Student Interaction

Research on student interaction focuses on understanding how students engage with learning materials and technologies, aiming to improve pedagogical approaches and personalize learning experiences. Current studies utilize various machine learning models, including transformers, LSTMs, and graph neural networks, to analyze large datasets of student interactions with intelligent tutoring systems, LLMs like ChatGPT, and online learning platforms. These analyses reveal valuable insights into student learning patterns, enabling the development of more effective adaptive learning systems and early identification of students at risk. This work has significant implications for improving educational outcomes and informing the design of more effective and equitable learning environments.