Impact of Algorithms and Big Data on Educational Field

Abstract

Using the potential of big data in education is the best option for students and institutions to effectively anticipate student data in this competitive environment. By analyzing this data, educators can identify students at risk of academic underperformance and provide targeted interventions to improve their outcomes. Techniques such as clustering and classification are pivotal in segmenting students based on criteria like academic achievements, interests, and socio-economic backgrounds. This allows institutions to effectively tailor teaching methods and resources for diverse student needs, which also investigates the challenges associated with big data analytics and machine learning algorithms. Issues like data quality management, the complexity of integration from various sources, and resource allocation are explored. Proposed solutions include organizing advanced training programs, leveraging expert consultations, and employing robust data models to address these obstacles efficiently. The study conducts a comparative analysis of algorithms used in educational settings, particularly linear regression and logistic regression, to forecast student performance. Logistic regression emerged as more accurate, demonstrating its effectiveness in predicting grades and identifying dropout risks. The methodology used offline and online datasets from Oxford College of Engineering and Management, Gaindakot-2, Nawalparasi to analyze internal marks and predict semester outcomes, highlighting the transformative role of machine learning in education.

Algorithms and big data are revolutionizing education by enabling personalized learning, improving decision-making, and optimizing resource allocation. They help track student progress, predict outcomes, and identify areas needing attention. However, the reliance on data can also raise concerns about privacy, bias, and the dehumanization of educational experiences.