In regard to national development (ND), this review article (which is basically a perspective approach) presents retroactive and forward-looking perspectives on university education in Nigeria. In the past, particularly during the 1970s, the Nigerian university (NU) sector was among the most outstanding in Africa as well as globally. The best institutions drew students from around Africa, who flocked to Nigeria to study. The NU structure evidently contained four essential components for an international and effective university system, viz., world-class instructors, world-class students, a conducive learning environment, and global competitiveness. The NU structure, nevertheless, has undergone some neglect over the past thirty years and lost its distinctive identity, which raises questions about its function and applicability at the current stage of ND. Hence, some retrospective and forward-looking observations on university education in Nigeria in connection to ND are conveyed in this perspective article uses basically published articles and other relevant literature, as well as other sources and data from available literature. Hitherto, there is an urgent need for reinforcement of the university system in order to give it the desired and comparable international quality and functionality needed to meet the demands of current issues and the near future. However, this article conveys an intense belief and conviction that the NU system is still important for both the political and socioeconomic development (growth) of the nation. The article concludes by recommending the way forward in this regard.

Islamic based low-cost private school facing a suffer from a lack of new students due to the incapability to adapt to current condition. They are already “lost in the competition” with other school. This quality also includes the inability of the low-cost private schools to adapt with technology and change. Another finding is that low-cost private schools struggling in managing the funding and management. Low-cost private high school has become a key support in ensuring the fulfilment 12-year compulsory education stated by Indonesian Education Law. With only 40 percent of the total population able to attend public schools, the government as well as the private sector needs to accommodate affordable private schools to meet the need of high schools in Jakarta. Low-cost private schools become essential in providing the gap between the need of schools from the total population of students and the availability of the public school. The previous study highlighted organizational agility with the mediation of Islamic Work Ethic can be achieved through leadership capability and organizational culture, whilst several studies also suggest that Islamic Work Ethics or Istiqamah have no impact in promoting organizational agility. This research conducted in 2022–2023 encompassed 133 low-cost private high school in Jakarta in which all of them are Islamic low-cost private high school. The finding of this study is that school principal leadership capabilities and school culture are positively affect school agility with Istiqamah as mediated variable.

The presented article focusses on the analysis of perception of the university social responsibility through the eyes of Slovak university students. The aim is to compare how the values, efficiency of the organisation (university), and the educational process influence the perception of social responsibility among university students themselves. The research is based on the application of quantitative methodology towards the evaluation of differences and similarities in perceptions using two types of tests for statistical analysis, comparative (Mann-Whitney U test) and correlational (bivariate correlation matrix of Spearman’s rho).The results of the research provide a deeper understanding of how universities can shape students’ approach to social responsibility through their values and educational processes, which has important implications for the development of university policies and practices.

Gamification is an active methodology of great value that, in a quality educational environment, provides students with the necessary motivation to participate in their teaching-learning process. An emerging active methodology, which is based on the use of information and communication technologies (ICT) and requires an educational space that guarantees greater flexibility in the pedagogical dynamics in favor of academic achievement. This increase in interest in active methodologies, and specifically in gamification, has raised doubts about whether current educational spaces are prepared to host a renewal in methodology or if, on the contrary, they could undermine the attitude of change. For this reason, this research seeks to analyze whether current educational spaces are facilitating elements for the incorporation of gamification in the classroom. The methodological cut of the research is quantitative, specifically in two phases. On the one hand, a descriptive analysis of the results is carried out, obtaining information on the trend of each item. On the other hand, an inferential analysis is carried out around different variables to verify their possible influence on the evaluations of the participants. The results obtained, in the sample made up of 210 teachers distributed in the different centers and who carry out their educational activity from 3rd to 6th grade of primary school, indicate that teachers believe it is relevant to take into account the educational space when incorporating active methodologies in class.

Entrepreneurship education plays a crucial role in improving college students' entrepreneurial skills. With the significant momentum gained by digital entrepreneurship, there is an urgent need for digital transformation in entrepreneurship education. However, entrepreneurship education digital transformation (EEDT) is developing in a rapid but fragmented manner, which requires more systematic guidance. This study aims to assess the current research themes and formulate a framework for entrepreneurship education digital transformation. The research employs a systematic literature review and a theory triangulation method. According to the review’s outcome, which focused on 56 articles published between 2018 and 2023, the researcher constructed a conceptual framework for entrepreneurship education digital transformation. To test the construct validity of the framework, the researcher modified it twice through theory triangulation, following the guidelines of the entrepreneurship education ecosystem theory and the education digital transformation framework. This study offers recommendations for research and practice in digital transformation of entrepreneurship education, encompassing a holistic strategy, new educational approaches, novel curriculum designs, and the enhancement of digital literacy among entrepreneurship teachers.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.