Design of a conversational recommender system in education

Abstract

In recent years, we have seen a significant proliferation of e-learning platforms. E-learning platforms allow teachers to create digital courses in a more effective and time-saving way, but several flaws hinder their actual success. One main problem is that teachers have difficulties finding and combining open-access learning materials that match their specific needs precisely when there are so many to choose from. This paper proposes a new strategy for creating digital courses that use learning objects (LOs) as primary elements. The idea consists of using an intelligent chatbot to assist teachers in their activities. Defined using RASA technology, the chatbot asks for information about the course the teacher has to create based on her/his profile and needs. It suggests the best LOs and how to combine them according to their prerequisites and outcomes. A chatbot-based recommendation system provides suggestions through BERT, a machine-learning model based on Transformers, to define the semantic similarity between the entered data and the LOs metadata. In addition, the chatbot also suggests how to combine the LOs into a final learning path. Finally, the paper presents some preliminary results about tests carried out by teachers in creating their digital courses.