Adaptive and social mechanisms for automated improvement of eLearning materials

Abstract

Online environments introduce unprecedented scale for formal and informal learning communities. In these environments, user-contributed content enables social constructivist approaches to education. In particular, students can help each other by providing hints and suggestions on how to approach problems, by rating each other's suggestions, and by engaging in discussions about the questions. In addition, students can also learn through composing their own questions. Furthermore, with grounding in Item Response Theory, data mining and statistical student models can assess questions and hints for their quality and effectiveness. As a result, internet-scale learning environments allow us to move from simple, canned quizzing systems to a new model where automated, data-driven analysis continuously assesses and refines the quality of teaching material. Our poster describes a framework and prototype of an online drill-and-practice system that leverages user-contributed content and large-scale data to organically improve itself.