Geração de Sequências Curriculares Adaptativas baseada em Computação Evolucionária: Estado da Arte e Tendências
Resumo
Para efetiva adoção dos sistemas de e-Learning, as necessidades e o contexto do aluno precisam ser levados em consideração na entrega de conteúdo. Assim, um dos desafios mais interessantes da área é a seleção de uma Sequência Curricular Adaptativa que forneça um ensino individualizado. Tal desafio é conhecido na literatura como um problema NP-Difícil, causando uma frequente utilização de meta-heurísticas, particularmente de Computação Evolucionária, para aproximar à melhor solução. Neste artigo é feita uma revisão dos últimos trabalhos neste campo de pesquisa, apresentando um modelo de comparação de acordo com as variáveis utilizadas para o sequenciamento.
Palavras-chave:
Sequências Curriculares Adaptativas, Computação Evolucionária, e-Learning
Referências
Acampora, G., Gaeta, M., and Loia, V. (2011). Hierarchical optimization of personalized experiences for e-learning systems through evolutionary models. Neural Computing and Applications, 20(5):641–657.
Agarwal, S., Goyal, M., Kumar, A., and Rajalakshmi, K. (2016). Intuitionistic fuzzy ant colony optimization for course sequencing in e-learning. In Contemporary Computing (IC3), 2016 Ninth International Conference on, pages 1–6. IEEE.
Al-Azawei, A. and Badii, A. (2014). State of the art of learning styles-based adaptive educational hypermedia systems (ls-baehss). International Journal of Computer Science & Information Technology, 6(3):1.
Al-Muhaideb, S. and Menai, M. E. B. (2011). Evolutionary computation approaches to the curriculum sequencing problem. Natural Computing, 10(2):891–920.
Bhaskar, M., Das, M. M., Chithralekha, T., and Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4):1271–1279.
Brusilovsky, P. et al. (1999). Adaptive and intelligent technologies for web-based eduction. Ki, 13(4):19–25.
Chakraverty, S., Hans, S., and Mittal, V. (2012). Aco-driven personalized e-learning with perspectives and learning aims. In Proceedings of the International Conference on E-learning, E-business, Enterprise Information Systems, & E-government, pages 359–365.
Chu, C.-P., Chang, Y.-C., and Tsai, C.-C. (2011). Pc2pso: personalized e-course composition based on particle swarm optimization. Applied Intelligence, 34(1):141–154.
Davis, D., Chen, G., Hauff, C., and Houben, G.-J. (2016). Gauging mooc learners’ adherence to the designed learning path. In EDM ’16: 9th International Conference on Educational Data Mining, page ...
de Marcos, L., Martinez, J.-J., Gutierrez, J.-A., Barchino, R., Hilera, J.-R., Oton, S., and Gutierrez, J.-M. (2011). Genetic algorithms for courseware engineering. International Journal of Innovative Computing, Information and Control, 7(7):1–27.
Dharshini, A. P., Chandrakumarmangalam, S., and Arthi, G. (2015). Ant colony optimization for competency based learning objects sequencing in e-learning. Applied Mathematics and Computation, 263:332–341.
Hnida, M., Idrissi, M. K., and Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. In Information Technology Based Higher Education and Training (ITHET), 2016 15th International Conference on, pages 1–6. IEEE.
Jebari, K., El Moujahid, A., Bouroumi, A., and Ettouhami, A. (2011). Genetic algorithms for online remedial education based on competency approach. In Multimedia Computing and Systems (ICMCS), 2011 International Conference on, pages 1–6. IEEE.
Kardan, A. A., Aziz, M., and Shahpasand, M. (2015). Adaptive systems: a content analysis on technical side for e-learning environments. Artificial Intelligence Review, 44(3):365–391.
Kardan, A. A., Ebrahim, M. A., and Imani, M. B. (2014). A new personalized learning path generation method: Aco-map. Indian Journal of Scientific Research, 5(1):17.
Khamparia, A. and Pandey, B. (2015). Knowledge and intelligent computing methods in e-learning. International Journal of Technology Enhanced Learning, 7(3):221–242
Li, J.-W., Chang, Y.-C., Chu, C.-P., and Tsai, C.-C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3):3223–3232.
