Personalized Recommendation of Learning Objects Through Bio-inspired Algorithms and Semantic Web Technologies: an Experimental Analysis
Resumo
The emerging need to explore the Web as a learning source allied with the purpose of providing personalized recommendations is a tough task. Considering this scenario, this work presents an approach that combines Semantic Web technologies and bio-inspired algorithms to perform personalized recommendation of Learning Objects (LOs) using local repositories and Web resources. Web resources are retrieved and structured as LOs, which allows for the automatic generation of metadata, minimizing course tutors' work. Experiments were performed to verify which bio-inspired evolutionary algorithm would be most appropriate in this context. Also, discussions regarding the quality of recommendations considering local repositories and Web have been made. Initial experiments evaluating the efficiency of the proposed approach have shown promising results.
Palavras-chave:
Personalized recommendation, Learning objects, Bio-inspired algorithms, Ontology
Referências
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Bernard, J., Chang, T.-W., Popescu, E., and Graf, S. (2016). Optimizing pattern weights with a genetic algorithm to improve automatic working memory capacity identification. In Int. Conf. on Intelligent Tutoring Systems, pages 334–340. Springer.
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CLEO (2003). CLEO Extensions to the IEEE Learning Object Metadata. Technical report, CLEO Collaborative Partners (Cisco Systems, Inc., IBM Corporation, Microsoft Corporation, Thomson NETg), Washington, USA.
Colchester, K., Hagras, H., Alghazzawi, D., and Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1):47–64.
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Dwivedi, P., Kant, V., and Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2):819–836.
El-Bishouty, M. M., Chang, T.-W., Graf, S., Chen, N.-S., et al. (2014). Smart e-course recommender based on learning styles. Journal of Computers in Education, 1(1):99–111.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
García-Floriano, A., Ferreira-Santiago, A., Yáñez-Márquez, C., Camacho-Nieto, O.,Aldape-Pérez, M., and Villuendas-Rey, Y. (2017). Social web content enhancement in a distance learning environment: intelligent metadata generation for resources. International Review of Research in Open and Distributed Learning, 18(1):161–176.
Gasparetti, F., De Medio, C., Limongelli, C., Sciarrone, F., and Temperini, M. (2018). Prerequisites between learning objects: Automatic extraction based on a machine learning approach. Telematics and Informatics, 35(3):595–610.
Harman, K. and Koohang, A. (2007). Learning objects: standards, metadata, repositories, and LCMS. Informing Science.
Hitzler, P., Kr¨otzsch, M., Parsia, B., Patel-Schneider, P. F., Rudolph, S., et al. (2009). OWL 2 web ontology language primer. W3C recommendation, 27(1):123.
IEEE LTSC (2001). IEEE 1484.12.1 - Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE.
Ince, M., Yigit, T., and Isık, A. H. (2017). A hybrid ahp-ga method for metadata-based learning object evaluation. Neural Computing and Applications, pages 1–11.
Júnior, C. B. and Dorça, F. (2018). Uma abordagem para a criação e recomendação de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. In Brazilian Symposium on Computers in Education, pages 1533–1542.
Krishnanand, K., Nayak, S. K., Panigrahi, B. K., and Rout, P. K. (2009). Comparative study of five bio-inspired evolutionary optimization techniques. In 2009 World Congress on Nature & Biologically Inspired Computing, pages 1231–1236. IEEE.
Kurilovas, E., Zilinskiene, I., and Dagiene, V. (2014). Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach. Computers in Human Behavior, 30:550–557.
McGuinness, D. L., Van Harmelen, F., et al. (2004). OWL web ontology language overview. W3C recommendation, 10(10):2004.
Ming, D. Z. T. S. Z. and Jie, Y. D. C. (2002). Overview of ontology [j]. Acta Scicentiarum Naturalum Universitis Pekinesis, 5:027.
Neven, F. and Duval, E. (2002). Reusable learning objects: a survey of lom-based repositories. In Proc. of the Tenth ACM Int. Conf. on Multimedia, pages 291–294. ACM.
Pal, S., Pramanik, P. K. D., Majumdar, T., and Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6):3243–3268.
Pontes, W. L., Franc¸a, R. M., Costa, A. P. M., and Behar, P. (2014). Filtragens de recomendação de objetos de aprendizagem: uma revisão sistemática do cbie. In Brazilian Symposium on Computers in Education, pages 549–558.
Rastegarmoghadam, M. and Ziarati, K. (2017). Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies, 22(3):1067–1087.
Roy, D., Sarkar, S., and Ghose, S. (2008). Automatic extraction of pedagogic metadata from learning content. Int. J. of Artificial Intelligence in Education, 18(2):97–118.
Tilahun, S. L. and Ong, H. C. (2015). Prey-predator algorithm: a new metaheuristic algorithm for optimization problems. Int. J. of Information Technology & Decision Making, 14(06):1331–1352.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2):65–85.
Balaji, S. and Revathi, N. (2016). A new approach for solving set covering problem using jumping particle swarm optimization method. Natural Computing, 15(3):503-517.
Bassett, L. (2015). Introduction to JavaScript object notation: a to-the-point guide to JSON. ” O’Reilly Media, Inc.”.
