International Journal of Education Sciences

In the modern information-driven academic world, identifying student employability and placement preparedness has predicted. be made a part and parcel of academic planning and career. development. This study provides a machine learning-based. structure to evaluate and forecast student placement pre-paredness by combining various performance aspects-academic achieve- ment, coding activity, aptitude and behavioral engage-ment metrics. Multi-source was gathered and preprocessed in the study. student information, such as student records (CGPA, attendance), logging time spent and difficulty of the problem solved, and indicators of co-curricular involvement. Advanced preprocessing data generation, feature engineering and tech-niques.

The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as a predictor of future performance in school by utilizing computer technology. Peer pressure is an influential factor and impacts academic achievement, and therefore this study will focus on the relationship between peer pressure and academics through machine learning methods, resulting in predictive assessments for students similar to this population.

The rapid advancement of Information and Communication Technology (ICT) has revolutionized digital pedagogies, reshaping modern education by improving accessibility, learner engagement, and academic outcomes. This review critically explores the psychological ramifications of digital learning environments while assessing the effectiveness of ICT tools in fostering inclusive and sustainable education. By integrating insights from contemporary research, this paper examines the impact of digital pedagogies on cognitive function, emotional health, and social interactions among students.

The integration of predictive analytics into higher education has significantly transformed institutional decision-making processes. However, prevailing implementations remain predominantly performance-centered, focusing on dropout prediction and grade forecasting rather than holistic developmental outcomes. Concurrently, higher education systems worldwide are confronting escalating concerns regarding student mental health, disengagement, career uncertainty, and labor market volatility. These intersecting challenges necessitate a broader theoretical reconceptualization of predictive analytics—one that integrates psychological well-being and long-term occupational success as central educational outcomes.

E-learning has increasingly established itself as a vital mode of education in the digital era, where information is widely accessible. This study provides an analytical perspective on the effectiveness of e-learning practices across diverse academic and professional settings. Drawing upon a broad body of literature, the paper examines how e-learning influences learner engagement, knowledge retention, skill development, and overall academic performance. Key factors shaping its effectiveness—such as instructional design, technological infrastructure, learner characteristics, and teaching strategies—are evaluated in detail.

The very fast adoption of Artificial Intelligence (AI) and Machine Learning (ML) in education has transformed contemporary teaching and learning ecosystems driven by advances in immersive technologies and the growing engagement of global technology leaders with virtual environments. AI-powered educational platforms enable adaptive and personalized learning pathways by dynamically adjusting content, pace and instructional strategies to learners’ preferences, abilities and learning styles by improving engagement, retention and academic outcomes. Deep learning models and predictive analytics are further reshaping pedagogical practices by supporting data-driven decision making, early identification of learning difficulties, and targeted interventions while simultaneously raising critical concerns related to ethics, data privacy, equity and long-term sustainability.