Muhammad, A., Zhou, Q., Beydoun, G., Xu, D., and Shen, J. (2016). Learning path adaptation in online learning systems. In Computer Supported Cooperative Work in Design (CSCWD), 2016 IEEE 20th International Conference on, pages 421–426. IEEE.
Pires, J. M. and Cota, M. P. (2015). A new learning cognitive architecture using a statistical function and genetic algorithms: An intelligent new e-learning model. In eLearning (econf), 2015 Fifth International Conference on, pages 1–8. IEEE.
Premlatha, K. and Geetha, T. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4):443–465.
Pushpa, M. (2012). Aco in e-learning: Towards an adaptive learning path. International Journal on Computer Science and Engineering, 4(3):458.
Riad, B., Ali, S., Mourad, H., and Hamid, S. (2012). An adaptive learning based on ant colony and collaborative filtering. In Proceedings of the World Congress on Engineering, volume 2, pages 851–855.
Sharma, R., Banati, H., and Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, pages 579–590. Springer.
Shmelev, V., Karpova, M., and Dukhanov, A. (2015). An approach of learning path sequencing based on revised bloom’s taxonomy and domain ontologies with the use of genetic algorithms. Procedia Computer Science, 66:711–719.
Tan, X.-h., Shen, R.-m., and Wang, Y. (2012). Personalized course generation and evolution based on genetic algorithms. Journal of Zhejiang University SCIENCE C, 13(12):909–917.
Van Merrienboer, J. J. and Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development, 53(3):5–13.
Vazquez, J. M. M., Ramirez, J. A. O., Gonzalez-Abril, L., and Morente, F. V. (2011). Designing adaptive learning itineraries using features modelling and swarm intelligence. Neural Computing and Applications, 20(5):623–639.
Wan, S. and Lyu, C. (2014). Adaptive course generation based on evolutionary algorithm. In Information Science and Technology (ICIST), 2014 4th IEEE International Conference on, pages 168–171. IEEE.
Wang, F.-H. (2011). Personalized recommendation for web-based learning based on ant colony optimization with segmented-goal and meta-control strategies. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, pages 2054–2059. IEEE.
Wang, S.-L. and Wu, C.-Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with applications, 38(9):10831–10838.
Wong, L.-H. and Looi, C.-K. (2010). A survey of optimized learning pathway planning and assessment paper generation with swarm intelligence. Intelligent Tutoring Systems in E-learning Environments: Design, Implementation and Evaluation, Hershey: IGI Global, pages 285–302.
Agarwal, S., Goyal, M., Kumar, A., and Rajalakshmi, K. (2016). Intuitionistic fuzzy ant colony optimization for course sequencing in e-learning. In Contemporary Computing (IC3), 2016 Ninth International Conference on, pages 1–6. IEEE.
Al-Azawei, A. and Badii, A. (2014). State of the art of learning styles-based adaptive educational hypermedia systems (ls-baehss). International Journal of Computer Science & Information Technology, 6(3):1.
Al-Muhaideb, S. and Menai, M. E. B. (2011). Evolutionary computation approaches to the curriculum sequencing problem. Natural Computing, 10(2):891–920.
Bhaskar, M., Das, M. M., Chithralekha, T., and Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4):1271–1279.
Brusilovsky, P. et al. (1999). Adaptive and intelligent technologies for web-based eduction. Ki, 13(4):19–25.
Chakraverty, S., Hans, S., and Mittal, V. (2012). Aco-driven personalized e-learning with perspectives and learning aims. In Proceedings of the International Conference on E-learning, E-business, Enterprise Information Systems, & E-government, pages 359–365.
Chu, C.-P., Chang, Y.-C., and Tsai, C.-C. (2011). Pc2pso: personalized e-course composition based on particle swarm optimization. Applied Intelligence, 34(1):141–154.
Davis, D., Chen, G., Hauff, C., and Houben, G.-J. (2016). Gauging mooc learners’ adherence to the designed learning path. In EDM ’16: 9th International Conference on Educational Data Mining, page ...
de Marcos, L., Martinez, J.-J., Gutierrez, J.-A., Barchino, R., Hilera, J.-R., Oton, S., and Gutierrez, J.-M. (2011). Genetic algorithms for courseware engineering. International Journal of Innovative Computing, Information and Control, 7(7):1–27.
