Search Results (215)

This article aims to create insights concerning the remuneration of executives (also known as vice chancellors (VCs)) in higher education in South Africa. Their remuneration is a trending and contentious topic in the media and literature within the South African context. The motivation for conducting this study is that there are no clear indicators, norms, or standards to measure salaries. Therefore, this study is grounded in agency and institutional theories. Moreover, prior to this study, there were no longitudinal studies in the South African context that have analysed VCs’ salaries, using predictors like student enrolment, return on assets, debt ratio, and revenue. The research design was longitudinal, while the research approach was quantitative. The universities that did not meet the requirements for 2016 to 2023 were excluded from the analysis, which was conducted using Python, version 3.11.7, Python Software Foundation: Wilmington, DE, USA, 2025. Since the data points were small (n = 8), bootstrapping was used to resample 1000 samples. The correlation results showed a significant relationship with the fixed salary, whereas the regression results were not significant. It was found that the VCs’ salary is a larger portion of the fixed salary, and the historical data (2013 to 2016) showed an upward trend; the forecast from 2024 to 2026 showed a flat trend. The forecasts are salient and create insights that will assist remuneration practitioners to budget for VCs’ salaries in order to attract, motivate, and retain them. Full article

Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation. Full article

This study explores the readiness of Micro and Small Enterprises (MSEs) in Mexico, specifically grocery stores, to implement the Virtual Value Chain (VVC) through Information and Communication Technologies for Development (ICT4D). A mixed-methods approach was used, combining diagnostic tools, structured surveys, and interviews. Quantitative data were analyzed using descriptive statistics, correlation analysis, and machine learning to identify digital adoption patterns. The results indicate that limited technology adoption remains the main obstacle to VVC integration. Significant associations were found between digital engagement and the age and educational level of store managers. Key digital gaps persist in inventory control, supplier coordination, and demand forecasting. Although machine learning models did not significantly outperform baseline predictions on willingness to adopt technology, the findings emphasize the potential of targeted training and accessible mobile solutions. The study proposes a new diagnostic and predictive framework to assess VVC readiness in low-resource contexts. It shows that ICT, when strategically aligned with business operations and paired with adequate training, can enhance sustainability and livelihoods. Although the study is limited to one geographic area and one business sector, it offers a foundation for scaling similar initiatives. The findings support context-sensitive strategies and capacity-building efforts tailored to the realities of MSEs in emerging economies. Full article

There is large scale data collected from the various Wi-Fi networks on modern university campuses which contribute to observing student behavioral patterns. This paper explores the use of Wi-Fi connection information and internet browsing habits to forecast student dining preferences, improving data-driven models for campus eating service optimizations. This study combines spatial–temporal features with browsing behavior analysis and employs advanced machine learning techniques to develop a multi-modal learning framework. Moreover, when Chinese consumers go out to eat, the analysis of anonymized Wi-Fi data also reveals considerable relationships among digital footprints and dining choices using a predictive model that can reach an accuracy level between 84 and 88%. The discoveries assist in the advancement of educational data mining and are beneficial for the real-world optimization of campus services, all under strong privacy protection using an end-to-end comprehensive data protection framework. Full article

In the era of educational digital transformation, integrating information and communication technology (ICT) into school administration aligns with the goals of promoting personalized learning, equity, and teaching quality. This study examines how ICT reshapes management practices, addresses challenges, and achieves educational objectives. To explore ICT’s impact on school administration (2009–2024), we conducted a systematic scoping review of four databases (Web of Science, Scopus, ScienceDirect, and IEEE Xplore) following the PRISMA-ScR guidelines. Retrieved studies were screened, analyzed, and synthesized to identify key trends and challenges. The results show that ICT significantly improves administrative efficiency. Automated systems streamline routine tasks, allowing administrators to allocate more time to strategic planning. It enables data-driven decision-making. By analyzing large datasets, ICT helps identify trends in student performance and resource utilization, facilitating accurate forecasting and better resource allocation. Moreover, ICT strengthens stakeholder communication. Online platforms enable instant interaction among teachers, students, and parents, increasing the transparency and responsiveness of school administration. However, there are challenges. Data privacy concerns can erode trust, as student and staff data collection and use may lead to breaches. Infrastructure deficiencies, such as unreliable internet and outdated equipment, impede implementation. The digital divide exacerbates inequality, with under-resourced schools struggling to utilize ICT fully. ICT is vital in educational administration. Its integration requires a strategic approach. This study offers insights for optimizing educational management via ICT and highlights the need for equitable technological advancement to create an inclusive, high-quality educational system. Full article

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers. Full article

Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article

The accurate forecasting of student disability trends is essential for optimizing educational accessibility and resource distribution in the context of Brazil’s oldest public policy, the National Textbook Program (PNLD). This study applies machine learning (ML) and time series forecasting models (TSF) to predict the number of visually impaired students in Brazil using educational census data from 2021 to 2023, with the aim of estimating the amount of Braille textbooks to be acquired in the PNLD’s context. By performing a comparative analysis on various ML models (e.g, Naive Bayes, ElasticNet, gradient boosting) and TSF techniques (e.g., ARIMA and SARIMA models, as well as exponential smoothing) to predict future enrollment trends, we identify the most effective approaches for school-level and long-term disability enrollment predictions. Results show that ElasticNet and gradient boosting excel in forecasting enrollment estimations over TSF models. Despite challenges related to data inconsistencies and reporting variations, incorporating external demographic and health data could further improve predictive accuracy. This research contributes to AI-driven educational accessibility by demonstrating how predictive analytics can enhance policy decisions and ensure an equitable distribution of resources for students with disabilities. Full article

