Preprint
Article

This version is not peer-reviewed.

Design and Implementation of an Enhanced Learning Management System: Addressing Modern Academic Challenges

Submitted:

19 May 2025

Posted:

20 May 2025

You are already at the latest version

Abstract
The entire development and requirement engineering process of a modern Learning Management System (LMS) geared for an advanced technologically equipped educational environment is described. The proposed LMS aims to move beyond existing communication, accessibility, and academic collaboration restrictions by integrating new features such as real-time voice/video chat, virtual study rooms, personalize timetables, and enhanced authentication mechanisms. The design of the system includes key features including a centralized management interface for course materials, student discussion forums, and calendar integration with external services like Google Calendar. Through formal requirement elicitation involving questionnaires and interviews, stakeholder feedback was gathered to determine the lack in current LMS platforms, e.g., ineffective chat features, lack of collaboration features, and privacy concerns. The solution is centered on usability, performance efficiency, interoperability, and security as per ISO 25010:2011 quality standards. Fifteen functional requirements and ten requirements of quality were drawn up to satisfy the users as well as institutional guidelines. Limitations such as notification limits, offline storage limits, and safe login limits were applied to maintain stability of the system and satisfaction among users. A hierarchical goal documentation and mapping method, supported by AND/OR diagrams, was used to formally elaborate user needs into executable system goals. Special focus was given to the design of a more advanced authentication system, supporting biometric login, SSO integration, and secure password recovery. Despite challenges like notification overload and third-party API dependence, this LMS design has the potential to significantly enhance the delivery of education, support collaborative learning, and allow for continuous improvement through iterative user feedback and future feature enrichment.
Keywords: 
;  ;  ;  

1. Introduction

The revolution in learning processes through digital means has stimulated the propagation of Learning Management Systems (LMS), revolutionizing how institutions of higher education deliver, structure, and evaluate learning outcomes. Now, LMS platforms are key centers of technology that engage learners and teachers in exchange via centralized, dynamic, and adaptive digital environments [1,2]. While even greater reliance on hybrid and online learning modalities has been set, particularly following the COVID-19 pandemic, the requirements on LMS systems have changed from simple content repositories to intelligent, social, and secure platforms, enabling multilateral user needs. The present project introduces a reengineered LMS that endeavors to surmount current limitations and meet the evolving requirements of a global academic community. The proposed system emphasizes robust authentication mechanisms, real-time communication tools with improved functionality, user-friendly interface design, and improved mobile responsiveness. These enhancements are driven by demands gathered from the end users—students and faculty members—via structured questionnaires and one-on-one interviews, combining system development with user-centered design principles [3,4,5]. The necessity for the next-generation LMS is driven by inherent flaws in existing systems, such as poor authentication protocols, no mobile support, fragmented collaboration tools, and limited calendar integrations [6]. To address these shortcomings, the LMS under development incorporates group lounges with video/audio conferencing, individual calendars with external synchronization (e.g., Google Calendar), biometric and multi-factor authentication, and productivity tools like discussion forums and virtual whiteboards. These features are aimed at enhancing productivity, tightening security, and promoting inclusive learning for heterogeneous user profiles.
Moreover, the proposed LMS architecture complies with international quality standards—i.e., ISO 25010:2011—to ensure system reliability, usability, performance efficiency, and maintainability. For example, Single Sign-On (SSO) and biometric login features embedded directly ensure secure and seamless access, whereas notification control functionality and offline data caching try to balance user interaction with system performance [6].
However, the implementation and design of such an overarching LMS also have some drawbacks. Notification fatigue, internet reliance, user adaptability, and third-party tool integration privacy concerns need to be seriously addressed. In this direction, the LMS design features a multi-layered security system and modular architecture that facilitate continuous updates as per GDPR and other data protection regulations [7].
Through incorporating stakeholder feedback and best practices of modern requirement engineering methods, this LMS project is a forward-thinking solution for enhancing educational delivery and student engagement. Not only does it address present education requirements but also it has the capability to expand as new pedagogical and technological trends emerge.

