This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Over the past decade, new and exciting technologies have created opportunities for developing rich open-ended learning environments that combine a number of different learning paradigms and resources. Students can complete quests in game environments, engage in inquiry, interact with virtual agents, run science simulations, take quizzes, access the web, and more generally make choices about different learning activities. These choices can be extremely informative about student learning and diagnostic of what students can learn once they leave highly scripted curricula. By employing machine learning methods, such as hidden Markov Models and other sequence analysis algorithms to analyze student choices in relatively open learning environments and determine whether students are showing (sub) optimal behavior patterns, the environment can then adapt intelligently by encouraging (alternative) choices and better learning behaviors. The primary hypothesis is that helping students develop the metacognitive abilities to make learning choices will have strong effects on their subsequent abilities to learn in the future in unstructured and unsupervised but resource-rich environments. The goals for this project are to create: a) Choice adaptive intelligent learning environments and computational methodologies that help students develop strategies to enable them to learn on their own; b) Novel automated assessment tools for both teachers and students that link choice and learning behaviors to learning performance; and c) Research studies that will establish whether our interventions that combine choice with guidance is beneficial for both strong and weak learners in science domains.
The broader impacts of this work span multiple dimensions. First, it provides an encompassing computer science framework for bringing together a number of technology-rich, interactive environments that are proliferating for education into a common choice filled and adaptive architecture. Second, this choice-based framework provides a paradigm shift in that tracking and theorizing about choice is applied in the context of learning, which, in the past, has been dominated by characterizations of the knowledge construct. Characterizing learning by choice not only connects learning research to a larger body of social science research, it is also a fundamentally new way to characterize and guide learning that is closer to the goal of much instruction, namely intelligent future choice. Third, the computer environment should permit the collection and analysis of large log files by many researchers, and conceivably lead to a new database of common choice patterns and their effects on learning. We will create the framework that enables others to incorporate intelligence into their virtual worlds and help achieve these proposed outcomes.
