Intellectual Mathematical Support Software and Inner Architecture of LMS MAI CLASS.NET

Distance education prove to be effective in improving the learning and teaching environment. One of the main advantages of distance learning is that web-based courses can be taken anytime and anywhere. The implementation of an e-learning management system (LMS) requires not only good and fast hardware, but also the use of modern software technologies and architectural solutions. This article outlines the main ways of forming the LMS architecture based on a microservice approach, which allows the achievement of high performance and fault tolerance. A distinctive feature of the CLASS.NET system is the presence of a special mathematical software package that allows the optimization of educational processes and tasks (such as students tests generation, students progress analysis, knowledge level assessment, task difficulty analysis, personal learning curve planning). The process of interaction between the LMS system and mathematical software package, as well as the main ways of forming such software as completely independent applications for their further integration into other learning management systems, are thoroughly described. The efficiency of the microservice architecture in terms of scaling, performance and general behavior in case of critical errors in comparison with other systems based on classical architectural approaches is shown. The algorithm of predicting the time a student spends to answer the tasks, which is included in the mathematical software package, is considered.

Keywords

learning management system; LMS, e-learning; adaptive learning curve; microservices.

References

1. Naumov A.V., Dzhumurat A.S., Inozemtsev A.O. [Distance Learning System for Mathematical Disciplines CLASS.NET]. Herald of Computer and Information Technologies, 2014, vol. 1, no. 10, pp. 36-40. (in Russian)
2. Kibzun A.I., Karolinskaya S.N., Shayukov R.I. [A Remote Study System for Mathematical Disciplines in the University]. Herald of Computer and Information Technologies, 2006, vol. 1, no. 4, pp. 29-36. (in Russian)
3. Kibzun A.I., Zharkov E.A. Two Algorithms for Estimating Test Complexity Levels. Automation and Remote Control, 2017, vol. 78, no. 12, pp. 2165-2177.
4. Naumov A.V., Mkhitaryan G.A. On the Problem of Probabilistic Optimization for Tests within the Time-Limit. Automation and Remote Control, 2016, vol. 77, no. 9, pp. 1612-1621. DOI: 10.1134/S0005117916090083
5. Rui Chen, Shanshan Li, Zheng Li. From Monolith to Microservices: A Dataflow-Driven Approach. 24th Asia-Pacific Software Engineering Conference (APSEC), Nanjing, 2017, pp. 466-475.
6. Newman S. Building Microservices: Designing Fine-Grained Systems, Sebastopol, O'Reilly Media, 2015.
7. Villamizar M., Garces O.,Lang M. Cost Comparison of Running Web Applications in the Cloud Using Monolithic, Microservice, and AWS Lambda Architectures. Service Oriented Computing and Applications, 2017, vol. 11, no. 2, pp. 233-247.
8. Richards M. Microservices vs. Service-Oriented Architecture, Sebastopol, O'Reilly Media, 2015.
9. Vernon V. Implementing Domain-Driven Design, Massachusetts, Addison-Wesley Professional, 2013.
10. Richardson C. Microservice Patterns, New York, Manning, 2017.
11. Apache JMeterTM (2021). Available at: https://jmeter.apache.org/ (accessed 18 April 2021).
12. Kumari S., Rath S. Performance Comparison of SOAP and REST Based Web Services for Enterprise Application Integration. International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, 2015, pp. 1656-1660.
13. Ueda T., Nakaike T., Ohara M. Workload Characterization for Microservices. IEEE International Symposium on Workload Characterization (IISWC), Rhode Island, 2016, pp. 1-10.
14. XGBoost Homepage (2021). Available at: https://neerc.ifmo.ru/wiki/index.php?title=XG-\Boost (accessed 18 April 2021).
15. Van der Linden W.J. Conceptual Issues in Response-Time Modeling, Philadelphia, Law School Admission Council, 2008.
16. Van der Linden W.J. A Bivariate Lognormal Response-Time Model for the Detection of Collusion Between Test Takers. Journal of Educational and Behavioral Statistics, 2009, vol. 34, no. 3, pp. 378-394.
17. Liao D. Modeling the Speed-Accuracy-Difficulty Interaction in Joint Modeling of Responses and Response Time: Dissertation, Maryland, University of Maryland, 2018. DOI: 10.13016/M25H7BX5P
18. Rushkin I., Chuang I., Tingley D. Modelling and Using Response Times in Online Courses. Journal of Learning Analytics, 2019, vol. 6, no. 3, pp. 76-89.