2. Literature Review

Learning Management Systems (LMSs) have emerged as an essential backbone infrastructure in contemporary higher education, particularly with the global shift toward blended and online learning spaces [8,9]. They are websites that become hubs for academic content, communication between students and instructors, posting assignments, and overall course management. However, as the requirement elicitation phase of this project has highlighted, current LMS implementations are behind in fundamental areas such as usability, real-time communication functionalities, mobile-friendliness, and seamless integration with other systems. This literature review integrates contemporary academic and technical research to inform the development of a functionally robust and user-friendly LMS, with special focus on major themes such as student engagement, authentication security, communication tools, and personalized learning features.
Usability is a salient quality attribute in the design of LMS, as defined in the ISO 25010 standard, and it encompasses operability, learnability, and user error prevention. According to the author [10], we argue that user satisfaction and ease of navigation are strongly correlated with student engagement and overall system adoption. Similarly, [11] demonstrated that mobile-first and adaptive user interfaces greatly enhance the user experience, especially for learners accessing the platform through smartphones or tablets. These findings confirm the utilization of customizable interface elements in the LMS design, such as resizable fonts, themes, and personalized calendar functions, which enable the system to accommodate a great diversity of user preferences and abilities [12].
Due to the increasing cybersecurity attacks, strong authentication mechanisms need to be utilized. Best practices today include multi-factor authentication (MFA), biometric authentication (facial recognition or fingerprinting), and Single Sign-On (SSO) integrations with large providers like Google and Microsoft [13]. As much as SSO solutions are convenient, they can also potentially introduce vulnerabilities if not correctly mapped, as indicated by [14]. The proposed LMS transcends this with the combination of SSO flexibility and added passkey and biometric authentication security, with adherence to GDPR guidelines and ISO/IEC 27001 standards for data security.
Another cornerstone of a successful LMS is proper communication tools. To support synchronous and asynchronous learning, platforms must offer tools such as discussion boards, live chat, video conferencing, and shared whiteboards. Literature confirms that these tools facilitate increased peer communication and collaborative learning [15,16,17] González-González et al. (2022) proved that LMS platforms incorporating Zoom or Microsoft Teams for real-time communication had more satisfied learners and less learner attrition. In accordance with this, the recommended introduction of virtual study rooms and voice/video chat capabilities natively will offer a seamless user experience without relying too much on third-party applications.
Time management skills, particularly calendar integration, are critical to academic success in online learning environments. According to the author [18] discovered that effective time management is a predictor of student performance. Integration with external applications like Google Calendar enhances the visibility of assignments, meetings, and deadlines. These integrations must retain user customization and respect privacy guidelines. According to the author [19] propose standard formats like CSV or ICS for importing and exporting data, which is compatible with your system's approach towards calendar synchronization while maintaining custom options like color-coding and personalized views.
User and academic, as well as individual data privacy and security, is at the core of trust in LMS systems. Since most systems use third-party APIs and cloud hosting, it is crucial to follow data minimization, data encryption, as well as user consent policies [20]. Safe recovery processes for passwords, unusual login alerts, and compliance with standards such as GDPR and ISO/IEC 29100:2011 are essential components of a secure design. By incorporating advanced recovery processes and activity-based alerts, the proposed LMS upholds these principles, protecting user data at every touchpoint.
Quality and performance consideration is just as important. ISO 25010 provides attributes such as performance efficiency, reliability, maintainability, and fault tolerance. According to the author [21] mention the aspect that small response times, high system availability (99.9% and above), and frequent stress testing significantly affect user satisfaction and system scalability. The LMS system covered in this project incorporates these aspects by offering module-level updates and applying performance requirements for catering to thousands of users concurrently without breaks. In conclusion, the Learning Management System as proposed has evidence and best practice core in usability, security, communication, and customization. The aspect that the system is centered on secure authentication, real-time communication interfaces, and usability ensures its applicability in the modern education industry. Future revisions must include user feedback updates as well as incorporation of adaptive technology, such as machine learning, to ensure personalized learning channels and continuous improvement of the system[22].

3. Methodology

For the purpose of establishing and defining the requirements for the proposed Learning Management System (LMS), both qualitative and quantitative requirement elicitation techniques were employed, i.e., questionnaires and interviews. These methods were selected with the aim of eliciting varied user expectations, functional requirements, as well as system limitations from students and teachers, who are the primary end users of the system[23,24,25].

3.1. Questionnaire-Based Data Collection

The first elicitation technique used was a structured questionnaire, created to gather high volumes of response in an effective way. The questionnaire consisted of multiple-choice questions and open-ended questions with the aim of measuring user satisfaction with the current LMS, identifying areas of pain, and gaining ideas for improvements. It was distributed online to students and academic staff members to facilitate anonymous and honest response. This methodology proved particularly effective in involving large numbers of people in a cost-effective and efficient way[26,27,28]. Some of the key takings from the questionnaire were strong user demand for an improved communications module with voice and video chat, the need to integrate outside calendars, and enhanced collaboration tools such as study lounges to enable group study.

