Improving Inquiry-Driven Modeling in Science Education through Interaction with Intelligent Tutoring Agents

Abstract

This paper presents the design and evaluation of a set of intelligent tutoring agents constructed to teach teams of students an authentic process of inquiry-driven modeling. The paper first presents the theoretical grounding for inquiry-driven modeling as both a teaching strategy and a learning goal, and then presents the need for guided instruction to improve learning of this skill. However, guided instruction is difficulty to provide in a one-to-many classroom environment, and thus, this paper makes the case that interaction with a metacognitive tutoring system can help students acquire the skill. The paper then describes the design of an exploratory learning environment, the Modeling and Inquiry Learning Application (MILA), and an accompanying set of metacognitive tutors (MILA-T). These tools were used in a controlled experiment with 84 teams (237 total students) in which some teams received and interacted with the tutoring system while other teams did not. The effect of this experiment on teams' demonstration of inquiry-driven modeling are presented.

Using AI to Teach AI: Lessons from an Online AI Class

“…In addition, building on our prior work on intelligent tutoring systems (Joyner and Goel 2015a, 2015b), we created about 100 “nanotutors” to support the exercises and embedded them in the video lessons. The nanotutors are highly focused intelligent tutoring agents guiding students' understanding of one narrowly defined skill such as completing a semantic network for a particular problem or simulating an agent's planning in the blocks world.…”