eKocdar, Serpil, Aras Bozkurt, and Tulay Goru Dogan. 2020b. \u0026ldquo;Engineering through Distance Education in the Time of the Fourth Indus Revolution: Reflections from Three Decades of Peer Reviewed Stud.\u0026rdquo; \u003cem\u003eComputer Applications in Engineering Education\u003c/em\u003e 29(4):931\u0026ndash;49. doi:10.1002/cae.22367.\u003c/li\u003e\n \u003cli\u003eKoh, Sin Yee, and I. Lin Sin. 2020. \u0026ldquo;Academic and Teacher Expatriates: Mobilities, Positionalities, and Sub.\u0026rdquo; \u003cem\u003eGeography Compass\u003c/em\u003e 14(5). doi:10.1111/gec3.12487.\u003c/li\u003e\n \u003cli\u003eMaheshwari, Greeni, Khanh Linh Kha, and Anantha Raj A. Arokiasamy. 2022. \u0026ldquo;Factors Affecting Students\u0026rsquo; Entrepreneurial Intentions: A Systematic R (2005\u0026ndash;2022) for Future Directions in Theory and Practice.\u0026rdquo; \u003cem\u003eManagement Review Quarterly\u003c/em\u003e 73(4):1903\u0026ndash;70. doi:10.1007/s11301-022-00289-2.\u003c/li\u003e\n \u003cli\u003eMarin-Velasquez, Melba, and Brittany Closson-Pitts. 2019. \u0026ldquo;Forging the Ideal Educated Girl: The Production of Desirable Subjects \u0026nbsp;in Muslim South Asia.\u0026rdquo; \u003cem\u003eGender and Education\u003c/em\u003e 31(4):560\u0026ndash;61. doi:10.1080/09540253.2019.1583322.\u003c/li\u003e\n \u003cli\u003eDi Martino, Pietro, Francesca Gregorio, and Paola Iannone. 2022. \u0026ldquo;The Transition from School to University in Mathematics Education Rese: New Trends and Ideas from a Systematic Literature Review.\u0026rdquo; \u003cem\u003eEducational Studies in Mathematics\u003c/em\u003e 113(1):7\u0026ndash;34. doi:10.1007/s10649-022-10194-w.\u003c/li\u003e\n \u003cli\u003eMelesse, Tadesse, and Fuad A. Obsiye. 2022. \u0026ldquo;Analysing the Education Policies and Sector Strategic Plans of Somalil.\u0026rdquo; \u003cem\u003eCogent Education\u003c/em\u003e 9(1). doi:10.1080/2331186x.2022.2152545.\u003c/li\u003e\n \u003cli\u003eMetwally, Ahmed Hosny Saleh, Lennart E. Nacke, Maiga Chang, Yining Wang, and Ahmed Mohamed Fahmy Yousef. 2021. \u0026ldquo;Revealing the Hotspots of Educational Gamification: An Umbrella Review.\u0026rdquo; \u003cem\u003eInternational Journal of Educational Research\u003c/em\u003e 109:101832. doi:10.1016/j.ijer.2021.101832.\u003c/li\u003e\n \u003cli\u003eMiseliunaite, Brigita, Irina Kliziene, and Gintautas Cibulskas. 2022. \u0026ldquo;Can Holistic Education Solve the World\u0026rsquo;s Problems: A Systematic Litera Review.\u0026rdquo; \u003cem\u003eSustainability\u003c/em\u003e 14(15):9737. doi:10.3390/su14159737.\u003c/li\u003e\n \u003cli\u003eMOE\u0026amp;S ESSP. 2022. \u003cem\u003eEducation Sector Strategic Plan 2022-2026\u003c/em\u003e. Hargeisa, Somaliland: Somaliland Ministry of Education and Science.\u003c/li\u003e\n \u003cli\u003eMOE\u0026amp;S JRES. 2023. \u003cem\u003eJoint Review of the Education Sector (JRES) Synthesis Report\u003c/em\u003e. Hargeisa, Somaliland: Somaliland Ministry of Education and Science.\u003c/li\u003e\n \u003cli\u003eMoscovitz, Hannah, and Emma Sabzalieva. 2023. \u0026ldquo;Conceptualising the New Geopolitics of Higher Education.\u0026rdquo; \u003cem\u003eGlobalisation, Societies and Education\u003c/em\u003e 21(2):149\u0026ndash;65. doi:10.1080/14767724.2023.2166465.\u003c/li\u003e\n \u003cli\u003eNguyen, Nicole. 2020. \u0026ldquo;On Geopolitics and Education: Interventions, Possibilities, and Future Directions.\u0026rdquo; \u003cem\u003eGeography Compass\u003c/em\u003e 14(9). doi:10.1111/gec3.12500.\u003c/li\u003e\n \u003cli\u003eOppido, Stefania, Stefania Ragozino, and Gabriella Esposito De Vita. 2023. \u0026ldquo;Peripheral, Marginal, or Non-Core Areas? Setting the Context to Deal W Territorial Inequalities through a Systematic Literature Review.\u0026rdquo; \u003cem\u003eSustainability\u003c/em\u003e 15(13):10401. doi:10.3390/su151310401.