Abstract
The goal of smart education is to utilize advanced technology in order to improve the teaching experience by establishing a stimulating and interactive atmosphere for learning. Conversational agents emerge as an aid for a smarter education. One of the possibilities to be explored is the building of tools that help predict and prevent student failure or dropout. This case study presents a research project that consists on the creation of a school platform for student interaction, in which a conversational agent, developed using Rasa, communicates with both the students and the class director and is able to assign a risk of academic failure, based on their answers to questionnaires scripted by a team of psychologists. XGBoost outperfomed AdaBoost, Decision Tree and Random Forest algorithms with an accuracy of 97%.
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Acknowledgements
This work is supported by: FCT—Fundação para a Ciência e Tecnologia within the RD Units Project Scope: UIDB/00319/2020 and the Northern Regional Operational Programme (NORTE 2020), under Portugal 2020 within the scope of the project “Hello: Plataforma inteligente para o combate ao insucesso escolar”, Ref. NORTE-01-0247-FEDER-047004.
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Magalhães, R., Veloso, B., Marcondes, F.S., Lima, H., Durães, D., Novais, P. (2023). A Conversational Agent for Smart Schooling A Case Study on K-12 Dropout Risk Assessment. In: Castillo Ossa, L.F., Isaza, G., Cardona, Ó., Castrillón, O.D., Corchado Rodriguez, J.M., De la Prieta Pintado, F. (eds) Trends in Sustainable Smart Cities and Territories . SSCT 2023. Lecture Notes in Networks and Systems, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-031-36957-5_11
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