Teaching Partial Order Relations: A Programming Approach

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Author(s)

Dayou Jiang 1,*

1. Department of Computer Science and Technology, Anhui University of Finance and Economics, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2024.01.03

Received: 6 Aug. 2023 / Revised: 3 Oct. 2023 / Accepted: 14 Dec. 2023 / Published: 8 Feb. 2024

Index Terms

Discrete mathematics, partial order relations, teaching framework, Python programming, algorithm implementation

Abstract

This paper investigates teaching methods that leverage programming techniques to strengthen the understanding of partial ordering relations. Partial orders are vital in diverse domains, such as mathematics and economics. A comprehensive teaching framework is presented in this paper, incorporating standard programming languages to instruct partial order relations effectively. The approach integrates theoretical concepts, practical illustrations, and interactive programming exercises to enhance students' comprehension and application of partial order relations. Furthermore, the evaluation of teaching effectiveness and potential implications for computer science and mathematics education are discussed.

Cite This Paper

Dayou Jiang, "Teaching Partial Order Relations: A Programming Approach", International Journal of Education and Management Engineering (IJEME), Vol.14, No.1, pp. 25-32, 2024. DOI:10.5815/ijeme.2024.01.03

Reference

[1]https://en.wikipedia.org/wiki/Partially_ordered_set
[2]Carlsen, L., & Bruggemann, R. (2021). Inequalities in the European Union—A Partial Order Analysis of the Main Indicators. Sustainability, 13(11), 6278.
[3]Leemans, S.J.J., van Zelst, S.J. & Lu, X. (2023). Partial-order-based process mining: a survey and outlook. Knowl Inf Syst. 65, 1–29.
[4]Gao, Z., Ries, C., Simon, H., & Zilles, S. (2016). Preference-based teaching. In Conference on Learning Theory, Hamilton, New Zealand, November 16-18, 971-997.
[5]Fattore, M., & Bruggemann, R. (2017). Partial order concepts in applied sciences. Cham: Springer International Publishing.
[6]Hart, E. W., & Sandefur, J. (2017). Teaching and learning discrete mathematics worldwide: Curriculum and research. Springer.
[7]Ouvrier-Buffet, C. (2020). Discrete mathematics teaching and learning. In Encyclopedia of mathematics education. Cham: Springer International Publishing. 227-233.
[8]Heckmann, T., Schwanghart, W., & Phillips, J. D. (2015). Graph theory—Recent developments of its application in geomorphology. Geomorphology. 243, 130-146.
[9]Shevtsova, M., Kanel-Belov, A., & Golafshan, M. (2023). An Indirect Method for Solving Combinatorial Problems. arXiv preprint arXiv:2302.09761.
[10]Kaplansky, I. (2020). Set theory and metric spaces. American Mathematical Society. Vol. 298.
[11]Bradford, M., Muntean, C., & Pathak, P. (2014). An analysis of flip-classroom pedagogy in first year undergraduate mathematics for computing. In 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, Madrid, Spain, October 22-25, 1-5.
[12]McMaster, K., Anderson, N., & Rague, B. (2007). Discrete math with programming: better together. ACM SIGCSE Bulletin. 39(1), 100-104.
[13]Liu, Y. A., & Castellana, M. (2021). Discrete math with programming: A principled approach. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, Virtual Event USA March. 13 – 20,1156-1162.
[14]Brüggemann, R., Carlsen, L., Voigt, K., & Wieland, R. (2014). PyHasse software for partial order analysis: Scientific background and description of selected modules. Multi-indicator systems and modelling in partial order. 389-423.
[15]O'Regan, G. (2021). Guide to discrete mathematics. Springer International Publishing.
[16]Fuchs, L. (2011). Partially ordered algebraic systems, Courier Corporation. Vol. 28.