3.2. Interview-Based Elicitation

In addition to the questionnaire, one-on-one interviews were conducted to gather more in-depth input and explore complex user requirements that might not surface via written input. A semi-structured interview method was employed, providing both question consistency and room for follow-up questions. The interview touched on major areas of the LMS, such as the chat system, ease of use of the calendar, and authentication systems. Answers were documented and examined for recurring themes and to guide requirement prioritization[29]. The interviews indicated considerable user frustration with the existing authentication system and clarified the value of incorporating secure login alternatives such as biometric passkeys and Single Sign-On (SSO) capabilities. Students also indicated the need for an easier-to-use calendar interface and the ability to sync it with services such as Google Calendar.

3.3. Requirement Analysis and Goal Mapping

The results of both elicitation techniques were combined and utilized to develop a sophisticated goal documentation and mapping strategy that relates user requirements to system capability. Functional and quality requirements were then identified and mapped to explicit ISO25010:2011 quality characteristics such as usability, security, reliability, and maintainability. An AND/OR goal diagram was also constructed to graphically illustrate the relationship between high-level goals and related sub-goals[30]. This graph played a critical role in explaining dependencies as well as contradictions between system goals[30,31,32,33]. In general, this multi-method approach provided a rich description of the shortfalls of the current system and the users' expectations. It ensured the new LMS was designed from a user-centered perspective and hence became more relevant, usable, and effective for all stakeholders involved in academic activities.

3.4. Integration with Quality Standards and Industry Models

To ensure the LMS conformed to industry standards, the requirements were mapped against the ISO25010:2011 software quality model. Ten key quality attributes were defined, including usability, interoperability, security, maintainability, and performance efficiency. For instance, usability was addressed by offering an intuitive interface, while security requirements were addressed by incorporating features like multi-factor authentication and secure password recovery mechanisms. Each of the quality needs was justified based on user needs and system goals, supporting traceability and accountability through the development process. In addition, related constraints such as notification triggers, data storage capacity, session durations, and password reset guidelines were documented to assist with operational viability and adherence to institutional policy. Overall, this multi-method requirement elicitation method provided a balanced image of the faults in the current system and requirements from the users. By aligning questionnaire depth with interview breadth, the team was able to develop a holistic and user-focused set of system requirements. Using this approach ensured that the newly redesigned LMS would not just address existing issues but also incorporate forward-thinking innovations tailored to suit the evolving demands of its scholar community.

6. Discussion

The above Learning Management System (LMS) stands out from standard platforms like Moodle and Blackboard by integrating intelligent features that focus on personalization, accessibility, and real-time engagement. Unlike most traditional systems, the LMS provides state-of-the-art functionality like voice and video conferencing, customized study lounges, interactive whiteboards, and integration with external calendars—all of which are designed to enhance the overall learning experience.
One of the most significant advantages of this LMS is its use of Artificial Intelligence (AI) to streamline academic functions. For instance, predictive analytics can detect assignment bottlenecks or student disengagement and forecast these beforehand, allowing for timely intervention by instructors. Smart notification systems also give priority to notifications depending on urgency, mitigating the ubiquitous issue of notification fatigue that users cited as a drawback of outdated systems. These skills are consistent with educational technology futures as seen in 2024 where adaptive learning and automation are more demanded.
Requirements elicitation techniques—questionnaires and interviews—were very effective at capturing stakeholder expectations. These channels ratified the need for integrated study spaces, improved authentication mechanisms, and time management features. User feedback especially determined the inclusion of Single Sign-On (SSO) and biometric login functionality, significantly enhancing security and user convenience.
Yet, there are challenges too. Excessive reliance on technology, especially in regions with poor internet connection, can detract from accessibility. Where third-party services like Google Calendar and Microsoft Teams are incorporated, there are external dependencies that may have to be tracked and updated round the clock for maintaining system integrity. Data privacy issues and compliance with data protection laws like GDPR again highlight the importance of having robust data governance frameworks in place.
Despite these, the LMS meets ISO25010:2011 standards in a number of quality attributes including usability, security, reliability, and interoperability. This ensures not only a technically sufficient system but also one aligned with the academic goals of inclusivity, engagement, and efficiency.

