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Mrs. A. Devi is an Assistant Professor in the Department of Computer Science and Engineering at Hyderabad Institute of Technology and Management, Hyderabad, Telangana, India. She is involved in teaching and academic activities in computer science and engineering. Her contribution focuses on computational techniques for student engagement analysis and academic performance prediction.
Student involvement and scholarly achievement are crucial metrics of educational efficacy, learning advancement, and student accomplishment. Conventional educational evaluation techniques predominantly depend on test scores, attendance logs, assignment marks, and instructor assessments, which may inadequately reflect the varied behavioural, cognitive, emotional, and environmental elements affecting student learning. This chapter explores the theoretical underpinnings, data types, computational frameworks, and forecasting techniques related to multimodal AI-driven analysis of student engagement and academic achievement. Recent advancements in educational foundational models, learning analytics, federated learning, and adaptive recommendation systems are regarded as promising avenues for research. The chapter culminates by suggesting a multimodal prediction model that amalgamates academic records, digital learning engagements, and student-produced learning evidence to facilitate prompt educational interventions, equitable learning prospects, and data-driven academic decision-making.