Dharshini, A. P., Chandrakumarmangalam, S., and Arthi, G. (2015). Ant colony optimization for competency based learning objects sequencing in e-learning. Applied Mathematics and Computation, 263:332–341.
Hnida, M., Idrissi, M. K., and Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. In Information Technology Based Higher Education and Training (ITHET), 2016 15th International Conference on, pages 1–6. IEEE.
Jebari, K., El Moujahid, A., Bouroumi, A., and Ettouhami, A. (2011). Genetic algorithms for online remedial education based on competency approach. In Multimedia Computing and Systems (ICMCS), 2011 International Conference on, pages 1–6. IEEE.
Kardan, A. A., Aziz, M., and Shahpasand, M. (2015). Adaptive systems: a content analysis on technical side for e-learning environments. Artificial Intelligence Review, 44(3):365–391.
Kardan, A. A., Ebrahim, M. A., and Imani, M. B. (2014). A new personalized learning path generation method: Aco-map. Indian Journal of Scientific Research, 5(1):17.
Khamparia, A. and Pandey, B. (2015). Knowledge and intelligent computing methods in e-learning. International Journal of Technology Enhanced Learning, 7(3):221–242
Li, J.-W., Chang, Y.-C., Chu, C.-P., and Tsai, C.-C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3):3223–3232.
Muhammad, A., Zhou, Q., Beydoun, G., Xu, D., and Shen, J. (2016). Learning path adaptation in online learning systems. In Computer Supported Cooperative Work in Design (CSCWD), 2016 IEEE 20th International Conference on, pages 421–426. IEEE.
Pires, J. M. and Cota, M. P. (2015). A new learning cognitive architecture using a statistical function and genetic algorithms: An intelligent new e-learning model. In eLearning (econf), 2015 Fifth International Conference on, pages 1–8. IEEE.
Premlatha, K. and Geetha, T. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4):443–465.
Pushpa, M. (2012). Aco in e-learning: Towards an adaptive learning path. International Journal on Computer Science and Engineering, 4(3):458.
Riad, B., Ali, S., Mourad, H., and Hamid, S. (2012). An adaptive learning based on ant colony and collaborative filtering. In Proceedings of the World Congress on Engineering, volume 2, pages 851–855.
Sharma, R., Banati, H., and Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, pages 579–590. Springer.
Shmelev, V., Karpova, M., and Dukhanov, A. (2015). An approach of learning path sequencing based on revised bloom’s taxonomy and domain ontologies with the use of genetic algorithms. Procedia Computer Science, 66:711–719.
Tan, X.-h., Shen, R.-m., and Wang, Y. (2012). Personalized course generation and evolution based on genetic algorithms. Journal of Zhejiang University SCIENCE C, 13(12):909–917.
Van Merrienboer, J. J. and Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development, 53(3):5–13.
Vazquez, J. M. M., Ramirez, J. A. O., Gonzalez-Abril, L., and Morente, F. V. (2011). Designing adaptive learning itineraries using features modelling and swarm intelligence. Neural Computing and Applications, 20(5):623–639.
Wan, S. and Lyu, C. (2014). Adaptive course generation based on evolutionary algorithm. In Information Science and Technology (ICIST), 2014 4th IEEE International Conference on, pages 168–171. IEEE.
Wang, F.-H. (2011). Personalized recommendation for web-based learning based on ant colony optimization with segmented-goal and meta-control strategies. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, pages 2054–2059. IEEE.
Wang, S.-L. and Wu, C.-Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with applications, 38(9):10831–10838.
Wong, L.-H. and Looi, C.-K. (2010). A survey of optimized learning pathway planning and assessment paper generation with swarm intelligence. Intelligent Tutoring Systems in E-learning Environments: Design, Implementation and Evaluation, Hershey: IGI Global, pages 285–302.
Publicado
30/10/2017
Como Citar
MACHADO, Marcelo O. C.; SOUZA, Jairo F.; BARRÉRE, Eduardo.
Geração de Sequências Curriculares Adaptativas baseada em Computação Evolucionária: Estado da Arte e Tendências. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 28. , 2017, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1137-1146.
DOI: https://doi.org/10.5753/cbie.sbie.2017.1137.
