Digital Methods Of Assessing Students’ Learning In Education Using Machine Learning

Abstract

In contemporary education, the integration of digital methods and machine learning algorithms for assessing students' learning has gained significant traction. This research paper investigates the application of machine learning techniques and digital assessment tools to enhance the evaluation process in educational contexts. By examining various methodologies and approaches, the potential benefits, challenges, and future directions of employing digital methods for assessing student learning outcomes. It discusses the advantages of machine learning-based assessment systems, including scalability, personalized feedback, and adaptive learning pathways, while also addressing concerns related to data privacy, algorithmic bias, and result interpretation. Additionally, the paper explores the integration of digital assessment tools into existing educational frameworks and assesses their impact on teaching practices and student engagement. Through a review of relevant literature and case studies, this paper highlights emerging trends and best practices in the field of digital assessment in education.