ESL GENIE: AN INTERACTIVE, INTELLIGENT SYSTEM FOR SECOND LANGUAGE LEARNING AND TEACHING ON THE WEB

In a classroom setting, accommodating students with diverse backgrounds and levels of knowledge can be pedagogically challenging, especially for topics that have a knowledge space containing complex dependencies. Intelligent Tutoring Systems (ITS) attempt to address this challenge by providing computer-based learning environments that adapt to a student's level, facilitating self-paced and personalized learning trajectories. Traditionally, the knowledge domains of these systems are modeled explicitly, according to expert knowledge that may not always match the underlying learning dependencies of a novice, instead of taking full advantage of a corpus of user data in combination with machine learning techniques. In the field of second-language learning, this problem of knowledge domain mapping is exacerbated by the wide variety of factors influencing the structure of the underlying knowledge space, such as first language and previous language exposure. We are developing a web-based system for hybrid English as a Second Language (ESL) learning that takes a data-driven approach to the problem of domain mapping, assuming only very high-level organization of the domain knowledge, and having the actual structure of the student model be induced from a large corpus of student response data. We believe that a data-driven approach to student knowledge modeling, facilitated by the wide reach of free online learning systems and the explosive growth of internet access in developing countries, can drive a revolution in personalized learning.