AI Can Predict Student Academic Performance Based on Social Media Subscriptions
A team of Russian researchers, including scientists from HSE University, used AI to analyse 4,500 students’ subscriptions to VK social media communities. The study found that algorithms can accurately identify both high-performing students and those struggling with their studies. The paper has been published in IEEE Access.
Every person leaves behind a digital footprint—likes, photos, music listening habits, and clicks on links—so even the most cautious users can be profiled based on their online activity. Some people believe they do not need to manage their digital footprint, as they assume that social media information cannot affect their professional or personal lives. Yet for scientists, publicly available data on people’s online activity provides valuable material for research.
A team of researchers from HSE University, Skoltech, and Tomsk State University (TSU) collected data on VK community subscriptions from 4,445 students with publicly available profiles. Then, using natural language processing (NLP) techniques, the researchers classified the topics of these social media communities, assessed the complexity of the texts students read, and analysed the emotional tone of the content. They then compiled a digital profile for each student, capturing their preferences and interests. Afterward, the researchers used machine learning to explore the relationship between students’ online activity and their academic performance.
The researchers developed an algorithm that predicts academic performance by analysing students’ online community subscriptions. In particular, high-performing students are more likely to subscribe to science and education communities that discuss new technologies and publish analytical articles. High achievers also tend to read more complex texts and show greater interest in discussions and in-depth analyses of information.
In contrast, their low-performing peers are more likely to subscribe to entertainment communities focused on humour, memes, music, and video games. The content in these communities expressed more negative emotions and was also less informative than the content favoured by top performers.
Sergei Gorshkov
'Some of the results surprised us. For example, students interested in art or travel tend to perform exceptionally well academically. These hobbies do not interfere with their studies—in fact, they seem to enhance academic performance. In contrast, active engagement with communities related to side jobs is associated with lower academic achievement, which is understandable,' comments Sergei Gorshkov, Doctoral Student at the School of Data Analysis and Artificial Intelligence of the HSE Faculty of Computer Science.
Educational institutions can use this approach to identify talented applicants and tailor their curricula to specific groups. Additionally, subscription analysis can assist employers in recruiting candidates with strong analytical abilities.
Dmitry Ignatov
'This study serves as yet another reminder of the importance of digital hygiene. For example, when signing contracts to open a bank account or with a mobile operator, you may be asked to grant permission to access information from social network accounts linked to your phone number. This data can later be used to create your digital profile. Whether or not you want this to happen is up to you,' says Dmitry Ignatov, Head of the Laboratory for Models and Methods of Computational Pragmatics at the HSE Faculty of Computer Science.
This work is part of an open data study supported by the University Consortium of Big Data Researchers and approved by the Ethics Committee of the TSU Faculty of Psychology.
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