A Chatbot for Automatic Processing of Learner Concerns in an Online Learning Platform

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 th...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2018, Vol.9 (5)
Main Authors: BAKOUAN, Mamadou, Hamidja, Beman, KONE, Tiemoman, OUMTANAGA, Souleymane, BABRI, Michel
Format: Article
Language:English
Subjects:
Chatbots
Domains
Experimentation
Keywords
Knowledge bases (artificial intelligence)
Online instruction
Questions
Similarity
Teachers
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Summary: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%.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2018.090521