Learning management systems (LMS) have become an important part of the modern higher education industry, and Moodle is one of the most popular open-source systems, which have become popular on an international level. Despite its extensive application, the majority of the existing evaluation tools are inclined to examine either performance, sustainability, or scalability separately and, thus, cannot be useful in long-term institutional planning. The current study proposes an assessment model that is a rational model of a Moodle-based LMS, as all three dimensions are included in the assessment model. The framework identifies the performance of the system, resource utilization and resource management, load-adaptive scalability metric, and an adaptable and predictable algorithmic-based assessment procedure in a variety of deployment environments. The framework applies Min–Max normalization and weighted aggregation to combine the three evaluation dimensions into a unified assessment score. To make the model more realistic, it was tested on publicly available data, for example, the Open University Learning Analytics Dataset (OULAD), which simulated actual interactions between LMS users and the utilization of cloud resource traces to analyze scalability and sustainability. Experimental evaluation using the OULAD and cloud resource datasets demonstrated approximately a 10% improvement in performance under medium workload conditions, an 18% reduction in sustainability due to increased resource utilization, and a 20% improvement in scalability as workload increased. These findings demonstrate that the proposed framework provides a systematic and data-driven approach for evaluating Moodle-based learning management systems and supports informed institutional decision-making.

The rapid evolution of Internet of Things (IoT) networks has presented serious security threats because of the enormous volume of distributed data produced by connected devices. Traditional IDSs (IDS) usually follow centralized data collection, resulting in communication overhead, scalability issues, and privacy problems. Although federated learning (FL) offers a way to train distributed models while preserving privacy, many current FL-based AD techniques cannot be adapted to account for the interaction relationships between IoT devices. To address these challenges, this study introduces the federated graph attention network (FL-GAT) for anomaly detection in IoT-edge environments. The proposed framework treats IoT devices as graph nodes and introduces a multi-head graph attention mechanism to capture the spatial interaction among devices while guaranteeing data privacy by adopting federated learning. Local models are trained in a distributed manner on edge devices without sharing raw data. Distributed IoT attack scenarios were used to evaluate the proposed framework using the TON_IoT and Bot-IoT benchmark datasets. Experimental results show that FL-GAT achieved 95.2 % accuracy and 94.5 % F1 score on TON_IoT and 94.8 % accuracy and 94.1 % F1 score on Bot-IoT, with better results than centralized deep learning and federated deep learning baseline models, and graph-based baseline models. Furthermore, the attention mechanism enhances the interpretability of the model by identifying the key interactions between devices that lead to unusual activities. Although the proposed framework shows encouraging performance and scalability, the evaluation was conducted using benchmark IoT datasets under a simulated experimental setting. Future work will focus on real-world deployment scenarios, dynamic network conditions, and lightweight edge optimization for resource-constrained IoT devices.