BPEC: Collaborative Research: Creating Personalized Learning Pathways by Managing Cognitive Load

点击翻译按钮获取中文摘要

Washington University, in collaboration with the St. Louis Science Center will develop and evaluate a computer programing environment that uses personalized learning pathways to better engage and support young learners. In both formal and informal education settings, examples of solved problems are an important learning resource, enabling self-teaching, and supporting the inherent heterogeneity found in classrooms. Recent studies of programmers at varying experience levels revealed that selecting and adapting example code shared on the web is often used to support just in time learning and to access infrequently used techniques. However, while examples can be powerful learning tools, they must be well-matched to the learner's experience level and learning preferences. The project staff will develop computer algorithms that predict what future problems and examples will maximize learning for a specific learner.Research on learning via examples has demonstrated that by controlling the cognitive load for a given learner through the selection and presentation of examples and their related practice problems, it is possible to improve learner's success on near and far transfer tasks. This proposal hypothesizes that by carefully controlling the cognitive load, it will be possible to construct personalized learning pathways that help a learner to efficiently move from a novice understanding to mastery of a concept. This project aims to answer the following questions:1) Is it possible to predict the perceived cognitive load for a future problem, given a learner's history and use this to select an appropriate next problem for that learner? 2) What are the factors of a learner's history that are most predictive of the perceived cognitive load for a future problem?3) Is it possible to effectively use the predicted cognitive load to construct personalized learning pathways for an individual learner?