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2022 | OriginalPaper | Chapter

Evaluating Student Behaviour on the MathE Platform - Clustering Algorithms Approaches

Authors : Beatriz Flamia Azevedo, Ana Maria A. C. Rocha, Florbela P. Fernandes, Maria F. Pacheco, Ana I. Pereira

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

The MathE platform is an online educational platform that aims to help students who struggle to learn college mathematics as well as students who wish to deepen their knowledge on subjects that rely on a strong mathematical background, at their own pace. The MathE platform is currently being used by a significant number of users, from all over the world, as a tool to support and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills. This paper is addressed to evaluate the students’ performance on the Linear Algebra topic, which is a specific topic of the MathE platform. In order to achieve this goal, four clustering algorithms were considered; three of them based on different bio-inspired techniques and the k-means algorithm. The results showed that most students choose to answer only basic level questions, and even within that subset, they make a lot of mistakes. When students take the risk of answering advanced questions, they make even more mistakes, which causes them to return to the basic level questions. Considering these results, it is now necessary to carry out an in-depth study to reorganize the available questions according to other levels of difficulty, and not just between basic and advanced levels as it is.

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Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035. Society for Industrial and Applied Mathematics, USA (2007). https://doi.org/10.1145/1283383.1283494

Atabay, H.A., Sheikhzadeh, M.J., Torshizi, M.: A clustering algorithm based on integration of k-means and pso. In: 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC2016) - Higher Education Complex of Bam, pp. 59–63. Iran (2016). https://doi.org/10.1109/CSIEC.2016.7482110

Azevedo, B.F.: Study of Genetic Algorithms for Optimization Problems. Master’s thesis, Instituto Politecnico de Braganca Escola Superior de Tecnologia e Gestao, Portugal, Braganca, Portugal (2020)

Azevedo, B.F., Amoura, Y., Kantayeva, G., Pacheco, M.F., Pereira, A.I., Fernandes, F.P.: Collaborative Learning Platform Using Learning Optimized Algorithms, vol. 1488. Springer (2021). https://doi.org/10.1007/978-3-030-91885-9-52

Azevedo, B.F., Pereira, A.I., Fernandes, F.P., Pacheco, M.F.: Mathematics learning and assessment using MathE platform: a case study. Educ. Inf. Technol. 27(2), 1747–1769 (2021). https://doi.org/10.1007/s10639-021-10669-yCrossRef

Bansal, J.C., Singh, P.K., Pal, N.R. (eds.): Evolutionary and Swarm Intelligence Algorithms. SCI, vol. 779. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91341-4CrossRef

Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224–227 (1979). https://doi.org/10.1109/TPAMI.1979.4766909

Eesa, A.S., Orman, Z.: A new clustering method based on the bio-inspired cuttlefish optimization algorithm. Expert Syst.37 (2020). https://doi.org/10.1111/exsy.12478

Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995). https://doi.org/10.1109/ICNN.1995.488968

10.

Kuo, R.J., Amornnikun, P., Nguyen, T.P.Q.: Metaheuristic-based possibilistic multivariate fuzzy weighted c-means algorithms for market segmentation. Appl. Soft Comput. J. 96 (2020). https://doi.org/10.1016/j.asoc.2020.106639

11.

Kuo, R.J., Huang, Y.D., Lin, C.C., Wu, Y.H., Zulvia, F.E.: Automatic kernel clustering with bee colony optimization algorithm. Inf. Sci. 283, 107–122 (2014). https://doi.org/10.1016/j.ins.2014.06.019CrossRef

12.

MATLAB: The mathworks inc (2019a). https://www.mathworks.com/products/matlab.html

13.

Nakane, T., Bold, N., Sun, H., Lu, X., Akashi, T., Zhang, C.: Application of evolutionary and swarm optimization in computer vision: a literature survey. IPSJ Trans. Comput. Vis. Appl. 12(1), 1–34 (2020). https://doi.org/10.1186/s41074-020-00065-9CrossRef

14.

Nemmich, M.A., Debbat, F., Slimane, M.: A data clustering approach using bees algorithm with a memory scheme. Lecture Notes Networks Syst. 50, 261–270 (2019). https://doi.org/10.1007/978-3-319-98352-3-28CrossRef

15.

Nguyen, T.P.Q., Kuo, R.J.: Automatic fuzzy clustering using non-dominated sorting particle swarm optimization algorithm for categorical data. IEEE Access 7, 99721–99734 (2019). https://doi.org/10.1109/ACCESS.2019.2927593CrossRef

16.

Pacifico, L.D.S., Ludermir, T.B.: An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering. Nat. Comput. 20(3), 611–636 (2020). https://doi.org/10.1007/s11047-020-09809-zMathSciNetCrossRef

17.

Pedró, F., Subosa, M., Rivas, A., Valverde, P.: Artificial intelligence in education: challenges and opportunities for sustainable development (2019), uNESCO DOC Digital Library - Available online at https://unesdoc.unesco.org/ark:/48223/pf0000366994. Accessed May 2021

18.

Qaddoura, R., Faris, H., Aljarah, I.: An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis. J. Ambient. Intell. Humaniz. Comput. 12(8), 8387–8412 (2020). https://doi.org/10.1007/s12652-020-02570-2CrossRef

19.

Saitta, S., Raphael, B., Smith, I.F.C.: A comprehensive validity index for clustering. Intell. Data Anal. 12(6), 529–548 (2008). https://doi.org/10.3233/IDA-2008-12602CrossRef

20.

Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory To Algorithms. Cambridge University Press (2014)

21.

Singh, T.: A novel data clustering approach based on whale optimization algorithm. Expert Syst. 38(3) (2021). https://doi.org/10.1111/exsy.12657

22.

Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, 1 edn. (2008). https://doi.org/10.1007/978-3-540-73190-0

23.

Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328MathSciNetCrossRefMATH

24.

Yapiz: Evolutionary clustering and automatic clustering (2022). https://www.mathworks.com/matlabcentral/fileexchange/52865-evolutionary-clustering-and-automatic-clustering. Accessed 2 Feb 2022

Title: Evaluating Student Behaviour on the MathE Platform - Clustering Algorithms Approaches
Authors: Beatriz Flamia Azevedo
Ana Maria A. C. Rocha
Florbela P. Fernandes
Maria F. Pacheco
Ana I. Pereira
Publisher: Springer International Publishing
Book: Learning and Intelligent Optimization
Print ISBN: 978-3-031-24865-8

Electronic ISBN: 978-3-031-24866-5

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-24866-5_24

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