7. Conclusion and Future Work

In short, this LMS is a forward-looking solution aimed at meeting the evolving needs of the modern educational institution. Through the incorporation of AI-driven analytics, collaboration tools, enhanced security, and mobile accessibility, it goes beyond the limitations of older systems to create a dynamic, student-centered environment. The system's ability to personalize learning experiences, automate instructor tasks, and facilitate real-time interaction paves the way for transformative educational outcomes.
The iterative design process, driven by stakeholder feedback and industry standards, ensures the system is not just functional but also adaptable. However, there are limitations to the LMS. Notification overload, learning curve, dependence on third-party tools, and data privacy concerns will have to be managed on an ongoing basis as the system evolves.
Future work will involve scaling the implementation to multiple institutions to test performance in diverse academic settings. One area of improvement will involve the integration of blockchain technology for secure credentialing, which will render academic records tamper-proof and globally verifiable. Machine learning models can also be expanded to offer automated grading, plagiarism detection, and content recommendation engines.

References

  1. Samala, A. D., Rawas, S., Criollo-C, S., Bojic, L., Prasetya, F., Ranuharja, F., & Marta, R. (2024). Emerging technologies for global education: A comprehensive exploration of trends, innovations, challenges, and future horizons. SN Computer Science, 5(8), 1-24. [CrossRef]
  2. Realpe-Muñoz, P., Collazos, C. A., Granollers, T., Muñoz-Arteaga, J., & Fernandez, E. B. (2017, September). Design process for usable security and authentication using a user-centered approach. In Proceedings of the XVIII International Conference on Human Computer Interaction (pp. 1-8).
  3. Ukeje, N., Gutierrez, J., & Petrova, K. (2024). Information security and privacy challenges of cloud computing for government adoption: a systematic review. International Journal of Information Security, 23(2), 1459-1475. [CrossRef]
  4. Elimadi, I., Chafiq, N., & Ghazouani, M. (2024). Artificial intelligence in the context of digital learning environments (DLEs): Towards adaptive learning. In Engineering applications of artificial intelligence (pp. 95-111). Cham: Springer Nature Switzerland.
  5. Saeed, S. (2016). Surveillance system concept due to the uses of face recognition application. Journal of Information Communication Technologies and Robotic Applications, 7(1), 17-22.
  6. Saeed, S. (2019). A conceptual system on ubiquitous cardiovascular health-care system (UCHS). SSUET, 9(1), 15-19.
  7. Saeed, S. (2018). Performance analysis of quality assurance due to the usage of two enterprise resource planning systems: Microsoft Dynamics AX and SAP. Mehran University of Engineering Journal (MUET), 37(2), 337-350.
  8. Saeed, S., Jhanjhi, N. Z., Naqvi, S. M. R., & Khan, A. (2022). Cost optimization of software quality assurance. In Deep learning in data analytics: Recent techniques, practices and applications (pp. 241–255).
  9. Saeed, S., Jhanjhi, N. Z., Naqvi, S. M. R., & Khan, A. (2022). Analytical approach for security of sensitive business cloud. In Deep learning in data analytics: Recent techniques, practices and applications (pp. 257–266).
  10. Clegg, K., Schubert, T. J., Block, R. C., Burke, F., Desai, N. R., Greenfield, R., ... & Jones, L. K. (2023). Translating Evidence-based Approaches into optimal Care for individuals at High-risk of ASCVD: Pilot testing of case-based e-learning modules and design of the TEACH-ASCVD study. Journal of Clinical Lipidology, 17(5), 592-601. [CrossRef]
  11. Al-Fraihat, D., Joy, M., Masa'deh, R. E., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in human behavior, 102, 67-86. [CrossRef]
  12. Ali, A., Alrasheedi, M., Ouda, A., & Capretz, L. F. (2015). A study of the interface usability issues of mobile learning applications for smart phones from the users perspective. arXiv preprint arXiv:1501.01875. [CrossRef]
  13. Zamora-Antuñano, M. A., Rodríguez-Reséndiz, J., Cruz-Pérez, M. A., Rodríguez Reséndíz, H., Paredes-García, W. J., & Díaz, J. A. G. (2021). Teachers’ perception in selecting virtual learning platforms: A case of mexican higher education during the COVID-19 crisis. Sustainability, 14(1), 195.
