SIMPLIFY ITS An Intelligent Tutoring System based on Cognitive Diagnosis Models and spaced learning

Martínez Villanueva, Nora; Estévez-Costas, Andrés; Fernández-Hermida, David; Canas-Rodríguez, Agustin

Abstract:

In this paper we introduce Simplify ITS, a novel approach to an adaptive and Intelligent Tutoring System (ITS) aimed to support teachers in enhancing the student know ledge and skills. Unlike other approaches, our proposal does not intend to replace the teachers' role, but rather provides teachers with actionable information to make decisions about how to adapt the learning experience to the student needs. Also, it addresses two of the more important challenges in Learning Analytics solutions: building robust connections to the learning sciences and focusing on the student. In our approach, a student's skill profile is automatically estimated from the student's actions using Cognitive Diagnosis Models. Then, Simplify ITS uses the information contained in student profile to recommend activities during the learning process but also, it provides reviews and practices of those concepts that have been learned previously.

Year:

2018

Type of Publication:

Proceedings

Keywords:

learning analytics; adaptive and intelligent tutor system; cognitive diagnosis model; interleaving - spacing effect; experience API; ITEM RESPONSE THEORY; DINA MODEL; BAYESIAN NETWORKS; CHALLENGES; RETRIEVAL; PACKAGE; TESTS

Editor:

IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education

Number:

1703-1712

Month:

April