Identifying top-performing students via VKontakte social media communities using advanced NLP techniques

doi:10.1109/ACCESS.2024.3521857

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DOI Доступ к ресурсу на сайте издателя			10.1109/ACCESS.2024.3521857

#образование

#информационные технологии

#интеллектуальный анализ образовательных данных

#обработка естественного языка

#цифровой след

#адаптация к предметной области

Title: Identifying top-performing students via VKontakte social media communities using advanced NLP techniques
Creator: Gorshkov, Sergei S.
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Ignatov, Dmitry I.
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Chernysheva, Anastasia Yu.
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Goiko, Vyacheslav L.
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Kashpur, Vitaliy V.
Date: 2025
DOI: 10.1109/ACCESS.2024.3521857
Description: Identifying potentially high-performing students is crucial for universities aiming to enhance educational outcomes, for companies seeking to recruit top talents early, and for advertising platforms looking to optimize targeted marketing. This paper introduces an algorithm designed to identify students with exceptional academic performance by analyzing their subscriptions to communities on the social network VKontakte. The study examines a sample of 4445 students from Tomsk State University with publicly accessible VK profiles. The research methodology involves generating vector representations for each community based on embeddings, topic modeling, sentiment and emotion analysis, as well as text complexity metrics. To generate the embeddings, a separate model was trained and made publicly available on HuggingFace. The integration of diverse features was achieved using attention mechanisms, allowing the model to dynamically weigh their importance and capture intricate interrelations. These representations are then used to construct a digital user profile, capturing the students’ interests as reflected in their community subscriptions. Additionally, the machine learning pipeline incorporated stacking to combine predictions from multiple models, enhancing robustness and classification performance. Through a series of experiments, we developed a machine learning algorithm that effectively distinguishes between highand low-performing students based on these profiles. This approach also enabled the identification and interpretation of key factors differentiating high-performing students from their lower-performing peers. Additionally, we investigated the factors positively and negatively associated with academic performance.