This systematic review examines the influence of teacher professional development (TPD) programs on teachers’ attitudes toward digital instructional integration (DII). By analyzing 23 peer-reviewed studies published between 2020 and 2024, this study explores the key components of effective TPD programs and their impact on digital instructional practices. The review follows the PRISMA framework, detailing a structured selection process, including database searches, inclusion and exclusion criteria, and a rigorous qualitative synthesis of findings. Results indicate that effective TPD programs share key characteristics, including collaborative learning environments, hands-on digital training, ongoing mentorship, and institutional support. These elements foster positive shifts in teachers’ attitudes, confidence, and competencies regarding DII. The study highlights the need for sustained and context-specific professional development initiatives beyond one-time training sessions to ensure meaningful and long-term integration of digital tools in education. This review contributes to the field by providing evidence-based insights into designing and implementing impactful TPD programs. Its findings inform policymakers, educators, and professional development providers on structuring TPD to maximize digital instructional integration. Future research should explore the longitudinal impacts of TPD on teaching efficacy and student outcomes in technology-enhanced learning environments.

Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. This research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. The system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, DenseNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. The proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases.

Educational Data Mining is a discipline focused on developing ways for studying the unique and increasingly large-scale data generated by educational settings and applying those methods to better understand students and the environments in which they learn. Predicting student performance is one of the most critical concerns in educational data mining, which is gaining popularity. Student performance prediction attempts to forecast a student's grade before enrolling in a course or completing an exam. The goal of this paper is to present a systematic literature review on predicting student performance using machine learning techniques and how the prediction algorithm can be used to identify the most important attribute(s) in a student's data. The study showed that neural networks is the most used classifier for predicting students’ academic results and also provided the best results in terms of accuracy. Also, 87% of the algorithms used were supervised learning as compared to 13% for unsupervised learning and 59% of the studies employed various feature selection methods to improve the performance of the machine learning models.

Texture plays a crucial role in computer vision, providing valuable information about image regions. Log Gabor filters that mimic the human eye’s visual cortex are used as feature extractors to identify medicinal plants based on the leaf textural features. This method was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana, consisting of forty-nine (49) plant species as well as the Flavia and Swedish Leaf datasets, which are benchmark datasets. The Log Gabor filter outperformed the Gabor filters, which have been extensively used in this area when tested on nine supervised classifiers (K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Logistic Regression, Decision tree, Random Forest, Multilayer Perceptron, Gradient Boosting and Stochastic Gradient Descent) with 10-fold cross-validation. The Support Vector Machine and Multilayer Perceptron were the best performing classifiers for both Log Gabor filter and Gabor filter in terms of accuracy, precision, true positive rate, F1 score and false positive rate. The Log Gabor filter’s highest accuracy was 79% for Mydatastet, 97% for Flavia, and 98% for the Swedish Leaf dataset whiles the Gabor filter’s highest accuracy was 66% for Mydatastet, 92% for Flavia and 96% for the Swedish Leaf dataset.

Integration of digital tools and resources in STEM instruction has garnered significant attention due to its high potential. Digital professional development and professional learning communities are identified as a pivotal factor for equipping teachers with the necessary digital skills to effectively orchestrate digital resources. Notably, their role is considered critical. However, the intricate relationships among school digital preparedness, professional learning community, digital professional development, and digital instructional integration among STEM teachers remain underexplored. Utilising partial least-squares–structural equation models (PLS–SEM), the present study examined links in school digital preparedness, professional learning community, digital professional development, and digital instructional integration among STEM teachers (N = 188). Findings from the PLS–SEM analysis indicate that digital professional development and professional learning community exhibit a direct positive relationship with school digital preparedness and digital instructional integration. Relatedly, digital professional development is positively correlated with digital instructional integration. In terms of indirect effect, findings show that professional learning community and digital professional development play a significant positive mediating role in linking digital professional development and digital instructional integration. This study reports new evidence on the influence of school digital preparedness on digital instructional integration through digital professional development among 188 STEM teachers and concludes that, when STEM teachers regularly immerse themselves in digital professional development program, they are more likely to benefit from their digital professional development by integrating digital technologies in classroom instruction. Policymakers and educational leaders should consider promoting digital professional development among STEM teachers, along with efforts to encourage digital instructional integration.