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Comparing Multi-objective GA and PSO for the Pedagogical Activities Sequencing from Bloom’s Digital Taxonomy

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Computer Supported Education (CSEDU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1817))

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Abstract

Sequencing of pedagogical actions consists of determining action sequences or learning paths for improving or developing the student’s abilities. As the sequence quality is a crucial measure to evaluate the sequencer, the sequencing of pedagogical actions is an optimization problem, and techniques such as the metaheuristics from computational intelligence are suitable for coping with it. This paper formulates the sequencing problem as a multiobjective optimization problem, where the sequences contain actions associated with the Revised Bloom’s Taxonomy, the initial state is the student RASI profile, and the two optimization criteria are the similarity between the student’s profile and the sequence as well as the number of actions in the sequence. The multiobjective algorithms’ bases are genetic algorithms (GA) and particle swarm optimization (PSO) to minimize the aforementioned criteria. Students from higher education institutions were the participants in the experiments. Comparisons between both algorithms included the results found for each criterium and the satisfaction level of students with the sequences. In addition, a group of students received random sequences to compare the effectiveness of such a proposal. The algorithms found similar results among the students and suggested that the proposed approaches are better accepted than the randomized pedagogical sequences.

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Notes

  1. 1.

    In this study, “actions” refer to BT levels, and “activities” to a list of activities from BDT for each BT level.

  2. 2.

    Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.

  3. 3.

    According to the original terminology of PSO, the description uses the particle which is a solution representation.

References

  1. Agbonifo, O.C., Olanrewaju, A.O.: Genetic algorithm-based curriculum sequencing model for personalized e-learning system. Int. J. Educ. Manag. Eng. 5(8), 27–35 (2018)

    Google Scholar 

  2. Al-Muhaideb, S., Menai, M.E.B.: Evolutionary computation approaches to the curriculum sequencing problem. Nat. Comput. 10(2), 891–920 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  3. Almeida, D.J., Fernandes, M.A., da Costa, N.T.: Sequencing and recommending pedagogical activities from bloom’s taxonomy using RASI and multi-objective PSO. In: Cukurova, M., Rummel, N., Gillet, D., McLaren, B.M., Uhomoibhi, J. (eds.) Proceedings of the 14th International Conference on Computer Supported Education, CSEDU 2022, Online Streaming, 22–24 April 2022, vol. 2. pp. 105–116. SCITEPRESS (2022). https://doi.org/10.5220/0011090000003182

  4. Almeida., D.J., Fernandes., M.A., Torrezão da Costa., N.: Sequencing and recommending pedagogical activities from bloom’s taxonomy using RASi and multi-objective PSO. In: Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, pp. 105–116. INSTICC, SciTePress (2022). https://doi.org/10.5220/0011090000003182

  5. Brown, M., McCormack, M., Reeves, J., Brook, D.C., Grajek, S., Alexander, B., Bali, M., Bulger, S., Dark, S., Engelbert, N., et al.: 2020 educause horizon report teaching and, learning Technical Report EDUCAUSE (2020)

    Google Scholar 

  6. Chu, C.P., Chang, Y.C., Tsai, C.C.: Pc 2 PSO: personalized e-course composition based on particle swarm optimization. Appl. Intell. 34(1), 141–154 (2011)

    Article  Google Scholar 

  7. Churches, A.: Bloom’s digital taxonomy (2010)

    Google Scholar 

  8. da Costa, N.T., Almeida, D.J., Oliveira, G.P., Fernandes, M.A.: Customized pedagogical recommendation using automated planning for sequencing based on bloom’s taxonomy. Int. J. Distance Educ. Technol. 20, 1–19 (2022)

    Article  Google Scholar 

  9. da Costa, N.T., Fernandes, M.A.: Sequenciamento de ações pedagógicas baseadas na taxonomia de bloom usando planejamento automatizado apoiado por algoritmo genético. Revista Brasileira de Informática na Educação 29, 485–501 (2021)

    Article  Google Scholar 

  10. De-Marcos, L., Martínez, J.J., Gutiérrez, J.A., Barchino, R., Gutiérrez, J.M.: A new sequencing method in web-based education. In: 2009 IEEE Congress on Evolutionary Computation, pp. 3219–3225. IEEE (2009)

    Google Scholar 

  11. Dwivedi, P., Kant, V., Bharadwaj, K.K.: Learning path recommendation based on modified variable length genetic algorithm. Educ. Inf. Technol. 23(2), 819–836 (2018)

    Article  Google Scholar 

  12. Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. John Wiley & Sons Inc, Hoboken (2006)

    Google Scholar 

  13. Entwistle, N.: Student Learning and Academic Understanding: A Research Perspective with Implications for Teaching. Academic Press, Cambridge (June 2018)