\u003c/li\u003e\n \u003cli\u003ePerry, Laura B., Emma Rowe, and Christopher Lubienski. 2021. \u0026ldquo;School Segregation: Theoretical Insights and Future Directions.\u0026rdquo; \u003cem\u003eComparative Education\u003c/em\u003e 58(1):1\u0026ndash;15. doi:10.1080/03050068.2021.2021066.\u003c/li\u003e\n \u003cli\u003ePuttick, Steve. 2023. \u0026ldquo;Geographical Education II: Anti-Racist, Decolonial Futures.\u0026rdquo; \u003cem\u003eProgress in Human Geography\u003c/em\u003e 47(6):850\u0026ndash;58. doi:10.1177/03091325231202248.\u003c/li\u003e\n \u003cli\u003eRashid, Lubna. 2019. \u0026ldquo;Entrepreneurship Education and Sustainable Development Goals: A Litera Review and a Closer Look at Fragile States and Technology-Enabled Approaches.\u0026rdquo; \u003cem\u003eSustainability\u003c/em\u003e 11(19):5343. doi:10.3390/su11195343.\u003c/li\u003e\n \u003cli\u003eReuge, Nicolas, Robert Jenkins, Matt Brossard, Bobby Soobrayan, Suguru Mizunoya, Jim Ackers, Linda Jones, and Wongani Grace Taulo. 2021. \u0026ldquo;Education Response to COVID 19 Pandemic, a Special Issue Proposed by U: Editorial Review.\u0026rdquo; \u003cem\u003eInternational Journal of Educational Development\u003c/em\u003e 87:102485. doi:10.1016/j.ijedudev.2021.102485.\u003c/li\u003e\n \u003cli\u003eSomaliland NDPIII. 2023. \u003cem\u003eNational Development Plan III (2023-2027)\u003c/em\u003e. Hargeisa, Somaliland: Ministry of Planning \u0026amp; National Development.\u003c/li\u003e\n \u003cli\u003eSri, Utaminingsih. 2019. \u0026ldquo;Kebijakan Penyelenggaraan Pendidikan Anak Usia Dini (PAUD) Berdasarkan Undang-Undang Nomor 20 Tahun 2003 Tentang Sistem Pendidikan Nasional \u0026nbsp;Dalam Perspektif Negara Hukum Kesejahteraan (Studi Kasus Di Kota Tange Selatan).\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eTakyi, Stephen Appiah, Owusu Amponsah, Michael Osei Asibey, and Raphael Anammasiya Ayambire. 2019. \u0026ldquo;An Overview of Ghana\u0026rsquo;s Educational System and Its Implication for Educ Equity.\u0026rdquo; \u003cem\u003eInternational Journal of Leadership in Education\u003c/em\u003e 24(2):157\u0026ndash;82. doi:10.1080/13603124.2019.1613565.\u003c/li\u003e\n \u003cli\u003eTieken, Mara Casey, and Trevor Ray Auldridge-Reveles. 2019. \u0026ldquo;Rethinking the School Closure Research: School Closure as Spatial Inju.\u0026rdquo; \u003cem\u003eReview of Educational Research\u003c/em\u003e 89(6):917\u0026ndash;53. doi:10.3102/0034654319877151.\u003c/li\u003e\n \u003cli\u003eTlili, Ahmed, Fahriye Altinay, Ronghuai Huang, Zehra Altinay, Jako Olivier, Sanjaya Mishra, Mohamed Jemni, and Daniel Burgos. 2022. \u0026ldquo;Are We There yet? A Systematic Literature Review of Open Educational R in Africa: A Combined Content and Bibliometric Analysis.\u0026rdquo; \u003cem\u003ePLOS ONE\u003c/em\u003e 17(1):e0262615. doi:10.1371/journal.pone.0262615.\u003c/li\u003e\n \u003cli\u003eValverde-Berrocoso, Jes\u0026uacute;s, Mar\u0026iacute;a del Carmen Garrido-Arroyo, Carmen Burgos-Videla, and Mar\u0026iacute;a Bel\u0026eacute;n Morales-Cevallos. 2020. \u0026ldquo;Trends in Educational Research about E-Learning: A Systematic Literatu Review (2009\u0026ndash;2018).\u0026rdquo; \u003cem\u003eSustainability\u003c/em\u003e 12(12):5153. doi:10.3390/su12125153.\u003c/li\u003e\n \u003cli\u003eYoon, Ee-Seul. 2019. \u0026ldquo;School Choice Research and Politics with Pierre Bourdieu: New Possibil.\u0026rdquo; \u003cem\u003eEducational Policy\u003c/em\u003e 34(1):193\u0026ndash;210. doi:10.1177/0895904819881153.\u003c/li\u003e\n \u003cli\u003eZapfe, Laura, and Christiane Gross. 2021. \u0026ldquo;How Do Characteristics of Educational Systems Shape Educational Inequa? Results from a Systematic Review.\u0026rdquo; \u003cem\u003eInternational Journal of Educational Research\u003c/em\u003e 109:101837. doi:10.1016/j.ijer.2021.101837.