Due to the rapid evolution of information technology, distance education has undergone a sustained expansion. Within the scope of open distant learning, MOOCs (Massive Open Online Courses) tend to open up access to education to all by bridging geographical and economic barriers. However, the evolution of this learning mode is facing some challenges such as the low completion rate of the courses as well as the heterogeneity of the learners’ profifiles. In this paper, we aim to personalize the MOOC contents for each learner in order to improve their academic performance and to enhance the online platforms effificiency. The idea is to build a system that takes into account the heterogeneity of learners profifiles and offers each learner a path adapted to their needs through the exploitation of their interactions with the learning environment. To this end, we suggest using the PSO method ”Particle Swarm Optimization” in order to construct the optimal choices of learning paths in the system. Furthermore, we conduct an online learning case study to show the effectiveness of the proposed approach.

We present a streamlined PCA-PSO structure of lightweight face recognition in this paper, which combines principal component analysis (PCA) to reduce dimensions with particle swarm optimization (PSO) to adaptively select features. In contrast to traditional PCA-based models, our model dynamically chooses the most informative subset of the 44 features and results in a small but informative representation. The approach has a high recognition accuracy of 99.99 on ORL and 98.45 on LFW, with low latency (approximately 3 seconds per epoch) and low memory footprint, and without the need to use a graphic card to run it. Extensive computational studies have shown that the proposed pipeline is much faster and consumes less memory than PCA-ACO and lightweight convolutional neural network (CNN) models like MobileNetV2 and Squeeze Net and is best suited to run in real-time in CPU-limited environments. The statistical tests prove the betterment of our method compared to the past methods, where the changes are statistically significant. This research offers an efficient, simple, yet scalable alternative to deep learning-based recognition systems, especially when embedded integration is a key requirement, by offering rapid convergence, adaptive feature selection, and compact representation.

In modern e-learning environments, ensuring both student identity verification and concentration monitoring during online examinations has become increasingly important. This paper introduces a robust sequential framework that integrates Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and fuzzy logic to achieve reliable face recognition and interpretable attentiveness assessment. Images from the Extended Yale B (cropped) dataset are preprocessed through grayscale normalization and resizing, while GANs generate synthetic variations in pose, illumination, and occlusion to enrich the training set and improve generalization. The CNN extracts discriminative facial features for identity recognition, and a fuzzy inference system transforms the CNN’s confidence scores into human-interpretable concentration levels. To stabilize learning and prevent overfitting, the model incorporates dropout regularization, batch normalization, and extensive data augmentation. Comprehensive evaluations using confusion matrices, ROC–AUC, and precision–recall analyses demonstrate an accuracy of 98.42%. The proposed framework offers a scalable and interpretable solution for secure and reliable online exam proctoring.