Adopting Good-Learners' Paths in an Intelligent Tutoring System
Selly Meliana, Dade Nurjanah · 2018
This paper proposes an approach to following good-learners' paths in Intelligent Tutoring Systems (ITS). Based on a theory which states that learning can be acquired through imitation of competent models, we observed and analyzed the exercises completed by good learners to predict an appropriate sequence of questions for the students to follow. We applied the Hidden Markov Model to represent students' skills and the generative Markov Decision Process to model the exercise experience of good learners. The resulting model represents recommendations of dynamic sequences of questions. A preliminary study has been conducted to elicit a real-world dataset of a programming course. It aims to measure the reliability of the proposed approach, in comparison with the conventional approach where teachers determined the questions. The approach performance is indicated by increased percentages of good learners. The experiment results show that the proposed approach has a better reliability than the conventional approach.