The Center for Computational Language and Education Research (CLEAR) is dedicated to advancing Human Language Technology and applying it to Personalized Learning for broad and diverse populations with varying language backgrounds and cognitive profiles. Personalized Learning augments or replaces traditional modes of learning with emerging, and often interactive, technologies that adapt to suit individual preferences. Personalized learning is such an important problem that it has recently been named by the National Academy of Engineering as one of the 14 grand challenges for the 21st century.
CLEAR conducts research and development which informs theoretical questions in personalized learning and leads to effective and scalable solutions in schools, on the web and in the work place. Advancing personalized learning involves a multi-disciplinary effort that leverages innovations in human language technology that span computer science, linguistics, education, cognition, psychology, and speech and language.
Center projects include: Adaptive assessment and intervention for reading difficulties, The development of increasingly rich linguistic annotation schemes that can serve as training and evaluation data for machine learning, Information extraction and natural language understanding using semantic role labeling and co-reference resolution, Spoken language processing and dialog understanding, and Human-computer interaction using animated agents or customizable interfaces. These projects have led to a wide variety of systems including some for language acquisition skills, tutoring and therapy, tools for question answering and navigating the web, and for learning and presentation of science topics ranging from plate tectonics to acoustics.