  14. Gopi, R., Sathiyamoorthi, V., Selvakumar, S., Manikandan, R., Chatterjee, P., Jhanjhi, N. Z., & Luhach, A. K. (2022). Enhanced method of ANN based model for detection of DDoS attacks on multimedia internet of things. Multimedia Tools and Applications, 1-19. [CrossRef]
  15. Chesti, I. A., Humayun, M., Sama, N. U., & Jhanjhi, N. Z. (2020, October). Evolution, mitigation, and prevention of ransomware. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
  16. Alkinani, M. H., Almazroi, A. A., Jhanjhi, N. Z., & Khan, N. A. (2021). 5G and IoT based reporting and accident detection (RAD) system to deliver first aid box using unmanned aerial vehicle. Sensors, 21(20), 6905.
  17. Babbar, H., Rani, S., Masud, M., Verma, S., Anand, D., & Jhanjhi, N. (2021). Load balancing algorithm for migrating switches in software-defined vehicular networks. Computational Materials and Continua, 67(1), 1301-1316. [CrossRef]
  18. Cahyanto, I., Madihah, H., Budiarso, I., Sutrisno, A., & Hidayat, T. (2024). Effectiveness Of Multifactor Authentication Technology For Protecting Student Privacy: A Systematic Literature Review. Edum Journal, 7(2), 253-269.
  19. Hu, X., Ng, J., Tsang, K. K., & Chu, S. K. (2020). Integrating mobile learning to learning management system in community college. Community College Journal of Research and Practice, 44(10-12), 722-737. [CrossRef]
  20. Daga, K., Viswanath, K. C., & Shankar, K. P. (2024, July). Single Sign-On and application integration using cloud Okta. In AIP Conference Proceedings (Vol. 3075, No. 1, p. 020085). AIP Publishing LLC.
  21. Chatterjee, P., Bose, R., Banerjee, S., & Roy, S. (2023). Enhancing data security of cloud based lms. Wireless Personal Communications, 130(2), 1123-1139.
  22. Marroquin, J. P., & Rodriguez, C. R. (2023, December). Quality Assessment Model Based on ISO/IEC 25010 for E-Learning Platforms in the Cloud and Based on Laravel. In 2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 751-757). IEEE.
  23. Zaman, N., Khan, A. R., & Salih, M. Designing of Energy aware Quality of Service (QoS) based routing protocol for Efficiency Improvement in Wireless Sensor Network (WSN).
  24. Khan, A., Jhanjhi, N. Z., Haji, D. H. T. B. A., & Omar, H. A. H. B. H. (2024). Internet of Things (IoT) impact on inventory management: A review. Cybersecurity measures for logistics industry framework, 224-247.
  25. Gouda, W., Almurafeh, M., Humayun, M., & Jhanjhi, N. Z. (2022, February). Detection of COVID-19 based on chest X-rays using deep learning. In Healthcare (Vol. 10, No. 2, p. 343). MDPI. [CrossRef]
  26. Chesti, I. A., Humayun, M., Sama, N. U., & Jhanjhi, N. Z. (2020, October). Evolution, mitigation, and prevention of ransomware. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-6). IEEE.
  27. Alferidah, D. K., & Jhanjhi, N. Z. (2020, October). Cybersecurity impact over bigdata and iot growth. In 2020 International Conference on Computational Intelligence (ICCI) (pp. 103-108). IEEE.
  28. Lim, M., Abdullah, A., & Jhanjhi, N. Z. (2021). Performance optimization of criminal network hidden link prediction model with deep reinforcement learning. Journal of King Saud University-Computer and Information Sciences, 33(10), 1202-1210. [CrossRef]
  29. Shahid, H., Ashraf, H., Javed, H., Humayun, M., Jhanjhi, N. Z., & AlZain, M. A. (2021). Energy optimised security against wormhole attack in iot-based wireless sensor networks. Computers, Materials and Continua, 68(2), 1967-1981. [CrossRef]
  30. Ghosh, G., Verma, S., Jhanjhi, N. Z., & Talib, M. N. (2020, December). Secure surveillance system using chaotic image encryption technique. In IOP conference series: materials science and engineering (Vol. 993, No. 1, p. 012062). IOP Publishing.
  31. Singhal, V., Jain, S. S., Anand, D., Singh, A., Verma, S., Rodrigues, J. J., ... & Iwendi, C. (2020). Artificial intelligence enabled road vehicle-train collision risk assessment framework for unmanned railway level crossings. IEEE Access, 8, 113790-113806. [CrossRef]
  32. Lim, M., Abdullah, A., Jhanjhi, N. Z., Khan, M. K., & Supramaniam, M. (2019). Link prediction in time-evolving criminal network with deep reinforcement learning technique. IEEE Access, 7, 184797-184807.
  33. Alkinani, M. H., Almazroi, A. A., Jhanjhi, N. Z., & Khan, N. A. (2021). 5G and IoT based reporting and accident detection (RAD) system to deliver first aid box using unmanned aerial vehicle. Sensors, 21(20), 6905.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

Subscribe

Disclaimer

Terms of Use

Privacy Policy

Privacy Settings

© 2025 MDPI (Basel, Switzerland) unless otherwise stated