# Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS
## 1 Introduction  
Intelligent tutoring systems (ITSs) have widely been used in education and have become one of the most popular areas of research and development [1, 2]. One of the strengths of these systems is their ability to offer personalized learning, at any time, for learners from different backgrounds and with different profiles [3, 4, 5, 6]. To be able to offer this dynamic personalization, it is necessary to capture the learner's characteristics (knowledge, abilities, needs, etc.) [7, 8, 9, 10] which are commonly represented in a student/learner model [11, 12]. However, it is difficult to identify accurately these characteristics. More specifically, it cannot be accurately said that a student has learned a concept or not. In fact, in certain cases, students can accidentally give a correct answer (a guess), or inversely, they can give a wrong answer without having any flaw in the skill (a slip). Thus, the diagnosis process often deals with uncertainty [12, 13, 14].  
To take into account uncertainties, several research works use Artificial Intelligence (AI) techniques. They proved to be successful means to handle problems with incomplete and/or uncertain data [2, 11]. As far as adaptive e-learning systems are concerned, fuzzy logic and Bayesian networks are some of the most used techniques to assess and diagnose student knowledge and skills [14, 15]. However, their utility remains limited when it comes to combining information from different sources and capturing potential contradictions. In this work, we propose to use the theory of belief functions, also called Dempster-Shafer theory of evidence [16], which is a popular mathematical framework that deals with uncertain information. It is used in several domains such as decision making, risk and reliability evaluation, [17, 18] sensor data fusion, and aggregation operators [19]. In addition, to its ability to deal with uncertain and imprecise information, this framework provides a natural and powerful way for the expression and fusion of uncertain information from heterogeneous sources and handle the existing conflict between these sources [20].  
In this paper, we present a diagnosis module called Diag-Skills. This module is based on the pairing of a semantic model (domain ontology) and the theory of belief functions. Diag-Skills uses the learner's evaluation grades to determine the state of the evaluated skills and predicts the state of the non-evaluated ones. This diagnosis serves as a support to the personalization of the learning. Therefore, we raise the following research question: how to consider the uncertainty related to the evaluation grades to achieve a better diagnosis that will naturally lead to better-personalized learning?  
This paper is organized as follows. In Section 2, we list some related works. In Section 3, we expose the system's architecture in which Diag-Skills is integrated. In Section 4, we present Diag-Skills and explain how belief functions are used to model uncertainty. In Section 5, we describe the experiment realized to evaluate Diag-Skills. Finally, we discuss the experimental results and present our perspectives for this work in Section 6.