Learning Analytics
Learning analytics leverages data to understand and improve educational processes, aiming to personalize learning and enhance student outcomes. Current research emphasizes using machine learning, including large language models and various regression techniques, to analyze diverse data sources—from student interactions with AI tutors to learning management system records—to identify at-risk students, predict performance, and optimize teaching strategies. This field is significant for its potential to improve educational practices through data-driven insights, fostering more effective and equitable learning environments, and informing the development of more transparent and interpretable AI-powered educational tools.
Papers
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
Shiqi Liu, Sannyuya Liu, Lele Sha, Zijie Zeng, Dragan Gasevic, Zhi Liu