E-Training Course Test Apps as a Media for Measuring Students' Academic Abilities

Authors

  • Siti Fatimah Universitas Indo Global Mandiri

Keywords:

E-Training application, Prototype model implementation, Educational technological advancements

Abstract

This study introduces an E-Training application developed to overcome limitations in traditional classroom settings where time constraints and delayed material delivery hinder student development. E-Training, utilizing information technology in education, responds to the evolving landscape of technological advancements and the global need for IT-based teaching concepts. The implementation of this system follows the prototype model, emphasizing iterative development stages, enhancing understanding, reducing risks, and ensuring cost-effective solutions. The research outlines the comprehensive design process involving UML diagrams, including use case, activity, sequence, and class diagrams, to establish a versatile educational platform. It details the system's structure involving three key actors: admin, student, and teacher. Moreover, the study elaborates on the testing phases encompassing black box and white box methodologies. Black box tests validate successful functionalities like logins, data input, and storage across user roles. Meanwhile, white box testing focuses on logic verification, ensuring accurate computations and display of student scores and rankings. Overall, this E-Training application emerges as a solution bridging the limitations of traditional classrooms, offering an adaptable environment for learning and teaching. Its systematic development and successful testing signify a substantial stride towards enhancing educational accessibility and effectiveness in the digital age.

Downloads

Download data is not yet available.

References

R. E. Matete, A. E. Kimario, and N. P. Behera, “Review on the use of eLearning in teacher education during the coronavirus disease (COVID-19) pandemic in Africa,” Heliyon, vol. 9, no. 2, p. e13308, 2023, doi: 10.1016/j.heliyon.2023.e13308.

A. H. Obidat, M. Alquraan, and M. H. Obeidat, “Data on factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan,” Data Br., vol. 33, p. 106402, 2020, doi: 10.1016/j.dib.2020.106402.

K. Quimby, N. Spitzer, K. F. Doré, and J. Kawatu, “An eLearning series for staff working in Title X-funded settings: An effort to disseminate national family planning recommendations,” Contraception, vol. 120, p. 109903, 2023, doi: 10.1016/j.contraception.2022.10.005.

M. Riesener et al., “A model for dependency-oriented prototyping in the agile development of complex technical systems,” Procedia CIRP, vol. 84, no. March, pp. 1023–1028, 2019, doi: 10.1016/j.procir.2019.04.196.

S. Hwan Kim, J. Jin, M. Sevinchan, and A. Davies, “How do automated reasoning features impact the usability of a clinical task management system? Development and usability testing of a prototype,” Int. J. Med. Inform., vol. 174, no. April, p. 105067, 2023, doi: 10.1016/j.ijmedinf.2023.105067.

G. Bergström et al., “Evaluating the layout quality of UML class diagrams using machine learning,” J. Syst. Softw., vol. 192, p. 111413, 2022, doi: 10.1016/j.jss.2022.111413.

H. Wu, “QMaxUSE: A new tool for verifying UML class diagrams and OCL invariants,” Sci. Comput. Program., vol. 228, p. 102955, 2023, doi: 10.1016/j.scico.2023.102955.

P. Danenas, T. Skersys, and R. Butleris, “Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams,” Data Knowl. Eng., vol. 128, no. February, p. 101822, 2020, doi: 10.1016/j.datak.2020.101822.

Meiliana, I. Septian, R. S. Alianto, Daniel, and F. L. Gaol, “Automated Test Case Generation from UML Activity Diagram and Sequence Diagram using Depth First Search Algorithm,” Procedia Comput. Sci., vol. 116, pp. 629–637, 2017, doi: 10.1016/j.procs.2017.10.029.

Z. Daw and R. Cleaveland, “Comparing model checkers for timed UML activity diagrams,” Sci. Comput. Program., vol. 111, no. P2, pp. 277–299, 2015, doi: 10.1016/j.scico.2015.05.008.

F. Chen, L. Zhang, X. Lian, and N. Niu, “Automatically recognizing the semantic elements from UML class diagram images,” J. Syst. Softw., vol. 193, p. 111431, 2022, doi: 10.1016/j.jss.2022.111431.

D. Felicio, J. Simao, and N. Datia, “Rapitest: Continuous black-box testing of restful web apis,” Procedia Comput. Sci., vol. 219, no. 2022, pp. 537–545, 2023, doi: 10.1016/j.procs.2023.01.322.

P. L. Fung et al., “Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration,” J. Aerosol Sci., vol. 152, no. September 2020, 2021, doi: 10.1016/j.jaerosci.2020.105694.

Published

2023-01-31

How to Cite

Fatimah, S. (2023). E-Training Course Test Apps as a Media for Measuring Students’ Academic Abilities. Journal of Computer Science Application and Engineering (JOSAPEN), 1(1), 17–22. Retrieved from https://journal.lenterailmu.com/index.php/josapen/article/view/9

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.