(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 5, 2018 168 | Page www.ijacsa.thesai.org A Chatbot for Automatic Processing of Learner Concerns in an Online Learning Platform Mamadou BAKOUAN 1 Laboratoire de Recherche en Informatique et Télécommunication (LARIT) Ecole Doctorale Polytechnique de l’INP-HB Yamoussoukro, Côte d’Ivoire Beman Hamidja KAMAGATE 2 Laboratoire de Recherche en Informatique et Télécommunication (LARIT) Ecole Supérieure Africaine des TIC - ESATIC Abidjan, Côte d’Ivoire Tiemoman KONE 3 Laboratoire d'Informatique, Signaux et Télécommunications Institut de Recherche Mathématiques IRMA/UFHB Abidjan, Côte d’Ivoire Souleymane OUMTANAGA 4 , Michel BABRI 5 Laboratoire de Recherche en Informatique et Télécommunication (LARIT) INP-HB Yamoussoukro, Côte d’Ivoire Abstract—In this article, we present a chatbot model that can automatically respond to learners’ concerns on an online training platform. The proposed chatbot model is based on an adaptation of the similarity of Dice to understand the concerns of learners. The first phase of this approach allows selecting the pre- established concerns that the teacher has in a knowledge base which are closest to those posed by the learner. The second phase consists of selecting among these k most appropriate concerns based on a measure of similarity built on the concept of domain keywords. The experimentation of the prototype of this chatbot makes it possible to find the adequate answers. In the case, where the question refers to a question from the teacher, the learner is asked if the question identified is the one he was referring to. If he answers in the affirmative, the instructions associated with his request are sent to him. If not, the learner’s concern is sent to the human tutor. The hybridization of this chatbot with the human agent comes to enrich the initial knowledge base of the chatbot. The results obtained with the concept based on the keywords of the domain are encouraging. The learner’s comprehension rate is above 50% when applying the concept of domain keywords while the measure of Dice is below 50%. Keywords—Metadata; ontologies; semantic similarity; natural language; semantic web; chatbot I. INTRODUCTION Chatbot are interactive virtual characters whose mission is to provide assistance to people in high-profile environments. Previous research has shown that this technology seems to have a positive influence on learning [1]. In addition, the presence of interactive virtual agents, also called Chatbot, taking on the role of guardian [2], seems to have positive effects on student engagement [3] and on the effectiveness of teaching [4]. In the education system in Côte d'Ivoire, the number of graduates is growing steadily, without a corresponding increase in the capacity of higher education institutions [5]. To face this situation, the government has opted for the integration of new technologies (ICT) in education through the interconnection of universities and public schools in Côte d'Ivoire [6]. This project should make it possible to unclog university lecture halls by relying on distance learning and facilitate access to teaching resources. However, since 2015 the infrastructures of the e-Education project are not operational. In this dynamic, the State uses e-learning through the creation of Université Virtuelle de Côte d'Ivoire (UVCI) [7]. One of UVCI’s missions is to develop distance education in Côte d'Ivoire. This type of teaching is based on a set of platforms to facilitate access to learning resources for learners. In the pedagogical model of the UVCI, the human tutor plays the role of framer. It ensures the educational follow-up of the training. However, the response time of the physical tutor is low and the high number of students per physical tutor degrades the quality of the training. This sometimes gives rise to the feeling of abandonment in some students. To remedy this, we offer a chatbot that helps to take care of students’ concerns on a permanent basis. It is about lightening the task of teachers and tutors while contributing to the framing and effective management of student concerns. In the next section, we will describe the role of metadata and ontologies in how chatbot work. Then we will discuss the mechanism used by the chatbot to understand the sentences. Finally, we will see the experimentation of the prototype of the chatbot and the results. II. LITERATURE REVIEW Information systems have to evolve with certainty, their agility is a major requirement. Software architectures must therefore promote real flexibility and reusability to adapt to change. New software architectures have brought a real ability of an architecture to evolve in order to integrate some changes response to the complex need of integration of information systems. It is particularly in this context that the new generation of formal metadata system technologies and the semantic web, derived from the Service-Oriented Architectures paradigm, aims to respond in a relevant way to the question of interoperability related to the agility of chatbot systems.