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Automated student knowledge testing and control system ZELIS

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Category: Content №6 2024

Last Updated on 28 December 2024

Published on 30 November -0001

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Authors:

O.Zelensky*, orcid.org/0000-0001-8780-587X, State University of Economics and Technology, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.Lysenko, orcid.org/0000-0002-5200-1211, State University of Economics and Technology, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (6): 193 - 199

https://doi.org/10.33271/nvngu/2024-6/193

Abstract:

Purpose. To substantiate the methodology and develop the software of the automated system of testing and monitoring the knowledge of ZELIS students.

Methodology. Examination papers contain text, tables, pictures, formulas, etc., that is, all the possibilities of the MS WORD editor. Questions and answer options are generated randomly. As the operation showed, the most effective number is 30 questions and 5 answers to them. When working with forms, a large number of people are tested. Each form is scanned to receive a raster file in *.jpg format. To recognize these files, a mathematical apparatus has been developed that accurately determines the number of the correct answer based on the form. In online mode, students receive input data through the Internet, which is relevant in the conditions of martial law, as well as when testing students’ knowledge during the educational process without additional use of laboratory time.

Findings. The ZELIS software is updated in the Visual Studio 2019 environment in the C# programming language using the MS ACCESS or SQL SERVER DBMS, as well as the FireBase RealTime DataBase cloud DBMS.

Originality. Input information for creating tests comes in a single RTF format that allows you to use tables, figures, formulas, etc. The system also provides the possibility of simultaneous testing of a large number of persons. This process uses proprietary recognition algorithms and takes only a few minutes to process a large number of forms.

Practical value. The article describes the automated system of testing and monitoring the knowledge of ZELIS students, which was developed by the authors. The system was developed at the State University of Economics and Technology and has been operating since 2015. Approximately 250 exams are conducted in one online paper-based testing session, with approximately 1,200 students participating.

Keywords: knowledge testing, ZELIS, form, algorithm, RealTime DataBase, DBMS

References:

1. Drissi, S., & Amirat, A. (2016). An adaptive E-learning system based on student’s learning styles: An empirical study. International Journal of Distance Education Technologies, 14, 34-51. https://doi.org/10.4018/IJDET.2016070103.

2. Ethink (2018). Three Benefits of Adaptive Learning in Your LMS. Retrieved from https://ethinkeducation.com/blog/3-benefits-utilizing-adaptive-learning-lms.

3. Moodle vs Google Classroom: Key Differences You Should Know. (n.d.). Retrieved from https://www.teachfloor.com/blog/moodle-vs-google-classroom.

4. Harati, H., Yen, C. J., Tu, C. T., Cruickshank, B., & Armfield, S. W. (2020). Online adaptive learning: A study of score validity of the adaptive self-regulated learning model. International Journal of Web-Based Learning and Teaching Technologies, 15, 18-35. https://doi.org/10.4018/IJWLTT.2020100102.

5. Bergey, B. W., Ketelhut, D. J., Liang, S., Natarajan, U., & Karakus, M. (2015). Scientific inquiry self-efficacy and computer game self-efficacy as predictors and outcomes of middle school boys’ and girls’ performance in a science assessment in a virtual environment. Journal of Science Education and Technology, 24, 696-708. https://doi.org/10.1007/s10956-015-9558-4.

6. Chiranjeevi, K., & Jena, U. (2018). SAR image compression using adaptive differential evolution and pattern search based K-means vector quantization. Image Analysis and Stereology, 37, 35-54. https://doi.org/10.5566/ias.1611.

7. Hesterman, D. (2017). Report on Intensive Mode Delivery in Engineering, Computer Science, and Mathematics. Retrieved from http://www.ecm.uwa.edu.au/__data/assets/pdf_file/0009/2700846/Hesterman-2015-UWA-ECM-Report-on-intensive-mode-delivery.pdf.

8. Hussain, A. J., Al. Fayadh, A., & Radi, N. (2018). Image compression techniques: a survey in lossless and lossy algorithms. Neurocomputing, 300, 44-69. https://doi.org/10.1016/j.neucom.2018.02.094.

9. Jarno, M., & Bormin, H. (2012). Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length. IEEE Geoscience and Remote Sensing Letters, 9, 1118-1121. https://doi.org/10.1109/LGRS.2012.2191531.

10. Latham, G., Seijts, G., & Slocum, J. (2016). The Goal-setting and goal orientation labyrinth. Organizational Dynamics, 45, 271-277. https://doi.org/10.1016/j.orgdyn.2016.10.001.

11. Li, J., & Liu, Z. (2019). Multispectral transforms using convolution neural networks for remote sensing multispectral image compression. Remote Sensing, 11, 759-779. https://doi.org/10.3390/rs11070759.

12. Murray, M. C., & Pérez, J. (2015). Informing and performing: A study comparing adaptive learning to traditional learning. Informing Science: the International Journal of an Emerging Transdiscipline, 18, 111-125. Retrieved from http://www.inform.nu/Articles/Vol18/ISJv18p111-125Murray1572.pdf.

13. Nussbaumer, A., Hillemann, E., Gütl, C., & Albert, D. (2015). A Competence-based service for supporting self-regulated learning in virtual environments. J. Learn. Anal, 2, 101-133. https://doi.org/ 10.18608/jla.2015.21.6.

14. Panadero, E. (2017). A Review of Self-Regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422.

15. Sabourin, J., Mott, B., & Lester, J. (2013). Discovering behavior patterns of self-regulated learners in an inquiry-based learning environment. In Lane, H. C., Yacef, K., Mostow, J., Pavlik, P. (Eds.). Lecture Notes in Computer Science: Artificial Intelligence in Education, (pp. 209-218). Berlin/Heidelberg: Springer.

16. Shi, C., Zhang, J., & Zhang, Y. (2016). Content-based onboard compression for remote sensing images. Neurocomputing 191, 330-340. https://doi.org/10.1016/j.neucom.2016.01.048.

17. Villegas-Ch, W., Roman-Cañizares, M., Jaramillo-Alcázar, A., & Palacios-Pacheco, X. (2020). Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment. Applied Sciences, 10, 7016. https://doi.org/10.3390/app10207016.

18. Lefei, Z., Liangpei, Z., & Dacheng, T. (2015). Compression of hyperspectral remote sensing images by tensor approach. Neurocomputing, 147, 358-363. https://doi.org/10.1016/ j.neucom.2014.06.052.

19. Zemliachenko, A. N., Abramov, S. K., Lukin, V. V., Vozel, B., & Chehdi, K. (2015). Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters. Journal of Applied Remote Sensing, 9, 095066. https://doi.org/10.1117/ 1.JRS.9.095066.

20. Zhan, X., Zhang, R., Yin, D., & Huo, C. (2013). SAR image compression using multiscale dictionary learning and sparse representation. IEEE Geoscience and Remote Sensing Letters, 10, 1090-1094. https://doi.org/10.1109/LGRS.2012.2230394.

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