Intelligent education has become an emerging multi-disciplinary research field that exploits data mining (DM) techniques to understand the activities, behaviors, and performance in education and develop AI-based tools to help instructors teach better, students learn better, and educators manage better. Many studies on AIBDE have already been conducted to analyze and mine the patterns and the associations from the educational big-data (EBD), showing that DM and AI are powerful for promoting and shaping educational routes. In recent years, more and more AI tools have been going into classrooms, schools, and online courses.
However, education is a fused environment, including humans, machines, materials, and interactors. Thus, the EBD is collected from multiple sources, modes, and periods, incurring many challenges in AI and DM. For example, extreme sparsity makes the current DM methods ineffective and leads to degraded performance. The spatial-temporal behaviors, the noisy label, and the data privacy are also hard points in AIBDE. On the other hand, AI techniques, e.g., ChatGPT, are making more reformations in education from many teaching-learning activities. Friendly theories still need to be developed to support AI in education, while it is urgent to create DM approaches to understanding techniques.
The 1st workshop at ICDE: DMLS2022 https://vpomelo.github.io/dmls2022/index.html