Abstract
MathE is an international online platform that aims to provide a resource for in-class support as well as an alternative instrument to teach and study mathematics. This work focuses on the investigations of the students’ behavior when answering the training questions available in the platform. In order to draw conclusions about the value of the platform, the ways in which the students use it and what are the most wanted mathematical topics, thus deepening the knowledge about the difficulties faced by the users and finding how to make the platform more efficient, the data collected since the it was launched (3 years ago) is analyzed through the use of data mining and machine learning techniques. In a first moment, a general analysis was performed in order to identify the students’ behavior as well as the topics that require reorganization; it was followed by a second iteration, according to the students’ country of origin, in order to identify the existence of differences in the behavior of students from distinct countries. The results point out that the advanced level of the platform’s questions is not adequate and that the questions should be reorganized in order to ensure a more consistent support for the students’ learning process. Besides, with this analysis it was possible to identify the topics that require more attention through the addition of more questions. Furthermore, it was not possible to identify significant disparities in the students behavior in what concerns the students’ country of origin.
This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021); Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288; Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021.
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References
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. SODA 2007, Society for Industrial and Applied Mathematics, USA (2007)
Ausubel, D.: The Acquisition and Retention of Knowledge: A Cognitive View. SPRINGER-SCIENCE+BUSINESS MEDIA, B.V (2000)
Azevedo, B.F., Rocha, A.M.A.C., Fernandes, F.P., Pacheco, M.F., Pereira, A.I.: Evaluating student behaviour on the mathe platform - clustering algorithms approaches. In: (In press) Book of 16th Learning and Intelligent Optimization Conference - LION 2022, pp. XX-XX. Milos - Greece (2022)
Azevedo, B.F., Amoura, Y., Rocha, A.M.A.C., Fernandes, F.P., Pacheco, M.F., Pereira, A.I.: Analyzing the mathe platform through clustering algorithms. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds.) Computational Science and Its Applications - ICCSA 2022 Workshops. LNCS, vol. 13378, pp. 201–218. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10562-3_15
Bransford, J., Brown, A.L., Cocking, R.R.: How people learn: brain, mind, experience, and school (2000). http://www.nap.edu/books/0309070368/html. Accessed June 2022
McKeachie, W.J.: Teaching Tips: Strategies, Research, and Theory for College and University Teachers. Houghton Mifflin, Boston, MA (2002)
MCLeod, S.A.: Constructivism as a theory for teaching and learning (2019). www.simplypsychology.org/constructivism.html. Accessed June 2022
Nation, U.: Sustainable development goals (2020). www.un.org/sustainabledevelopment/education/. accessed August 2022
Novak, J.D., Gowin, D.B.: Learning How to Learn. Cambridge University Press (1984)
OECD: The future of education and skills - education 2030 (2018). https://www.oecd.org/education/2030/E2030%20Position%20Pape%20(05.04.2018).pdf. Accessed June 2022
OECD: PISA 2018 Results, Vol. I. OECD Publishing (2019)
Oliver, K.: Methods for developing constructivist learning on the web. Educ. Technol. 40, 5–18 (2000)
Pacheco, M.F., Pereira, A.I., Fernandes, F.: Mathe - improve mathematical skills in higher education. In: Proceedings of the 2019 8th International Conference on Educational and Information Technology (ICET2019), pp. 173–176. United Kingdom (2019)
Piaget, J., Claparède, E.: The Language and Thought of the Child. Routledge (1959)
Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O. and Rokach, L. (eds). Clustering Methods Data Mining and Knowledge Discovery Handbook, Springer, Boston (2005). https://doi.org/10.1007/0-387-25465-X_15
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Trujillo, G., Tanner, K.: Considering the role of affect in learning: Monitoring students’ self-efficacy, sense of belonging, and science identity. CBE life Sci. Educ. 13, 6–15 (03 2014)
Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Processes. Harvard University Press (1978)
Vygotsky, L.S.: Thought and Language. MIT Press (1986)
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Azevedo, B.F., Romanenko, S.F., de Fatima Pacheco, M., Fernandes, F.P., Pereira, A.I. (2022). Data Analysis Techniques Applied to the MathE Database. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_43
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