The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Technological study programs in universities often experience high dropout rates, which makes it essential to analyze and predict potential risks to reduce dropout percentages. Student performance prediction (SPP) offers potential benefits, including personalized learning and early interventions. However, challenges such as (1) data quality and availability and (2) incomplete and inconsistent data complicate this process. Moreover, to support the fourth Sustainable Development Goal (SDG), we focus on the quality of education. A case study approach is used using data mining techniques, particularly classification, regression, and clustering, to predict student performance. The case presented aims to predict risks and ensure academic success and quality. The cross-industry standard process for data mining (CRISP-DM) methodology is used to structure and guide the prediction process. The study shows that using data from student learning processes within an academic success prediction model and data mining can identify at-risk students.

In this contribution, we present the BioS4You project and analyse the results obtained in the first 18 months of its activity. The “Bio-Inspired STEM topics for engaging young generations” (BioS4You) Erasmus+ KA2 Innovation project aims to bridge the gap between STEM national curricula (which include Science, Technology, Engineering, and Mathematics) and the needs of Z-generation students, uninterested to basic themes, but enthusiastic in issues related to environmental, social, and health concerns. The BioS4You project engages young learners in STEM subjects, starting with current issues of interest for them, as the social and environmental impact of new technologies, connecting STEM concepts to real-world technologies that are supporting on facing environmental, social, and health current challenges. Novel fields such as Bioengineering, Bioscience, Biotechnology can be implemented into classroom teaching, integrating academic disciplines, and stimulating the academic and social growth of young people. The knowledge of new STEM contents makes the students feel an active part of the technological innovation (and not just passive users) and help them to build a better future, bringing them closer to the STEM world and enabling them to make more informed choices for their future careers.

Background/Objectives: Seafarers are a particularly isolated workforce, as they are on-site only with their colleagues, both on and off duty. Long-term duties in the sea environment, irregular working hours, changing time zones, and lack of sleep are some of the many factors that negatively affect the physical and mental health of seafarers. This study aimed to explore the occupational stressors faced by seafarers in Lithuania using the HSE Management Standards Indicator Tool (HSE-MSIT). Methods: In 2023, a quantitative study, using a paper survey, of seafarers’ occupational stressors was conducted in Klaipeda city, Lithuania. A total of 385 Lithuanian seafarers participated in the study. Results: The greatest sources of stress at work were identified as changes at work, relationships, peer support, and management support. Role clarity, demands, and work control were not strong stressors for the seafarers. Analysis of perceived stress levels revealed notable differences between groups with different work experience and job positions. Analysis of years of service (0–11 years vs. 12+ years) found that seafarers with shorter service experienced more stress in terms of demands (p = 0.005), role clarity (p = 0.004), work control (p = 0.035), and relationships (p = 0.02). Based on job position (senior vs. junior), junior seafarers experienced significantly higher stress in the demands (p = 0.001) and role clarity (p = 0.009) subscales. The study revealed that job position and years of work had weak negative correlations with stress indicators at work. The Demands subscale was moderately positively correlated with relationships and change at work. The Relationships subscale was moderately positively correlated with peer support and change at work. Role clarity was moderately negatively correlated with change at work and relationships. Conclusions: Overall, changes at work, relationships, peer support, and management support in the working environment emerged as key factors of perceived stress among seafarers. Junior seafarers and seafarers with shorter years of service experienced higher stress at work. These findings may help in the development of targeted stress management and training strategies tailored to different profiles of seafarers.