Multilingual Competency and Academic Performance: A Machine Learning-Based Analysis of the 2022/2023 Somaliland National Primary Exam Data

Multilingual Competency and Academic Performance: A Machine Learning-Based Analysis of the 2022/2023 Somaliland National Primary Exam Data | 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 Multilingual Competency and Academic Performance: A Machine Learning-Based Analysis of the 2022/2023 Somaliland National Primary Exam Data Jibril Abdikadir Ali, Mustafe Khadar Abdi, Tawakal Abdi Ali, Abdisalan Hassan Muse, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5237882/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study assesses the influence of language proficiency on academic performance in primary education within Somaliland, utilizing data from the 2022/2023 National Primary Exams. Employing a dataset of 20,638 students and applying ten machine learning regression models, the research investigates the impact of Somali, Arabic, and English language skills on overall academic outcomes. The findings reveal that proficiency in these languages significantly contributes to overall performance, with English showing the strongest positive association, followed by Arabic and Somali. Additionally, the study highlights minimal gender disparities in academic performance, aligning with previous research from the region. However, the urban-rural divide in educational outcomes remains substantial, with urban students outperforming their rural counterparts. Machine learning models, particularly Polynomial Regression, outperformed traditional methods in predicting student success, showcasing the utility of advanced analytics in educational research. These findings offer critical insights for policymakers aiming to improve language education, reduce regional disparities, and promote equitable access to quality education across Somaliland. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Software Physical sciences/Mathematics and computing/Statistics Language proficiency academic performance primary education machine learning multilingual education gender disparities urban-rural divide educational outcomes Somaliland regression models. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5237882","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":378599265,"identity":"d5f377d9-849d-4157-ac79-8fa7b734ffec","order_by":0,"name":"Jibril Abdikadir Ali","email":"","orcid":"","institution":"Amoud University","correspondingAuthor":false,"prefix":"","firstName":"Jibril","middleName":"Abdikadir","lastName":"Ali","suffix":""},{"id":378599266,"identity":"b4c1e33e-e340-4b1a-a372-afc459c56aa6","order_by":1,"name":"Mustafe Khadar Abdi","email":"","orcid":"","institution":"Amoud 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