Learning Arithmetic

Research on arithmetic learning focuses on understanding and improving how humans and artificial systems acquire and perform arithmetic operations. Current efforts utilize various approaches, including transformer models, Bayesian Knowledge Tracing, and connectionist architectures, to investigate factors like data representation, training methodologies (e.g., chain-of-thought prompting), and the impact of noise and data quality on learning efficiency. These studies are significant for advancing our understanding of mathematical cognition and for developing effective educational tools and AI systems capable of supporting arithmetic learning in diverse contexts, including formative assessment and personalized tutoring.