Mood detection ontology integration with teacher context - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

Mood detection ontology integration with teacher context

Résumé

Recommender systems in education improve the teacher's working process by providing relevant resources to aid his course design in addition to learning new teaching methodologies. However, these systems have limited adaptability according to a global evaluation of teacher's activities. This approach of user profiling is convenient, but not adequate for teacher's context description. In our approach, it is assumed that the utilization of teacher's emotions has an inevitable role to accomplish a full contextual description for teacher. Teacher context ontology (TCO) provides a representation for the teacher's living and working contexts along with the main educational concepts. In this paper, we introduce a conceptual integration approach between Moodflow@doubleYou emotional data as a concept and TCO ontology. Furthermore, we intend to prove the importance of integrating such concept for sufficient teacher's context description. The impact of utilization emotional data in educational recommender systems is discussed. Finally, this paper represents the conducted experiments' results which show the advantage of such integration.
Fichier principal
Vignette du fichier
ICMLA2021_Mood detection ontology integration with teacher context.pdf (372.9 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03537277 , version 1 (20-01-2022)

Identifiants

Citer

Nader Nashed, Christine Lahoud, Marie-Hélène Abel, Frédéric Andrès, Bernard Blancan. Mood detection ontology integration with teacher context. 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021), 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021, Dec 2021, Pasadena, CA, United States. pp.1710-1715, ⟨10.1109/ICMLA52953.2021.00272⟩. ⟨hal-03537277⟩
69 Consultations
126 Téléchargements

Altmetric

Partager

More