An ageing population is a universal phenomenon experienced worldwide. In parallel with these demographic changes, a significant breakthrough in digital devices has also influenced this digital age. Designing instructional strategies to promote meaningful learning among older adult learners has been a long-standing challenge. To enhance older adults' life-long learning experiences, implementing instructional strategies in the process through which such adults learn can help to improve effective learning. Despite significant calls for research in this area, there is still insufficient research that systematically reviews the existing literature on older adult learning needs and preferences. Hence, in the present article, a systematic literature review was conducted of the effectiveness of instructional strategies designed for older adult learners through the use of digital technologies. The review was guided by the publication standard, which is ROSES (Reporting Standard for Systematic Evidence Syntheses). This study involves articles selected from two established databases, Web of Science and Scopus. Data from the articles were then analysed using the thematic analysis, which resulted in six main themes: (1) collaborative learning; (2) informal learning setting; (3) teaching aids; (4) pertinence; (5) lesson design; and (6) obtaining and providing feedback. The six main themes produced a further 15 sub-themes. The results from this study make significant contributions in the areas of instructional design and gerontology. The findings from this study highlight several important strategies of teaching digital technology, particularly for older adults, as follows: (1) to enhance instructional design use in teaching digital technology based on the needs and preferences of older adult learners; and (2) to highlight the factors for, and impact of, learning digital technologies among older adults.
Coronavirus disease 2019 (COVID-19) is a disease caused by a novel strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severely affecting the lungs. Our study aims to combine both quantitative and qualitative analysis of the convolutional neural network (CNN) model to diagnose COVID-19 on chest X-ray (CXR) images. We investigated 18 state-of-the-art CNN models with transfer learning, which include AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, GoogLeNet, Inception-ResNet-v2, Inception-v3, MobileNet-v2, NasNet-Large, NasNet-Mobile, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception. Their performances were evaluated quantitatively using six assessment metrics: specificity, sensitivity, precision, negative predictive value (NPV), accuracy, and F1-score. The top four models with accuracy higher than 90% are VGG-16, ResNet-101, VGG-19, and SqueezeNet. The accuracy of these top four models is between 90.7% and 94.3%; the F1-score is between 90.8% and 94.3%. The VGG-16 scored the highest accuracy of 94.3% and F1-score of 94.3%. The majority voting with all the 18 CNN models and top 4 models produced an accuracy of 93.0% and 94.0%, respectively. The top four and bottom three models were chosen for the qualitative analysis. A gradient-weighted class activation mapping (Grad-CAM) was used to visualize the significant region of activation for the decision-making of image classification. Two certified radiologists performed blinded subjective voting on the Grad-CAM images in comparison with their diagnosis. The qualitative analysis showed that SqueezeNet is the closest model to the diagnosis of two certified radiologists. It demonstrated a competitively good accuracy of 90.7% and F1-score of 90.8% with 111 times fewer parameters and 7.7 times faster than VGG-16. Therefore, this study recommends both VGG-16 and SqueezeNet as additional tools for the diagnosis of COVID-19.