International Journal of Education Sciences

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 present research gathered student data through two means: survey and record review.

Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern.

STEM education which brings together Science, Technology, Engineering, and Mathematics has become the foundation for success in today’s fast-changing, technology-driven world. It helps students think critically, solve real-world problems, and adapt to new innovations like artificial intelligence, robotics, data science, and biotechnology. In a country like India, where digital transformation is reshaping every sector, STEM education is not just an academic choice but a key to national growth and progress.

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.