Formation of Models for Registering Systemic Processes in The Digital Educational Environment of the University Based on Log File Analysis
Abstract
It has been demonstrated that technologies and methods of intelligent data analysis (IDA) in the educational domain, particularly based on the analysis of digital traces (DT) of students, offer substantial opportunities for analyzing student activities. Notably, the DT of students are generated both during remote learning sessions and during blended learning modes. By applying IDA methods to DT, one can obtain information that is beneficial for both the educator in a specific discipline and for the educational institution's management. Such information might pertain to various aspects of the functioning of the digital educational environment (DEE) of the institution, such as: the student's learning style; individual preferences; the amount of time dedicated to a specific task, among others. An algorithm has been proposed for constructing a process model in the DEE based on log analysis within the DEE. This algorithm facilitates the description of a specific process in the DEE as a hierarchy of foundational process elements. Additionally, a model based on cluster analysis methods has been proposed, which may prove beneficial for analyzing the registration logs of systemic processes within the university's DEE. Such an analysis can potentially aid in detecting anomalous behavior of students and other individuals within the university's DEE. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.References
REFERENCES
Surmelioglu, Y., & Seferoglu, S. S. (2019). An Examination of Digital Footprint Awareness and Digital Experiences of Higher Education Students. World Journal on Educational Technology: Current Issues, 11(1), 48-64.
McDermot, M. (2018). Digital footprints: Creation, implication, and higher education. FDLA Journal, 3(1), 11.
Zhang, H. Z., Xie, C., & Nourian, S. (2018). Are their designs iterative or fixated? Investigating design patterns from student digital footprints in computer-aided design software. International Journal of Technology and Design Education, 28, 819-841.
Songsom, N., Nilsook, P., Wannapiroon, P., Chun Che Fung, L. & Wong, K. (2019). System Architecture of a Student Relationship Management System using Internet of Things to collect Digital Footprint of Higher Education Institutions. International Journal of Emerging Technologies in Learning (iJET), 14(23), 125-140. Kassel, Germany: International Journal of Emerging Technology in Learning. Retrieved October 3, 2023 from https://www.learntechlib.org/p/217252/.
Abdulmohsen A. Data Mining in Education. (IJACSA) International Journal of Advanced Computer Science and Applications, 2016, vol. 7, no. 6, pp. 456-461.
Angeli, C., Howard, S., Ma, J., Yang, J., Kirschner, P.A. Data mining in educational technology classroom research: can it make a contribution? Computers & Education, 2017. 113, pp. 226–242.
Baker R., Yacef K. The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 2009, vol. 1, no. 1, pp. 3–17. DOI: 10.5281/ZENODO.3554657.
Bowers A. J., Sprott R., Taff S. A. Do we know who will drop out? A review of the predictors of dropping out of high school: Precision, sensitivity and specificity. The High School Journal, 2012, vol. 96, no. 2, pp. 77–100. DOI: 10.1353/hsj.2013.0000.
Bowers A. J. Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment Research and Evaluation, 2010, vol. 15. Article 7. DOI: 10.7275/r4zq-9c31.
Ezekiel U. O., Mogorosi, M. Educational Data Mining for Monitoring and Improving Academic Performance at University Levels. (IJACSA) International Journal of Advanced Computer Science and Applications, 2020, vol. 11, no. 11. pp. 570–591.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 2014, vol. 41(4), pp. 1432-1462.
R. Zamora-Musa and J. Velez. Use of Data Mining to Identify Trends between Variables to Improve Implementation of an Immer-sive Environment. Journal of Engineering and Applied Sciences, 2017, vol. 12, no. 22, pp. 5944-5948.
Rawad C., Rémi B. Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC. International Review of Research in Open and Distributed Learning, 2020, vol. 21, no. 40.
Razaque, Abdul & Alajlan, Abrar. Supervised Machine Learning Model-Based Approach for Performance Prediction of Students. Journal of Computer Science, 2020, 16, pp. 1150-1162.
Siemens G., Baker R. S. J. d. Learning analytics and educational data mining: towards communication and collaboration / LAK’12. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. New York: Association for Computing Machinery, 2012, pp. 252–254. DOI: 10.1145/2330601.2330661.
