Machine-Assisted Teaching for Open-Ended Problem Solving: Foundations and Applications

Descripción del proyecto

Enseñanza asistida por máquinas para la resolución de problemas

La lectura, la escritura y la tecnología digital serán los pilares de la educación en el futuro. Las necesidades de una sociedad cada vez más digitalizada nos obligan a garantizar que los estudiantes del mañana estén preparados para prosperar en los campos de la ciencia, la tecnología, la ingeniería, las matemáticas (CTIM) y la informática. Sin embargo, enseñar dominios abiertos como la programación informática o la resolución de rompecabezas conceptuales es todo un reto. En este contexto, el equipo del proyecto TOPS, financiado con fondos europeos, propone un nuevo marco computacional que modela al alumno como un agente de aprendizaje por refuerzo que aprende con la ayuda de un profesor automatizado. Esta tecnología de última generación para la enseñanza asistida por máquinas en dominios abiertos está diseñada para fomentar el pensamiento computacional y la capacidad de resolución de problemas.

Objetivo

Computational thinking and problem solving skills are essential for everyone in the 21st century, both for students to excel in STEM+Computing fields and for adults to thrive in the digital economy. Consequently, educators are putting increasing emphasis on pedagogical tasks in open-ended domains such as programming, conceptual puzzles, and virtual reality environments. When learning to solve such open-ended tasks by themselves, people often struggle. The difficulties are embodied in the very nature of tasks being open-ended: (a) underspecified (multiple solutions of variable quality), (b) conceptual (no well-defined procedure), (c) sequential (series of interdependent steps needed), and (d) exploratory (multiple pathways to reach a solution). These struggling learners can benefit from individualized assistance, for instance, by receiving personalized curriculum across tasks or feedback within a task. Unfortunately, human tutoring resources are scarce, and receiving individualized human-assistance is rather a privilege. Technology empowered by artificial intelligence has the potential to tackle this scarcity challenge by providing scalable and automated machine-assisted teaching. However, the state-of-the-art technology is limited: it is designed for well-defined procedural learning, but not for open-ended conceptual problem solving. The TOPS project will develop next-generation technology for machine-assisted teaching in open-ended domains. We will design novel algorithms for assisting the learner by bridging reinforcement learning, imitation learning, cognitive science, and symbolic reasoning. Our theoretical foundations will be based on a computational framework that models the learner as a reinforcement learning agent who gains mastery with the assistance of an automated teacher. In addition to providing solid foundations, we will demonstrate the performance of our techniques in a wide range of pedagogical applications.