Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

An exploratory study to compare the effects of immersive virtual reality based training on the learning motivation of final year medical students as compared to video and text-based learning. Different modes of delivery of a training simulation of laparoscopy operation were presented to students and learning motivation corresponding to which were evaluated using the Intrinsic Motivation Inventory. The study was conducted from September 2018 to May 2019. Undergraduate medical students from 8 medical colleges and universities across Punjab, Pakistan participated in this study. A total of 87 students with a mean age of 22.5 ± 4 years were recruited for the study. Of these, 57.4% (n = 50) were males and 42.6% (n = 37) were females. Paired sampled t-test was chosen for the statistical investigation for the study. The tests were conducted by comparing means of text, video, and virtual reality learning methodologies in medical students. All executed statistical models are having significance value P=.000. Therefore, results are generalizable and can be implemented across the population. Medical student motivation was observed to be the greatest in Virtual Reality settings as compared to video-based and text-based learning settings. Both theoretical and practical studies have importance in medical studies, whereas practical hand-on-practice can enhance medical students’ professional proficiency. Virtual reality was at the top in User experience, perceived competence, usefulness, and motivation for final year medical students. It can play a signficant role in contemporary teaching and learning methodology with medical educationist and students can get benefit from this technology.

This work explores student's academic performance using decision tree algorithm having parameters like Student's Academic Information and Students Activity. We collected records of 22 students from Spring 2017 semester, studying in undergraduate level from Oman's private Higher Education Institution. Proposed work utilizes Electronic Commerce Technologies module since it is a core module offered in every computing specialization. Furthermore, WEKA data mining tool is used to evaluate the decision tree algorithm for discovery of student's performance along with Moodle access time. Simulation results demonstrate that Random Forest Tree algorithm showed better accuracy than comparative decision tree algorithms. Hence, shows good agreement for the training set provided. Therefore, the proposed work aid in improving student's grades in the module. Helping stakeholders to analyze and evaluate the module delivery and results. Early detection and solution can be made both at the institutional level and module level.

Accurate information is a prerequisite for circulation and greater longevity of materials and products, enabling the economically and environmentally successful transition of circular economy (CE) in the aspiration towards sustainable development. However, a lack of appropriate digital tools is noticed in supplying relevant information and supporting recyclability, reusability, and reducibility processes. This study focuses on a building block of evolving technologies anointed the digital twin (DT). It aims to conceptualize DT as a cornerstone for the CE paradigm, positively influencing sustainability. It also endeavors to scrutinize remarkable implementations of DT-driven CE, accomplishing sustainable development goals (SDGs) in Ireland. An iterative methodology based on four steps is adopted to review an explosion of relevant, recent literature published in 2019–2024 and multiple case studies of Irish construction and manufacturing sectors. By analyzing the literature, this study discovers the evolution and taxonomy of DT, other technologies essential for its deployment, its proactive role in implementing CE and SDGs, and some challenges. Key findings of case studies reveal the current trajectory of employing DT with other digital products, upholding the CE practices within the bounds of the SDGs in Irish companies. In conclusion, DT is a conceivable means for advancing CE solutions and attaining SDGs towards social, economic, and environmental sustainability. The implications of this study are both theoretically and practically significant, encouraging industrialists and academia to adopt DT. It will help scholars gain a thorough understanding and guide industrialists to downsize their carbon footprint and waste, accomplishing SDGs. It will inspire managers with DT-driven information for decision-making and facilitate government and policymakers to collaborate in designing essential policies. Graphical Abstract

Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. Using the Pima Indian Diabetes dataset, we developed an ensemble model with 85.01% accuracy leveraging AutoGluon’s AutoML framework. To address the “black-box” nature of machine learning, we applied XAI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Integrated Gradients (IG), Attention Mechanism (AM), and Counterfactual Analysis (CA), providing both global and patient-specific insights into critical risk factors such as glucose and BMI. These methods enable transparent and actionable predictions, supporting clinical decision-making. An interactive Streamlit application was developed to allow clinicians to explore feature importance and test hypothetical scenarios. Cross-validation confirmed the model’s robust performance across diverse datasets. This study demonstrates the integration of AutoML with XAI as a pathway to achieving accurate, interpretable models that foster transparency and trust while supporting actionable clinical decisions.