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Advanced Machine Learning for Smart Pedagogy and Outcome-based Assessment in Higher Education

Year: 2025
Type: conference-paper
Abstract: Technology is rapidly transforming higher education, making it essential to adopt new teaching and assessment methods.Traditional teaching approaches often struggle to meet students' diverse learning needs and ensure effective learning outcomes.Machine learning (ML) offers a promising solution by enabling adaptive, personalized, and data-driven learning experiences.ML-powered educational systems analyzed large amounts of student data to identify learning patterns, predict performance, and provide tailored support.This helps educators create customized learning paths, improving student engagement and academic success.Outcome-based assessment (OBA), which evaluates students based on predefined learning goals, benefits from ML by automating grading, offering real-time feedback, and detecting knowledge gaps.However, implementing ML in education comes with challenges, including data privacy concerns, bias in algorithms, and the need for proper faculty training.Despite these issues, ML has the potential to revolutionize higher education by making learning more personalized, efficient, and fair.This research explores how advanced ML techniques such as deep learning, natural language processing (NLP), and predictive analytics can enhance teaching and assessment.It also examines best practices for integrating ML into education while addressing ethical concerns.By proposing a scalable and transparent ML framework, this study aims to support a more effective and student focused approach to higher education (Ersozlu et al., 2024).
DOI: 10.24874/qf.25.100
Open Access status: gold
OpenAlex ID: W7116843435
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