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Educational Inequality, Geospatial Analysis, Spatio-Temporal, Somaliland, Student Performance, Subject-Level Disparities, Educational Policy","lastPublishedDoi":"10.21203/rs.3.rs-7069860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7069860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study provides a comprehensive spatio-temporal analysis of educational outcomes in Somaliland, examining geographic and subject-level disparities in student performance. The primary objective was to identify and quantify patterns of inequality in national examination results to inform evidence-based educational policy. The study employed a quantitative approach, analyzing an anonymized, individual-level dataset of Somaliland National Certificate Examination results from 2020 to 2023. The methodology involved descriptive geospatial analysis to visualize performance trends and an inferential logistic regression model to test the significance of regional and subject-level divides, including a crucial interaction term between the two. The findings reveal significant heterogeneity in academic performance across Somaliland\u0026rsquo;s administrative regions, with eastern regions consistently outperforming western regions. A clear hierarchy of subject difficulty was identified, with STEM subjects (Chemistry, Physics, Math) showing significantly lower pass rates than language and religious studies. Most critically, the analysis uncovered significant interaction effects, indicating that the magnitude of subject difficulty varies substantially by region. This suggests that national-level educational challenges are geographically concentrated and that uniform policy interventions are likely to be ineffective. The study\u0026rsquo;s originality lies in its granular, spatio-temporal examination of subject-specific educational disparities in a post-conflict, data-scarce context, providing a robust evidence base for targeted policy interventions aimed at fostering educational equity.\u003c/p\u003e","manuscriptTitle":"Geographic Disparities and Subject-Level Divides: A Spatio-Temporal Analysis of Educational Outcomes in Somaliland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 06:04:32","doi":"10.21203/rs.3.rs-7069860/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T11:51:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T05:14:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T16:49:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202529550705568974944028113194197984226","date":"2026-02-11T18:43:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294898471236416701722761743886918709004","date":"2026-01-23T01:47:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T15:59:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162515851882232982837406479374225726050","date":"2026-01-05T06:22:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2750765937535512215224698714790954881","date":"2025-09-02T16:46:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-02T08:13:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-30T08:01:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T23:39:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T20:13:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-08-20T20:10:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"336da177-4de9-4d48-92cd-454d0a4d70e5","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54314483,"name":"Scientific community and society/Geography"},{"id":54314484,"name":"Social science/Geography"},{"id":54314485,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-16T01:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 06:04:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7069860","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7069860","identity":"rs-7069860","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}