This study investigates the effectiveness of machine learning and deep learning models for early prediction of student performance in higher education institutions. Using the Open University Learning Analytics (OULA) dataset, various models, including Decision Tree, Support Vector Machine, Neural Network, and Ensemble Model, were employed to predict student performance in three categories: Pass/Fail, Close to Fail, and Close to Pass. The Ensemble Model (EM) consistently outperformed other models, achieving the highest overall F1 measure, precision, recall, and accuracy. These results highlight the potential of data-driven techniques in informing educational stakeholders’ decision-making processes, enabling targeted interventions, and facilitating personalized learning strategies tailored to students’ needs. By identifying at-risk students early in the academic year, institutions can provide additional support to improve academic outcomes and retention rates. The study also discusses practical implications, including the development of pedagogical policies and guidelines based on early predictions, which can help educational institutions maintain strong academic outcomes and enhance their reputation for academic excellence. Future research aims to investigate the impact of individual activities on student performance and explore day-to-day student behaviors, enabling the creation of tailored pedagogical policies and guidelines.

Purpose: Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.Design/methodology/approach: Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.Findings: The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.Research limitations/implications: Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.Originality/value: This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This research paper examines the evolution of digital leadership as portrayed in The New York Times articles from 2020 to 2022, addressing the growing complexities and strategic roles influenced by digital technologies. The study identifies critical issues in digital leadership, including ethical dilemmas, cybersecurity threats, and the digital divide, which pose significant challenges to effective digital governance and strategy implementation. The objectives of this research were to uncover and analyze the prevailing themes and sentiments associated with digital leadership using advanced Natural Language Processing (NLP) and Artificial Intelligence (AI) tools, specifically Nvivo and Monkeylearn. Our methodology involved a content and sentiment analysis of 318 articles, chosen from an initial pool of 387, resulting in a dataset of 2,428 content blocks. This analysis enabled the identification of major themes: Digital Transformation, Innovation, Remote Working, and Cybersecurity. The results indicate that ‘Digital Transformation’ and ‘Innovation’ were the most prominent themes, with significant discussions around ‘technology adoption’ and ‘AI’. The sentiment analysis quantitatively underscores the public discourse, revealing that 60% of the analyzed content carried a positive tone, reflecting an optimistic view towards digital advancements. Conversely, 29% expressed negative sentiments, emphasizing the urgent need to address associated risks. Significantly, the statistical evaluation of theme prevalence and sentiment ratios provides a nuanced understanding of the digital leadership landscape, suggesting a critical balance is necessary between leveraging opportunities and mitigating risks. These findings contribute to the theoretical and practical discussions on digital leadership by highlighting the importance of strategic adaptability and ethical considerations in shaping policies that are both inclusive and effective. In conclusion, this study offers a detailed, numerical insight into the complexities of digital leadership, providing valuable evidence for policymakers and business leaders aiming to navigate the challenges and opportunities presented by digitalization.

Women's reproductive health has historically been marginalized within professional environments, regarded as a personal matter rather than a collective institutional issue, and predominantly excluded from organizational policies. Notwithstanding the direct influence of biological phenomena such as menstruation, maternity, and menopause on women's occupational experiences, these subjects have frequently been rendered obscured or deemed inappropriate for discourse in professional settings. This investigation scrutinizes public dialogs on professional social media platforms, particularly LinkedIn, to assess women's encounters with reproductive health and the intersectionality of these experiences with workplace policies. Employing a mixed‐methods framework, the research evaluates 52,592 comments on LinkedIn through both thematic and sentiment analyses. The outcomes reveal that women are increasingly participating in public discussions regarding menstruation, maternity, and menopause, and that such dialogs are significantly contributing to heightened awareness among employers. Drawing on social amplification theory, the study demonstrates how personal experiences shared online are reframed into collective narratives that challenge organizational norms. Simultaneously, feminist organizational theory provides insight into how these voices expose the structural exclusion of embodied female experiences from workplace design. Sentiment analysis indicates that a considerable proportion of these conversations arises from adverse experiences, thereby underscoring the inadequacy of institutional support for women's reproductive health. Concurrently, thematic analysis elucidates an escalating demand for policies including menstrual leave, flexible work arrangements following maternity, and support programs for menopause. The study positions these findings as indicators of a shifting organizational paradigm. Amplifying women's narratives emerges as a critical driver of policy reform, yet systemic change necessitates both cultural and institutional transformation.