An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning

Highlights • We design and develop an intelligent tutoring system to encourage students to learn through experimentation. • We allow to work at higher levels of Bloom’s Taxonomy with the designed intelligent tutoring system. • We use artificial intelligence and fuzzy rules to design and develop the learning instructor. • We establish foundations to build intelligent tutoring system that allow to learn through experimentation.
The way in which people learn and institutions teach is changing due to the ever-increasing impact of technology. People access the Internet anywhere, anytime and request online training. This has brought about the creation of numerous online learning platforms which offer comprehensive and effective educational solutions which are 100% online. These platforms benefit from intelligent tutoring systems that help and guide students through the learning process, emulating the behavior of a human tutor. However, these systems give the student little freedom to experiment with the knowledge of the subject, that is, they do not allow him/her to propose and carry out tasks on his/her own initiative. They are very restricted systems in term of what the student can do, as the tasks are defined in advance. An intelligent tutoring system is proposed in this paper to encourage students to learn through experimentation, proposing tasks on their own initiative, which involves putting into use all the skills, abilities tools and knowledge needed to successfully solve them. This system has been designed developed and applied for learning predictive parsing techniques and has been used by Computer Science students during four academic courses to evaluate its suitability for improving the student’s learning process.