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

Projektbeschreibung

Maschinengestützter Unterricht in Problemlösungskompetenz

Lesen, Schreiben und digitale Technologie werden die zukünftigen Säulen der Bildung sein. Angesichts der Anforderungen einer zunehmend digitalisierten Gesellschaft müssen wir dafür sorgen, dass die Lernenden von morgen in den Bereichen Mathematik, Informatik, Naturwissenschaften, Technik (MINT) und Informationsverarbeitung erfolgreich sind. Der Unterricht in offenen Bereichen wie der Computerprogrammierung oder dem Lösen von konzeptionellen Rätseln stellt jedoch eine Herausforderung dar. In diesem Zusammenhang schlägt das Team des EU-finanzierten Projekts TOPS einen neuen Berechnungsrahmen vor, der den Lernenden als Agenten des verstärkenden Lernens modelliert, der mithilfe einer automatisierten Lehrkraft lernt. Diese Technologie der nächsten Generation für den maschinengestützten Unterricht in offenen Bereichen ist dafür konzipiert, das rechnerische Denken und die Problemlösungskompetenz zu fördern.

Ziel

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.