Pedagogical Action Sequencing by Genetic Algorithm Using Bloom's Taxonomy and ASSIST
Abstract
This paper proposes the automatic and personalized sequencing of pedagogical actions, from the point of view of automated planning based on genetic algorithm. The actions were modeled from the structure presented by the two-dimensional analysis of the Bloom Taxonomy (TB). The personalization of the actions occurs in accordance with the cognitive profile of the student, guided by the characteristics given by Approaches and Study Skills Inventory for Students (ASSIST). For this purpose, a relationship between TB and ASSIST was mapped in this work. The results show that it is possible to perform the sequencing of personalized pedagogical actions based on the student's cognitive profile, from the proposed relationship.
Keywords:
Automated Planning, Student Cognitive Profile, Optimization Problem, Evolutionary Algorithm
References
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Brown, S., White, S., Wakeling, L., and Naiker, M. (2015). Approaches and study skills inventory for students (ASSIST) in an introductory course in chemistry. Journal of University Teaching & Learning Practice.
Costa, N., Júnior, C. P., and Fernandes, M. (2019a). Recomendação de ações pedagógicas utilizando planejamento automático e taxonomia digital de Bloom. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 30, page 1531.
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Entwistle, N. and Tait, H. (2013). Approaches and study skills inventory for students(assist) (incorporating the revised approaches to studying inventory – RASI). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Garrido, A., Morales, L., and Serina, I. (2016). On the use of case-based planning fore-learning personalization. Expert Systems with Applications, 60:1–15.
Hssina, B. and Erritali, M. (2019). A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Computer Science, 151:1152–1157.
Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218.
Limongelli, C. and Sciarrone, F. (2014). Fuzzy student modeling for personalization of e-learning courses. In Zaphiris, P. and Ioannou, A., editors, Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, pages 292–301, Cham. Springer International Publishing.
Lin, Y.-S., Chang, Y.-C., and Chu, C.-P. (2016). An innovative approach to scheme learning map considering tradeoff multiple objectives. Journal of Educational Technology& Society, 19(1):142–157.
Machado, M., Barrére, E., and Souza, J. (2018). Uma abordagem evolutiva para o problema de sequenciamento curricular adaptativo. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 29,page 1243.
Pireva, K. and Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 53:235 – 245.
Rastegarmoghadam, M. and Ziarati, K. (2017). Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies,22(3):1067–1087.
Shang, H. (2019). Cultural interpretation of deep approach to learning: an empirical analysis in a chinese university. In Cross-Cultural Business Conference 2019, page207.
Vanitha, V., Krishnan, P., and Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in e-learning. Computers & Electrical Engineering, 77:325–338.
Ariyaratne, M. and Fernando, T. (2014). A comparative study on nature inspired algorithms with firefly algorithm. International Journal of Engineering and Technology,4(10):611–617.
Brown, S., White, S., Wakeling, L., and Naiker, M. (2015). Approaches and study skills inventory for students (ASSIST) in an introductory course in chemistry. Journal of University Teaching & Learning Practice.
Costa, N., Júnior, C. P., and Fernandes, M. (2019a). Recomendação de ações pedagógicas utilizando planejamento automático e taxonomia digital de Bloom. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 30, page 1531.
Costa, N., Pereira Junior, C., Araújo, R., and Fernandes, M. (2019b). Application of AI planning in the context of e-learning. In International Conference on Advanced Learning Technologies (ICALT), page 57.
de Miranda, P. B., Ferreira, R., Castro, M. S., Neto, G. F., Souza, S. J., Santos, L. A., and Silva, L. L. (2019). Uma abordagem multiobjetivo para recomendação de caminhos de aprendizagem para grupo de usuários. Revista Brasileira de Informática na Educação, 27(3).
Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley &Sons.
Entwistle, N., McCune, V., and Tait, H. (1997). The approaches and study skills inventory for students (assist). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Entwistle, N. and Tait, H. (2013). Approaches and study skills inventory for students(assist) (incorporating the revised approaches to studying inventory – RASI). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Garrido, A., Morales, L., and Serina, I. (2016). On the use of case-based planning fore-learning personalization. Expert Systems with Applications, 60:1–15.
Hssina, B. and Erritali, M. (2019). A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Computer Science, 151:1152–1157.
Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218.
Limongelli, C. and Sciarrone, F. (2014). Fuzzy student modeling for personalization of e-learning courses. In Zaphiris, P. and Ioannou, A., editors, Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, pages 292–301, Cham. Springer International Publishing.
Lin, Y.-S., Chang, Y.-C., and Chu, C.-P. (2016). An innovative approach to scheme learning map considering tradeoff multiple objectives. Journal of Educational Technology& Society, 19(1):142–157.
Machado, M., Barrére, E., and Souza, J. (2018). Uma abordagem evolutiva para o problema de sequenciamento curricular adaptativo. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 29,page 1243.
Pireva, K. and Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 53:235 – 245.
Rastegarmoghadam, M. and Ziarati, K. (2017). Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies,22(3):1067–1087.
Shang, H. (2019). Cultural interpretation of deep approach to learning: an empirical analysis in a chinese university. In Cross-Cultural Business Conference 2019, page207.
Vanitha, V., Krishnan, P., and Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in e-learning. Computers & Electrical Engineering, 77:325–338.
Published
2020-11-24
How to Cite
COSTA, Newarney Torrezão da; FERNANDES, Marcia Aparecida.
Pedagogical Action Sequencing by Genetic Algorithm Using Bloom's Taxonomy and ASSIST. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1273-1282.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1273.
