CAERS: A Conversational Agent for Intervention in MOOCs' Learning Processes
| Autor: | Danielle Toti, Santi Caballé, Luigi Lomasto, Diego Rossi, Nicola Capuano, Mario A. R. Dantas, Regina Braga, Victor Ströele, Fernanda Campos |
|---|---|
| Přispěvatelé: | Federal University of Juiz de Fora, Universitat Oberta de Catalunya, Università degli Studi della Basilicata, Università degli studi di Salerno, Università Cattolica del Sacro Cuore |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Conversational agent
Process (engineering) Computer science Autonomous agent Ontology (information science) Recommender system computer.software_genre Task (project management) sistema de recomanació massive open online courses Mathematics education ComputingMilieux_COMPUTERSANDEDUCATION Massive open online courses sistema de recomendación Architecture Dialog system recommender system cursos en línia oberts massius Cursos en línia oberts i massius MOOCs (Web-based instruction) conversational agent Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI cursos en linea masivos en abierto cursos gratuitos online masivos Virtual learning environment agent de conversa computer agente conversacional |
| Zdroj: | Lecture Notes in Networks and Systems ISBN: 9783030906764 O2, repositorio institucional de la UOC Universitat Oberta de Catalunya (UOC) |
| Popis: | Massive Open Online Courses (MOOCs) make up a teaching modality that aims to reach a large number of students using Virtual Learning Environments. In these courses, the intervention of tutors and teachers is essential to support students in the teaching-learning process, answer questions about their content, and provide engagement for students. However, as these courses have a vast and diverse audience, tutors and teachers find it difficult to monitor them closely and efficiently with prompt interventions. This work proposes an architecture to favor the construction of knowledge for students, tutors, and teachers through autonomous interference and recommendations of educational resources. The architecture is based on a conversational agent and an educational recommendation system. For the training of predictive models and extraction of semantic information, ontology and logical rules were used, together with inference algorithms and machine learning techniques, which act on a dataset with messages exchanged between course forum participants in the humanities, medicine, and education fields. The messages are classified according to the type (question, answer, and opinion) and parameters about feeling, confusion, and urgency. The architecture can infer the moment in which a student needs help and, through a Conversational Recommendation System, provides the student with the opportunity to revise his or her knowledge on the subject. To help in this task, the architecture can provide educational resources via an autonomous agent, contributing to reducing the degree of confusion and urgency identified in the posts. Initial results indicate that integrating technologies and resources, complementing each other, can support the students and help them succeed in their educational training. |
| Databáze: | OpenAIRE |
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