Beldjoudi, S., Seridi, H., and Karabadji, N. E. I. (2018). Recommendation in collaborative e-learning by using linked open data and ant colony optimization. In Int. Conf. on Intelligent Tutoring Systems, pages 23–32. Springer.
Bernard, J., Chang, T.-W., Popescu, E., and Graf, S. (2016). Optimizing pattern weights with a genetic algorithm to improve automatic working memory capacity identification. In Int. Conf. on Intelligent Tutoring Systems, pages 334–340. Springer.
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web. Scientific american, 284(5):34–43.
Bernhard, K. and Vygen, J. (2008). Combinatorial optimization: Theory and algorithms. Springer, Third Edition, 2005.
Bhaskaran, S. and Santhi, B. (2017). An efficient personalized trust based hybrid recommendation (tbhr) strategy for e-learning system in cloud computing. Cluster Computing, pages 1–13.
Brusilovsky, P. and Peylo, C. (2003). Adaptive and intelligent web-based educational systems. Int. J. of Artificial Intelligence in Education, 13:159–172.
CLEO (2003). CLEO Extensions to the IEEE Learning Object Metadata. Technical report, CLEO Collaborative Partners (Cisco Systems, Inc., IBM Corporation, Microsoft Corporation, Thomson NETg), Washington, USA.
Colchester, K., Hagras, H., Alghazzawi, D., and Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1):47–64.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. (2009). Introduction to algorithms. MIT press.
Decker, S., Melnik, S., Van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., Erdmann, M., and Horrocks, I. (2000). The semantic web: The roles of XML and RDF. IEEE Internet computing, 4(5):63–73.
Dwivedi, P., Kant, V., and Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2):819–836.
El-Bishouty, M. M., Chang, T.-W., Graf, S., Chen, N.-S., et al. (2014). Smart e-course recommender based on learning styles. Journal of Computers in Education, 1(1):99–111.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
García-Floriano, A., Ferreira-Santiago, A., Yáñez-Márquez, C., Camacho-Nieto, O.,Aldape-Pérez, M., and Villuendas-Rey, Y. (2017). Social web content enhancement in a distance learning environment: intelligent metadata generation for resources. International Review of Research in Open and Distributed Learning, 18(1):161–176.
Gasparetti, F., De Medio, C., Limongelli, C., Sciarrone, F., and Temperini, M. (2018). Prerequisites between learning objects: Automatic extraction based on a machine learning approach. Telematics and Informatics, 35(3):595–610.
Harman, K. and Koohang, A. (2007). Learning objects: standards, metadata, repositories, and LCMS. Informing Science.
Hitzler, P., Kr¨otzsch, M., Parsia, B., Patel-Schneider, P. F., Rudolph, S., et al. (2009). OWL 2 web ontology language primer. W3C recommendation, 27(1):123.
IEEE LTSC (2001). IEEE 1484.12.1 - Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE.
Ince, M., Yigit, T., and Isık, A. H. (2017). A hybrid ahp-ga method for metadata-based learning object evaluation. Neural Computing and Applications, pages 1–11.
Júnior, C. B. and Dorça, F. (2018). Uma abordagem para a criação e recomendação de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. In Brazilian Symposium on Computers in Education, pages 1533–1542.
Krishnanand, K., Nayak, S. K., Panigrahi, B. K., and Rout, P. K. (2009). Comparative study of five bio-inspired evolutionary optimization techniques. In 2009 World Congress on Nature & Biologically Inspired Computing, pages 1231–1236. IEEE.
Kurilovas, E., Zilinskiene, I., and Dagiene, V. (2014). Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach. Computers in Human Behavior, 30:550–557.
McGuinness, D. L., Van Harmelen, F., et al. (2004). OWL web ontology language overview. W3C recommendation, 10(10):2004.
Ming, D. Z. T. S. Z. and Jie, Y. D. C. (2002). Overview of ontology [j]. Acta Scicentiarum Naturalum Universitis Pekinesis, 5:027.
Neven, F. and Duval, E. (2002). Reusable learning objects: a survey of lom-based repositories. In Proc. of the Tenth ACM Int. Conf. on Multimedia, pages 291–294. ACM.
Pal, S., Pramanik, P. K. D., Majumdar, T., and Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6):3243–3268.
Pontes, W. L., Franc¸a, R. M., Costa, A. P. M., and Behar, P. (2014). Filtragens de recomendação de objetos de aprendizagem: uma revisão sistemática do cbie. In Brazilian Symposium on Computers in Education, pages 549–558.
Rastegarmoghadam, M. and Ziarati, K. (2017). Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies, 22(3):1067–1087.
Roy, D., Sarkar, S., and Ghose, S. (2008). Automatic extraction of pedagogic metadata from learning content. Int. J. of Artificial Intelligence in Education, 18(2):97–118.
Tilahun, S. L. and Ong, H. C. (2015). Prey-predator algorithm: a new metaheuristic algorithm for optimization problems. Int. J. of Information Technology & Decision Making, 14(06):1331–1352.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2):65–85.
Publicado
24/11/2020
Como Citar
PEREIRA JÚNIOR, Cleon; BELIZÁRIO JÚNIOR, Clarivando Francisco; ARAÚJO, Rafael D.; DORÇA, Fabiano A..
Personalized Recommendation of Learning Objects Through Bio-inspired Algorithms and Semantic Web Technologies: an Experimental Analysis. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1333-1342.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1333.
