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Abstract Background The increasing levels of stress among students worldwide pose a significant challenge to educational institutions. This study aims to systematically identify and analyse the factors contributing to student stress using advanced machine learning techniques. Objective To explore the primary stressors affecting students and to evaluate the interrelations among psychological, physiological, environmental, academic, and social factors in influencing student stress levels. Methods The study utilized a comprehensive dataset, StressLevelDataset.csv, collected from a diverse group of 1100 students across various educational institutions. We employed machine learning tools, including correlation analysis and feature importance analysis using Random Forest models, to identify and rank the most significant stressors. Results Key findings suggest that psychological factors like self-esteem and physiological factors like sleep quality are crucial predictors of stress levels in students. A significant negative correlation was found between students’ anxiety levels and their academic performance, highlighting the adverse impacts of psychological stress on educational outcomes. Conclusion The results underscore the importance of targeted interventions focusing on mental health and well-being within educational settings. By addressing the identified stressors, particularly in the psychological and physiological domains, educational institutions can enhance student well-being and improve academic performance.

Nowadays, individuals can be overwhelmed by a huge number of documents being present in daily life. Capturing the necessary details is often a challenge. Therefore, it is rather important to summarize documents to obtain the main information quickly. There currently exist automatic approaches to this task, but their quality is often not properly assessed. State-of-the-art metrics rely on human-generated summaries as a reference for the evaluation. If no reference is given, the assessment will be challenging. Therefore, in the absence of human-generated reference summaries, we investigated an alternative approach to how machine-generated summaries can be evaluated. For this, we focus on the original text or document to retrieve a metric that allows a direct evaluation of automatically generated summaries. This approach is particularly helpful in cases where it is difficult or costly to find reference summaries. In this paper, we present a novel metric called Summary Score without Reference—SUSWIR—which is based on four factors already known in the text summarization community: Semantic Similarity, Redundancy, Relevance, and Bias Avoidance Analysis, overcoming drawbacks of common metrics. Therefore, we aim to close a gap in the current evaluation environment for machine-generated text summaries. The novel metric is introduced theoretically and tested on five datasets from their respective domains. The conducted experiments yielded noteworthy outcomes, employing the utilization of SUSWIR.