A distinctive characteristic of human learning is its capability to flexibly acquire a wide range of rich and complex forms of knowledge (e.g., first and second languages) and acquiring new and accumulated knowledge (e.g., learning physics is easier after having learned algebra). An adequate explanation of human learning must address how existing knowledge changes the way we learn so that we achieve knowledge goals and in specific contexts. This project aims to discover and specify how human learning processes operate differently under different contexts, depending upon what content is being learned. This research contributes to improved educational practices by specifying how learning processes are influenced by knowledge acquisition in a systematic and replicable way. This research will enhance our understanding of successful learning and optimal performance.
The project explores learning processes by contrasting learning from retrieval practice and learning from studying examples. The goal of this project is to resolve and clarify how these processes compete for cognitive resources, including attention and working memory, in ways that depend on the knowledge content to be learned. This research examines (1) the learning processes involved in learning from retrieval practice and from worked examples, (2) how these learning processes work differently when applied to different knowledge content, and (3) the computational mechanisms of learning that give rise to learning different content. The researchers use a combination of experiments in which the learning approach is varied along with the materials being studied and machine learning models. It will demonstrate knowledge-learning dependence by showing that one learning process (e.g., retrieval practice) produces better learning outcomes than another (e.g., example encoding) in some knowledge contexts but the reverse occurs in other knowledge contexts. By implementing the studies as part of a machine learning architecture, this research will provide computational evidence and theoretical insight into the hypothesized knowledge-learning dependence framework. The machine learning architecture developed may be used as an educational and research tool in learning sciences, and the project involves training new learning scientists.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