China’s carbon peak and neutrality transition is critically constrained by the severe talent shortage and structural inefficiencies in higher education. This study systematically investigates the current status of “dual-carbon” talent cultivation and demand in China, leveraging annual “dual-carbon” talent cultivation data from universities nationwide. By applying the GM(1,1)-ARIMA hybrid forecasting model, it projects future national “dual-carbon” talent demand. Key findings reveal significant regional disparities in talent cultivation, with a pronounced mismatch between industrial demands and academic supply, particularly in interdisciplinary roles pivotal to decarbonization processes. Forecast results indicate an exponential growth in postgraduate talent demand, outpacing undergraduate demand, thereby underscoring the urgency of advancing high-end technological research and development. Through empirical analysis and innovative modeling, this study uncovers the structural contradictions between “dual-carbon” talent cultivation and market demands in China, providing critical decision-making insights to address the bottleneck of carbon-neutral talent development. Full article

Predicting undergraduate student success is critical for informing timely interventions and improving outcomes in higher education. This study leverages over a decade of historical data from Louisiana State University (LSU) to forecast graduation outcomes using advanced machine learning techniques, with a focus on convolutional autoencoders (CAEs). We detail the data processing and transformation steps, including feature selection and imputation, to construct a robust dataset. The CAE effectively extracts meaningful latent features, validated through low-dimensional t-SNE visualizations that reveal clear clusters based on class labels, differentiating students likely to graduate from those at risk. A two-year gap strategy is introduced to ensure rigorous evaluation and simulate real-world conditions by predicting outcomes on unseen future data. Our results demonstrate the promise of CAE-derived embeddings for dimensionality reduction and computational efficiency, with competitive performance in downstream classification tasks. While models trained on embeddings showed slightly reduced performance compared to raw input data, with accuracies of 83% and 85%, respectively, their compactness and computational efficiency highlight their potential for large-scale analyses. The study emphasizes the importance of rigorous preprocessing, feature engineering, and evaluation protocols. By combining these approaches, we provide actionable insights and adaptive modeling strategies to support robust and generalizable predictive systems, enabling educators and administrators to enhance student success initiatives in dynamic educational environments. Full article

Human-Wildlife conflict (HWC) presents a growing challenge for wildlife conservation, especially as species recover and reoccupy human-dominated landscapes, creating tensions between ecological goals and local livelihoods. Such conflicts are increasingly reported across Europe, including Romania, involving semi-aquatic species like the Eurasian beaver (Castor fiber L.) and Eurasian otter (Lutra lutra L.). Enhancing coexistence with wildlife through the integration of conflict mapping, stakeholder engagement, and spatial analysis into conservation planning is therefore essential for ensuring the long-term protection of conflict species. A mixed-methods approach was used, including structured surveys among stakeholders, standardized damage report collection from institutions, and expert field assessments of species activity. The results indicate that while most respondents recognize the legal protection of both species, a minority have experienced direct conflict, primarily with beavers through flooding and crop damage. Tolerance varied markedly among demographic groups: researchers and environmental agency staff were most accepting, whereas farmers and fish farm owners were the least accepting; respondents with no personal damage experience and those with university or post-secondary education also displayed significantly higher acceptance toward both species. Institutional reports confirmed multiple beaver-related damage sites, and through field validation, conflict forecast zones with spatial clustering in Harghita, Brașov, Covasna, and Sibiu counties were developed. These findings underscore the importance of conflict forecasting maps, understanding the coexistence dynamics and drivers of acceptance, and the need to maintain high acceptance levels toward the studied species. The developed maps can serve as a basis for targeted interventions, helping to balance ecological benefits with socioeconomic concerns. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

Despite the extensive evidence supporting the effectiveness of cognitive stimulation, differences in results may be due to the influence of cognitive and non-cognitive aspects in people with dementia. The aim of this systematic review is to identify the most reliable variables in forecasting the effectiveness of cognitive stimulation in people with mild to moderate dementia. According to PRISMA guidelines, the research was conducted using five databases (PubMed, Scopus, Cochrane, Web of Science, APA PsycInfo), considering randomized controlled trials. A total of six studies were included. Different aspects moderating the gain resulting from cognitive intervention were collected and assessed in terms of demographic, cognitive, emotional, social, and quality of life parameters. People with dementia benefit more from cognitive intervention if they are female, if they have a low formal education level, a low baseline level of cognitive function, and lower depressive symptoms, and if caregivers actively participate in sessions. Quality of life, if low at baseline, also seems to improve following CST intervention. A deeper understanding of the cognitive and non-cognitive aspects ensuring improvement after cognitive stimulation may guide future research to develop more personalized interventions. Full article

This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studies that applied ML or GenAI within LA contexts. Records came from 12 databases (last search 15 March 2025), and the results were synthesized via thematic clustering. ML approaches dominate LA tasks, such as engagement prediction, dropout-risk modelling, and academic-performance forecasting, whereas GenAI—mainly transformer models like GPT-4 and BERT—is emerging in real-time feedback, adaptive learning, and sentiment analysis. Studies spanned world regions. Most ML papers (n = 75) examined engagement or dropout, while GenAI papers (n = 26) focused on adaptive feedback and sentiment analysis. No formal risk-of-bias assessment was conducted due to heterogeneity. While ML methods are well-established, GenAI applications remain experimental and face challenges related to transparency, pedagogical grounding, and implementation feasibility. This review offers a comparative synthesis of paradigms and outlines future directions for responsible, inclusive, theory-informed AI use in education. Full article