CRII: Cyberlearning: Automatic Discovery of Optimal Progressions of Language Content

Cornell University, Ithaca NY

Investigators

Abstract

A key challenge in education is determining the optimal sequence of concepts to present to a student. This is particularly important for second language learning, where there is a huge amount of content to learn and it is difficult for learners to stay engaged. Although researchers in education and psychology have identified guiding principles for sequencing learning materials, there is a lack of pragmatic techniques that can apply these principles to build effective learning progressions automatically. To address this gap, this project will: (1) develop techniques for estimating the difficulty of language learning content, (2) identify important characteristics of various expert-crafted progressions, and (3) experiment to find optimal progressions for individual students. These progressions will enhance the effectiveness of online interactive learning tools that are freely accessible to the public. The anticipated discovery of general principles of pacing will benefit educators and provide the foundation for maximizing engagement in adaptive learning systems. The project will conduct three investigations in order to build adaptive learning progressions. First, the project will build a library of language materials that are organized by difficulty. To do this, the PI will explore procedural models of target linguistic knowledge such as grammar, identify which procedural steps are necessary for understanding specific materials, and organize these materials into skill trees. Second, the project will develop a set of control parameters that capture key aspects of pacing and sequencing. This will allow the PI to extract pacing strategies from expert-crafted progressions, and map these strategies onto other progressions. Third, the project will optimize progressions for individual learners by conducting large-scale multivariate experiments in online interactive learning tools.