The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This study explored how facilitation skills—defined as instructional techniques that accurately convey core messages in a trusting relationship and encourage self-directed learning participation among adult learners—affect the effectiveness of learning. The research focused on adult learners enrolled in lifelong education programs at seven universities, including general and vocational colleges in Busan. It aimed to examine the relationships between instructors’ facilitation skills, learner engagement, and learning outcomes, as well as the mediating effect of engagement on these relationships. A total of 213 valid survey responses were analyzed from an initial 215 responses, excluding 2 unsuitable entries. The findings are summarized as follows. First, facilitation skills were found to partially influence learner engagement. Second, learner engagement was shown to affect learning outcomes. Third, facilitation skills were found to have a partial effect on learning outcomes. Fourth, learner engagement partially mediated the relationship between facilitation skills and learning outcomes. Based on these results, this study is expected to contribute to a deeper understanding of the relationship between facilitation skills and learning outcomes in adult learners, providing practical guidelines for enhancing effectiveness in various educational contexts.

The COVID-19 pandemic occasioned significant changes in many aspects of human life. The education system is one of the most impacted sectors during the pandemic. With the contagious nature of the disease, governments around the world encouraged social distancing between individuals to prevent the spread of the virus. This led to the shutdown of many academic institutions, to avoid mass gatherings and overcrowded places. Developed and developing countries either postponed their academic activities or used digital technologies to reach learners remotely. The study examined the benefits of online learning during the COVID-19 pandemic. The participants for the study consist of 5 lecturers and 30 students from the ML Sultan Campus of the Durban University of Technology, South Africa. Data was collected using open-ended interviews. Content analysis was applied to analyze the data collected. Data was collected until it was saturated. Different ways were implemented to make online learning and teaching successful. The findings identified that the benefits of online learning were that it promotes independent learning, flexible learning adaptability and others.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The purpose of the current study is to examine the mediating role of intercultural communicative competence on the relationship between teaching of English language and learning at Chinese higher vocational colleges. The convenience sampling technique was used to collect data from 668 teachers, teaching English language subjects in different public and private Chinese higher vocational colleges. Smart partial least squares-structural equation modeling on SmartPLS software version 4 was used to test the hypotheses. The result revealed the direct effect of English language teaching (ELT) is not significant on English language learning (ELL). However, the intercultural communicative competences (ICC) have been tested and proved to be a potential mediator between English language teaching and learning. Because the indirect effect of ELT on ELL is positive and significant through mediator ICC. Therefore, based on the findings of this study, it can be concluded that the inclusion of intercultural communication ability is a crucial component in the vocational education of college students. Policymakers should be cautious about promoting and expanding the availability of cultural teaching and learning across demographic conditions (e.g., linguistic and ethnic diversity, age, and gender) and various levels of language proficiency. In accordance with the effects of teacher education and professional development programs, the implementation of ICC content necessitates a harmonization of pedagogical approaches and assessment practices across designated levels in order to effectively achieve educational objectives. To promote ICC in English language education, there must be clear guidelines and communication to school leaders, educators, and administrators regarding the necessity and goals of cultural integration.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.