Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

TL;DR

This paper presents an ontology-based fuzzy markup language agent that facilitates co-learning between students and robots, using machine learning to adapt to individual learning abilities and improve educational outcomes.

Contribution

It introduces a novel FML-based robot agent that integrates domain ontology, machine learning, and fuzzy logic for personalized co-learning in classroom settings.

Findings

Robot-assisted learning benefits disadvantaged children.

FML-based agent accuracy improves with machine learning.

The system provides real-time feedback on student progress.

Abstract

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.