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This book explores the measurement of learning effectiveness and the optimization of knowledge retention by modeling the learning process and building the mathematical foundation of multi-space learning theory. Multi-space learning is defined in this book as a micro-process of human learning that can take place in more than one space, with the goal of effective learning and knowledge retention. This book models the learning process as a temporal sequence of concept learning, drawing on established principles and empirical evidence. It also introduces the matroid to strengthen the mathematical foundation of multi-space learning theory and applies the theory to vocabulary and mathematics learning, respectively. The results show that, for vocabulary learning, the method can be used to estimate the effectiveness of a single learning strategy, to detect the mutual interference that might exist between learning strategies, and to predict the optimal combination of strategies. In mathematical learning, it was found that timing is crucial in both first learning and second learning in scheduling optimization to maximize the intersection effective interval. The title will be of interest to researchers and students in a wide range of areas, including educational technology, learning sciences, mathematical applications, and mathematical psychology.
Tai Wang is a professor affiliated with the Faculty of Artificial Intelligence in Education at the Central China Normal University, China. His research interests include educational technologies, internet psychology, and natural language processing. One major topic of his research is learning environment constructions. Mengsiying Li is a PhD candidate at the National Engineering Research Center for E-Learning, Central China Normal University, China, whose research focuses on learning spaces, strategies, and behavior.
1. Introduction on Multi-Space Learning 2. Partition Spaces to Optimize Learning Effectiveness 3. Matroid Theory 4. Current Foundations of Learning Sciences 5. Applications in Vocabulary Learning 6. Applications in Math Learning 7. Summary
