In the fast-growing population of Africa, Nigeria has become the most populous nation with over 177,000,000 inhabitants and consisting of about 32% youths (Abdulrahman et al, 2014). Sustainable development can only be achieved through proper education at all levels. While the regular classroom-based educational programmes have been characterized by incessant strikes and ever-increasing requests for admission due to increasing population, the introduction and acceptance of Open and Distance Learning programmes (ODL) as an alternative to classroom based system, has become imperative to cater for the educational needs of the entire citizenry. Although there have been considerable investments in educational facilities in most schoolsoffering ODL programmes, this research found that a lot still need to be put in place, considering that that high cost of internet access, tutor-learner relationship, educational tools and the packaging of ODL educational resources continues to pose tremendous challenges to students thereby affecting their learning rates and by extension, their academic performance.

Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.