Digital technology development has led to the creation of various learning options in the science education field. Technology such as gamification has been used in several science areas, uncovering diverse benefits regarding its usage. However, more research is needed to understand how gamification enhances students learning in science fields. Therefore, this study review presented an overview of gamification usage in the science education field literature and focused on research indexed in several international databases. 31 studies were included for review. The results showed that most of the studies presented positive outcomes as gamification proved to be effective with science education students. The study also indicated that most gamification studies were mostly used to enhance students’ learning skills in science education. The results of this study provide valuable data regarding technology techniques to enhance students’ skills in science education. Further research is needed to cover areas that are not widely investigated in gamification literature.

Online customer advocacy has developed as a distinctive strategic way to improve organisational performance by fostering favourable reciprocal affinitive customer behaviours between the business and its customers. Intelligent systems that can identify online social advocates based on their social interaction and long-standing conversations with the brads are still lacking. This study adds to the burgeoning body of literature in this research area by developing a novel model to identify brand advocates using natural language inference (NLI) and artificial intelligence (AI) approaches. In particular, a hybridised deep learning model (BERT-BiLSTM-TextCNN) is proposed and adept at extracting the amount of entailment, contradiction, and neutrality obtained from the advocates' replies to the brands. This offers a new dimension to identify advocates based on the semantic similarities between the brands’ tweets and customers’ replies. The experimental results demonstrate the applicability of integrating the advantages of fine-tuned BERT, TextCNN, and BiLSTM using various evaluation metrics. Further, the proposed model is incorporated in a downstream task to verify and validate its effectiveness in capturing the correlation between brands and their advocates. Our findings contribute to the burgeoning body of literature in this research area and have important implications for identifying and engaging with brand advocates in online customer engagement.

Human activity recognition (HAR) is the task of classifying sequences of data into defined movements. Taking advantage of deep learning (DL) methods, this research investigates and optimizes neural network architectures to effectively classify physical activities from smartphone accelerometer data. Unlike traditional machine learning (ML) methods employing manually crafted features, our approach employs automated feature learning with three deep learning architectures: Convolutional Neural Networks (CNN), CNN-based autoencoders, and Long Short-Term Memory Recurrent Neural Networks (LSTM RNN). The contribution of this work is primarily in optimizing LSTM RNN to leverage the most out of temporal relationships between sensor data, significantly improving classification accuracy. Experimental outcomes for the WISDM dataset show that the proposed LSTM RNN model achieves 96.1% accuracy, outperforming CNN-based approaches and current ML-based methods. Compared to current works, our optimized frameworks achieve up to 6.4% higher classification performance, which means that they are more appropriate for real-time HAR.

Diabetic retinopathy (DR) is the main ocular complication of diabetes. Asymptomatic for a long time, it is subject to annual screening using dilated fundus or retinal photography to look for early signs. Fundus photography and optical coherence tomography (OCT) are used by ophthalmologists to assess retinal thickness and structure, as well as detect edema, hemorrhage, and scarring. The effectiveness of ConvNet no longer needs to be demonstrated, and its use in the field of imaging has made it possible to overcome many barriers, which were until now insurmountable with old methods. Throughout this study, a robust and optimal deep ConvNet is proposed to analyze fundus images and automatically distinguish between healthy, moderate, and severe DR. The proposed model combines the use of the ConvNet architecture taken from ImageNet, data augmentation, class balancing, and transfer learning in order to establish a benchmarking test. A significant improvement at the level of middle class which corresponds to the early stage of DR, which was the major problem in previous studies. By eliminating the need for retina specialists and broadening access to retinal care, the proposed model is substantially more robust in objectively early staging and detecting DR.

Adopting modern learning technology such as Augmented Reality (AR) technology in higher education has become a demand to enhance teaching and learning performance and motivate students to acquire effective learning processes. Yet, the readiness of universities to adopt modern learning technology, such as AR applications, in developing countries, particularly in Saudi Arabia, is considered one of the critical issues to ensure the AR system’s success. This study aims to explore the factors that influence the academic members’ willingness in Saudi Arabian higher education to adopt modern learning techniques such as AR technology. To test the model, a quantitative survey using a questionnaire with a five-point Likert scale was applied in this study to collect the data. The study was conducted among a sample of 228 academic members and e-learning staff. Based on the analysis, the study found that perceived usefulness and perceived pedagogical contribution are important predicting factors for academic members’ willingness to employ AR in Saudi Arabian higher education teaching. The findings from this study provide insights that assist further studies regarding AR integration factors in higher education. This study contributes to the literature by developing a theoretical and conceptual framework of critical success factors for AR incorporation in Saudi Arabian (SA) universities.