Authors:
Diego Santos
1
;
2
;
Isabela Gasparini
3
and
José Moreira de Oliveira
1
Affiliations:
1
Instituto de Informática, PPGCC, Federal University of Rio Grande do Sul, Brazil
;
2
Federal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Sapiranga, RS, Brazil
;
3
Universidade do Estado de Santa Catarina: Joinville, Santa Catarina, Brazil
Keyword(s):
Ontologies, Blended Learning, Recommender Systems, Context-Aware Systems.
Abstract:
Blended learning environments are those that combine face-to-face instruction with computer-mediated instruction and have gained space in the means of discussion about new educational methodologies. Several benefits are observed in the use of this methodology, among them: an increase in academic performance and students’ social skills, an increase in teaching and learning flexibility, an increase in student satisfaction, à decrease in dropout rates, and an increase in school retention. Recommender systems are useful in these environments, providing the suggestion of content and activities personalized to users; here, we present a model for recommending learning activities in a blended learning environment. To evaluate the model, SWRL rules were used through the Pellet inference engine. The approach was evaluated through a case study that represents the situation of a student in a blended learning environment, with several options of activities, in which the choices may vary according
to their general and academic profiles, in addition to their context. The recommendation rules are executed, resulting in the activity suggestion for the student. Thus, it was verified that the developed model fulfills the proposed objective of enriching the recommendation of learning resources in a blended learning environment through the modeling of the learner’s profile and of the educational resources with context awareness through ontology.
(More)