Solving the Individualized Instructional Content Delivery Problem Using Ontology and Metaheuristics on the Set Covering Problem: An Experimental Analysis

  • Clarivando F. Belizário Júnior UFU
  • Fabiano A. Dorça UFU
  • Luciana Assis UFVJM
  • Alessandro Vivas UFVJM


Intelligent Tutoring Systems (ITSs) based on a step-by-step problem-solving approach are limited in terms of compatible content. On the other hand, recommendation systems can suggest various content types but lack the granularity of concepts found in step-by-step approaches. This study addresses this challenge by proposing a method to recommend instructional content from diverse knowledge domains while incorporating the refined concepts of ITSs. To tackle this issue, the instructional content delivery problem (LORP) is formulated as a set covering problem, classified as NP-hard. We show that a PSO-based algorithm is a good candidate to solve LORP in a better runtime than the exact algorithm and with better solutions than the greedy heuristic. By leveraging collaborative filtering and an ontology that models students’ knowledge, learning styles, and search parameters, the approach offers more individualized content.


Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6):734–749.

Balaji, S. and Revathi, N. (2016). A new approach for solving set covering problem using jumping particle swarm optimization method. Nat. Comput., 15(3):503–517.

Belizário Júnior, C. F. and Dorça, F. (2018). Uma abordagem para a criaçao e recomendaçao de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. In Brazilian Symposium on Computers in Education, volume 29, pages 1533–1542, Fortaleza, CE. SBC.

Belizário Júnior, C. F., Dorça, F., Andrade, A. V., and Assis, L. P. (2020). Avanços na recomendação personalizada de objetos de aprendizagem através da utilização de meta-heurísticas clássicas associadas aos problemas de cobertura de conjuntos e de máxima cobertura: Uma análise experimental. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 31, pages 1383–1392, Natal, RN. SBC.

Belizário Júnior, C. F., Dorça, F., Assis, L. P., and Andrade, A. V. i. p. (2023). Solving the personalised learning objects recommendation problem using ontology and metaheuristics on the set covering problem: An experimental analysis. International Journal of Learning Technology, 18.

Bernacki, M. L., Aleven, V., and Nokes-Malach, T. J. (2014). Stability and change in adolescents’ task-specific achievement goals and implications for learning mathematics with intelligent tutors. Computers in Human behavior, 37:73–80.

Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web. Scientific american, 284(5):34–43.

Christudas, B. C. L., Kirubakaran, E., and Thangaiah, P. R. J. (2018). An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials. Tel. and Inf., 35(3):520–533.

CLEOLab (2003). Cleo extensions to the ieee learning object metadata. CLEO Collaborative Partners Cisco Systems, Inc., IBM Corporation, Microsoft Corporation, Thomson NETg. CLEO White Paper.

De Medio, C., Limongelli, C., Sciarrone, F., and Temperini, M. (2020). Moodlerec: A recommendation system for creating courses using the moodle e-learning platform. Computers in Human Behavior, 104:106168.

Deborah, L. J., Baskaran, R., and Kannan, A. (2014). Learning styles assessment and theoretical origin in an e-learning scenario: a survey. AI Review, 42(4):801–819.

Falci, S. H., Dorça, F. A., Falci, D. H. M., and Vivas, A. (2019). A low complexity heuristic to solve a learning objects recommendation problem. In 2019 IEEE 19th ICALT, volume 19, pages 49–53. IEEE.

Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.

Garey, M. R. and Johnson, D. S. (1979). Computers and intractability. Series of books in the mathematical sciences. W. H. Freeman & Co., New York.

Golab, L., Korn, F., Li, F., Saha, B., and Srivastava, D. (2015). Size-constrained weighted set cover. In 31st Int. Conf. on Data Engineering, pages 879–890. IEEE.

Graf, S., Kinshuk, and Ives, C. (2010). A flexible mechanism for providing adaptivity based on learning styles in lms. In 10th ICALT, pages 30–34. IEEE.

Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2):199–220.

Horrocks, I., Patel-Schneider, P. F., Boley, H., Tabet, S., Grosof, B., Dean, M., et al. (2004). Swrl: A semantic web rule language combining owl and ruleml. W3C Member submission, 21(79):1–31.

Limongelli, C., Gasparetti, F., and Sciarrone, F. (2015). Wiki course builder: a system for retrieving and sequencing didactic materials from wikipedia. In 2015 Inter. Conf. on Inform. Tech. Based Higher Education and Training, pages 1–6. IEEE.

LTSC (2002). Standard for learning object metadata (ieee 1484.12.1). Learning Technology Standards Committee.

Mitchell, S., OSullivan, M., and Dunning, I. (2011). Pulp: a linear programming toolkit for python. The University of Auckland, Auckland, New Zealand, 65.

Ouf, S., Ellatif, M. A., Salama, S. E., and Helmy, Y. (2017). A proposed paradigm for smart learning environment based on semantic web. Computers in Human Behavior, 72:796–818.

Pereira, Júnior, C., Belizário, Júnior, C. F., Araújo, R. D., and Dorça, F. A. (2020). Personalized recommendation of learning objects through bio-inspired algorithms and semantic web technologies: an experimental analysis. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 1333–1342. SBC.

Pereira, C. K., Campos, F., Ströele, V., David, J. M. N., and Braga, R. (2018). Broad-rsi–educational recommender system using social networks interactions and linked data. Journal of Internet Services and Applications, 9(7):1–28.

Soloman, B. A. and Felder, R. M. (2005). Index of learning styles questionnaire. NC State University. Available online at: (last visited on 12.05.2023), 70.

Soofi, A. A. and Ahmed, M. U. (2019). A systematic review of domains, techniques, delivery modes and validation methods for intelligent tutoring systems. International Journal of Advanced Computer Science and Applications, 10:99–107.

Tarus, J. K., Niu, Z., and Kalui, D. (2018). A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Computing, 22(8):2449–2461.

Tarus, J. K., Niu, Z., and Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72:37–48.

Tilahun, S. L. and Ong, H. C. (2015). Prey-predator algorithm: a new metaheuristic algorithm for optimization problems. International Journal of Information Technology & Decision Making, 14(06):1331–1352.

Tukey, J. W. (1949). Comparing individual means in the analysis of variance. Biometrics, 5(2):99–114.

Vanetti, M., Binaghi, E., Carminati, B., Carullo, M., and Ferrari, E. (2010). Content-based filtering in on-line social networks. In International Workshop on Privacy and Security Issues in Data Mining and Machine Learning, pages 127–140. Springer.

VanLehn, K. (2006). The behavior of tutoring systems. International journal of artificial intelligence in education, 16(3):227–265.

Zhao, X., Niu, Z., Chen, W., Shi, C., Niu, K., and Liu, D. (2015). A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. Journal of Intelligent Information Systems, 44(3):335–353.
Como Citar

Selecione um Formato
BELIZÁRIO JÚNIOR, Clarivando F.; DORÇA, Fabiano A.; ASSIS, Luciana; VIVAS, Alessandro. Solving the Individualized Instructional Content Delivery Problem Using Ontology and Metaheuristics on the Set Covering Problem: An Experimental Analysis. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1202-1214. DOI: