Effectiveness of Digital Simulation-Based Learning Approach in Optimizing Students’ Understanding of Queueing Models Using Real-Life Data

Authors

  • Arin Berliana Angrenani University of Jember
  • Dian Kurniati University of Jember
  • Susi Setiawani University of Jember
  • Rafiantika Megahnia Prihandini University of Jember
  • Ngizatul Afifah University of Jember

DOI:

https://doi.org/10.12962/j24775401.ijcsam.v11i2.8850

Abstract

This study examines indications of the effectiveness of a digital simulation-based learning approach in supporting students’ understanding of queueing models using real-life data. Aquasi-experimental one-group pretest–posttest design, supported by qualitative interview data, was conducted with 31 undergraduate mathematics education students at the University of Jember. The ExtendSim software was used to create interactive queueing simulations that allowed students to explore parameters such as arrival rate, service rate, and waiting time. Validity and reliability tests were conducted using item–total (Pearson) correlations and Cronbach’s alpha, with results indicating high validity (r > 0.5, p < 0.05) and high internal consistency (a > 0.80). A paired ttest showed a statistically significant increase in scores within this sample (t = 8.89, p < 0.001). Students’ perceptions of the simulation were highly positive, with an average Likert score of 3.23 (very high). Qualitative interviews further indicated that the simulations helped students visualize queue dynamics and relate theoretical concepts to real-life contexts. There were also indications of increased motivation, engagement, and computational thinking skills; however, these findings are limited by the single-site sample and the one-group study design.

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Published

2025-12-15

How to Cite

Angrenani, A. B., Kurniati, D., Setiawani, S., Prihandini, R. M., & Afifah, N. (2025). Effectiveness of Digital Simulation-Based Learning Approach in Optimizing Students’ Understanding of Queueing Models Using Real-Life Data. (IJCSAM) International Journal of Computing Science and Applied Mathematics, 11(2), 83–86. https://doi.org/10.12962/j24775401.ijcsam.v11i2.8850

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