Using Open Source Technologies and Generalizable Procedures in Conversational and Affective Intelligent Tutoring Systems

Abstract

In the last years, the educational field has been influenced by technological advances. The digital transformation in educational environments allows the incorporation of virtual teaching-learning environments, which allow or facilitate learning opportunities for students, showing, for example, where they make mistakes and providing personalized help whenever they require it. In addition, these systems provide permanent access availability whenever it is possible to access the Internet. Traditionally, simultaneously many students learn word problem-solving skills in the classroom through instruction from only one educational professional. The Intelligent Tutoring System (ITS) Hypergraph Based Problem Solver (HBPS) is capable of tutoring the whole process of solving arithmetic-algebraic word problems, in a personalized way and without imposing any restrictions on the resolution path. Nevertheless, the student-system interaction is performed through a traditional interface by selecting items from a drop-down menu and clicking on buttons. Since dialogue is the fundamental communication mechanism for human-human, we propose use a framework to improve the interaction of the HBPS using a conversational user interface that allows performing the same actions more easily using natural language as the main means of interaction. My thesis research focuses on two main topics. The first one is related to the incorporation of a conversational agent using an open source machine learning framework that is fully configurable. The second one in concerned with testing and modifying different neural architectures to improve performance in intent classification and entity extraction, in such a way that it can be exported to other mathematical domains.