    Google Scholar 

  14. Entwistle, N., Tait, H.: Approaches and study skills inventory for students (ASSIST) (incorporating the revised approaches to studying inventory - RASI). Centre for Research on Learning and Instruction, University of Edinburgh, Edinburgh (March 2013)

    Google Scholar 

  15. Fusilier, M., Bhuyan, R., Russell, J., Lin, S., Yang, S.: Studying approaches: samples in China, KUWAIT, and USA. J. Appl. Res. Higher Educ. ahead-of-print (2021). https://doi.org/10.1108/JARHE-11-2020-0385

  16. Goštautaitė, D.: Principal component analysis and bloom taxonomy to personalise learning. In: EDULEARN19 Proceedings 11th International Conference on Education and New Learning Technologies: Palma, Spain. 1–3 July 2019, pp. 2910–2920. IATED Academy (2019)

    Google Scholar 

  17. Goyal, M., Rajalakshmi, K.: Personalization of test sheet based on bloom’s taxonomy in e-learning system using genetic algorithm. In: Sa, P.K., Bakshi, S., Hatzilygeroudis, I.K., Sahoo, M.N. (eds.) Recent Findings in Intelligent Computing Techniques. AISC, vol. 708, pp. 409–414. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8636-6_42

    Chapter  Google Scholar 

  18. Hssina, B., Erritali, M.: A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Compu.t Sci. 151, 1152–1157 (2019)

    Article  Google Scholar 

  19. Huang, R., Spector, J.M., Yang, J.: Educational Technology: A Primer for the 21st Century. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6643-7

  20. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation. vol. 5, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  21. Krathwohl, D.R.: A revision of bloom’s taxonomy: an overview. Theory Into Pract. 41(4), 212–218 (2002). https://doi.org/10.1207/s15430421tip4104_2

  22. Lin, Y.S., Chang, Y.C., Chu, C.P.: An innovative approach to scheme learning map considering tradeoff multiple objectives. J Educ. Technol. Soc. 19(1), 142–157 (2016)

    Google Scholar 

  23. Martins, A.F., Machado, M., Bernardino, H.S., de Souza, J.F.: A comparative analysis of metaheuristics applied to adaptive curriculum sequencing. Soft. Comput. 25(16), 11019–11034 (2021). https://doi.org/10.1007/s00500-021-05836-9

    Article  Google Scholar 

  24. de Miranda, P.B., et al.: Uma abordagem multiobjetivo para recomendação de caminhos de aprendizagem para grupo de usuários. Revista Brasileira de Informática na Educação 27(3) (2019)

    Google Scholar 

  25. Pireva, K., Kefalas, P.: A recommender system based on hierarchical clustering for cloud e-learning. In: Intelligent Distributed Computing XI, pp. 235–245 (2017). https://doi.org/10.1007/978-3-319-66379-1_21

  26. Schrock, K.: Bloomin’ Apps: Kathy Schrock’s Guide to Everything (September 2011), https://www.schrockguide.net/bloomin-apps.html

  27. Smaili, E.M., Khoudda, C., Sraidi, S., Charaf, M.E.H.: An optimized method for adaptive learning based on PSO algorithm. In: 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), pp. 1–5. IEEE (2020)

    Google Scholar 

  28. Subiyantoro, E., Ashari, A., Suprapto, S.: Learning Path Recommendation using Hybrid Particle Swarm Optimization. Adv. Sci. Technol. Eng. Syst. J. 6(1), 570–576 (2021). https://doi.org/10.25046/aj060161

  29. Tait, H., Entwistle, N.: Identifying students at risk through ineffective study strategies. Higher Educ. 31, 97–116 (1996). https://doi.org/10.1007/BF00129109

  30. Woolf, B.P.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Morgan Kaufmann (2010)

    Google Scholar 

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Acknowledgment

The authors thank the Federal University of Uberlândia, the Goiano Federal Institute, and the Federal Institute of Triângulo Mineiro for supporting this research.

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Authors and Affiliations

  1. Computing Department, Federal University of Uberlândia, Uberlândia, Brazil

    Denis José Almeida & Márcia Aparecida Fernandes

  2. Instituto Federal Goiano, Campus Iporá, Iporá, Brazil

    Newarney Torrezão da Costa

Authors
  1. Denis José Almeida
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  2. Newarney Torrezão da Costa
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  3. Márcia Aparecida Fernandes
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Corresponding author

Correspondence to Márcia Aparecida Fernandes .

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  1. University of Ulster, Newtownabbey, UK

    James Uhomoibhi

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Almeida, D.J., da Costa, N.T., Fernandes, M.A. (2023). Comparing Multi-objective GA and PSO for the Pedagogical Activities Sequencing from Bloom’s Digital Taxonomy. In: Uhomoibhi, J. (eds) Computer Supported Education. CSEDU 2022. Communications in Computer and Information Science, vol 1817. Springer, Cham. https://doi.org/10.1007/978-3-031-40501-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-40501-3_8

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