Abstract
Measuring student achievement and competencies in mathematics is important for the teacher and the educational system, as well as in view of improving the motivation to learn among students. In this study we aim to develop an assessment methodology based on data mining approach. Two data mining techniques – cluster analysis and classification and regression trees (CART) are applied to investigate the influence of assessment elements on the final grade in two core mathematical subjects - linear algebra and analytical geometry. In addition, the specialty, academic year of education, and the sex of the students, as well as competency-based test results on content covered by secondary education curriculum are included. Using hierarchical cluster analysis the variables of scale type are classified into two clusters. CART models are built to regress and predict the summative assessment results in dependence of examined variables. The obtained models fit well over 90% of the data. It was established the relative importance of used variables in the model. The obtained results help to measure directly the student achievements and competencies in mathematics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Heywood, J.: The Assessment of Learning in Engineering Education, Practice and Policy. John Wiley & Sons Inc., Hoboken (2016)
White, R.W.: Motivation reconsidered: the concept of competence. Psychol. Rev. 66(5), 297–333 (1959)
Niss, M.: Mathematical competencies and the learning of mathematics: the Danish KOM project. In: 3rd Mediterranean Conference on Mathematical Education, pp. 115–124. The Hellenic Mathematical Society, Athens (2003)
Alpers, B., Demlova, M., Fant, C.-H., Gustafsson, T., Lawson, D., Mustoe, L., Olsson-Lehtonen, B., Robinson, C., Velichova, D. (eds.): A Framework for Mathematics Curricula in Engineering Education: A Report of the Mathematics Working Group. European Society for Engineering Education (SEFI), Brussels (2013)
Carr, M., Brian, B., Fhloinn, E.N.: Core skills assessment to improve mathematical competency. Eur. J. Eng. Educ. 38(6), 608–619 (2013)
Fhloinn, E.N., Carr, M.: Formative assessment in mathematics for engineering students. Eur. J. Eng. Educ. 42(4), 1–13 (2017)
Sangwin, C.J., Köcher, N.: Automation of mathematics examinations. Comput. Educ. 94, 215–227 (2016)
Albano G.: Knowledge, skills, competencies: a model for mathematics e-learning. In: Kwan, R., McNaught, C., Tsang, P., Wang, F.L., Li, K.C. (eds.) Enhancing Learning Trough Technology. Education Unplugged: Mobile Technologies and Web 2.0. ICT 2011. Communications in Computer and Information Science, vol. 177, pp. 214–225. Springer, Heidelberg (2011)
RULES_MATH Homepage. https://rules-math.com/. Accessed 25 Jan 2019
Rasteiro, D.M.L.D., Martinez, G.V., Caridade, C., Martin-Vaquero, J., Queiruga-Dios, A.: Changing teaching: competencies versus contents. In: Global Engineering Education Conference, EDUCON 2018, pp. 1761–1765. IEEE Press, New York (2018)
Queiruga-Dios, A., Sanchez, G.R., Del Rey, A.M., Demlova, M.: Teaching and assessing discrete mathematics. In: Global Engineering Education Conference, EDUCON 2018, pp. 1568–1571. IEEE Press, New York (2018)
Tapado, B.M., Acedo, G.G., Palaoag, T.D.: Evaluating information technology graduates employability using decision tree algorithm. In: The 9th International Conference on E-Education, E-Business, E-Management and E-Learning, IC4E 2018, pp. 88–93, ACM International Conference Proceedings Series, New York (2018)
Mat, U.B., Buniyamin, N.: Using neuro-fuzzy technique to classify and predict electrical engineering students’ achievement upon graduation based on mathematics competency. Indonesian J. Electr. Eng. Comput. Sci. 5(3), 684–690 (2017)
Jain, A., Choudhury, T., Mor, P., Sabitha, A.S.: Intellectual performance analysis of students by comparing various data mining techniques. In: 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 57–62. IEEE Press, Singapore (2017)
Dutt, A., Ismail, M.A., Herawan, T.: A systematic review on educational data mining. IEEE Access 5, 15991–16005 (2017)
Rencher, A.C., Christensen, W.F.: Methods of Multivariate Analysis, 3rd edn. Wiley, New York (2012)
Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer, Boston (2005)
Wu, X., Kumar, V.: The Top Ten Algorithms in Data Mining. Chapman & Hall/CRC, Boca Raton (2009)
Acknowledgments
This work was co-funded by Erasmus+ program of the European Union under grant 2017-1-ES01-KA203-038491 (RULES_MATH). The first and third authors acknowledge partial support from the Grant No. BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program (2014–2020), co-financed by the European Union through the European structural and Investment funds.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gocheva-Ilieva, S. et al. (2020). Data Mining for Statistical Evaluation of Summative and Competency-Based Assessments in Mathematics. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-20005-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20004-6
Online ISBN: 978-3-030-20005-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)