Stauffer M. Laravel: Up & Running: A Framework for Building Modern PHP Apps 2nd Edition, O’REILLY, 2019.
Rudometkina, M. N. (2014). Algoritm formirovaniya predikatnoy modeli gibkogo mnogovariantnogo protsessa na osnove analiza ego log-faylov. Fundamentalnyie issledovaniya, (11-1), 39-45.
Prewett, J. E. (2003, June). Analyzing cluster log files using logsurfer. In Proceedings of the 4th Annual Conference on Linux Clusters. Citeseer.
Chuah, E., Kuo, S. H., Hiew, P., Tjhi, W. C., Lee, G., Hammond, J., ... & Browne, J. C. (2010, December). Diagnosing the root-causes of failures from cluster log files. In 2010 International Conference on High Performance Computing (pp. 1-10). IEEE.
Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection. computers & security, 79, 94-116.
Goswami, A., & Polly, P. D. (2010). Methods for studying morphological integration and modularity. The Paleontological Society Papers, 16, 213-243.
Tsibulya, A. N., & NGIA, H. M. (2016). Algoritm analiza zhurnala registratsii sistemnyih protsessov sistemyi elektronnogo dokumentooborota s ispolzovaniem metoda klasterizatsii sotsialьnyih setey. Informatsionnyie sistemyi i tehnologii, 94(2), 148.
V. Lakhno, V. Malyukov, B. Akhmetov, B. Yagaliyeva, O. Kryvoruchko and A. Desiatko, "University Distributed Computer Network Vulnerability Assessment," 2023 IEEE International Conference on Smart Information Systems and Technologies (SIST), Astana, Kazakhstan, 2023, pp. 141-144, doi: 10.1109/SIST58284.2023.10223501.
Lakhno, V. et al. (2023). The Model of Server Virtualization System Protection in the Educational Institution Local Network. In: Shakya, S., Papakostas, G., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-99-0835-6_33
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Electronics and Telecommunications
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on https://creativecommons.org/licenses/by/4.0/.
2. Author’s Warranties
The author warrants that the article is original, written by stated author/s, has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author/s. The undersigned also warrants that the manuscript (or its essential substance) has not been published other than as an abstract or doctorate thesis and has not been submitted for consideration elsewhere, for print, electronic or digital publication.
3. User Rights
Under the Creative Commons Attribution license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions: 1. they must attribute the contribution in the manner specified by the author or licensor, 2. they may alter, transform, or build upon this work, 3. they may use this contribution for commercial purposes.
4. Rights of Authors
Authors retain the following rights:
- copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in own future works, including lectures and books,
- the right to reproduce the article for own purposes, provided the copies are not offered for sale,
- the right to self-archive the article
- the right to supervision over the integrity of the content of the work and its fair use.
5. Co-Authorship
If the article was prepared jointly with other authors, the signatory of this form warrants that he/she has been authorized by all co-authors to sign this agreement on their behalf, and agrees to inform his/her co-authors of the terms of this agreement.
6. Termination
This agreement can be terminated by the author or the Journal Owner upon two months’ notice where the other party has materially breached this agreement and failed to remedy such breach within a month of being given the terminating party’s notice requesting such breach to be remedied. No breach or violation of this agreement will cause this agreement or any license granted in it to terminate automatically or affect the definition of the Journal Owner. The author and the Journal Owner may agree to terminate this agreement at any time. This agreement or any license granted in it cannot be terminated otherwise than in accordance with this section 6. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
7. Royalties
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by the Journal Owner or its sublicensee.
8. Miscellaneous
The Journal Owner will publish the article (or have it published) in the Journal if the article’s editorial process is successfully completed and the Journal Owner or its sublicensee has become obligated to have the article published. Where such obligation depends on the payment of a fee, it shall not be deemed to exist until such time as that fee is paid. The Journal Owner may conform the article to a style of punctuation, spelling, capitalization and usage that it deems appropriate. The Journal Owner will be allowed to sublicense the rights that are licensed to it under this agreement. This agreement will be governed by the laws of Poland.
By signing this License, Author(s) warrant(s) that they have the full power to enter into this agreement. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
