This paper aims to analyze the impact of access to Information and Communication Technologies (ICT) on the private returns to higher education (HE) focusing on gender inequality in 2020. Methodology: To evaluate the above impact a set of Mincerian equations will be estimated. The proposed approach mitigates biases associated with self-selection and individual heterogeneity. Data: The database comes from the National Household Income and Expenditure Survey (Encuesta Nacional de Ingresos y Gastos de los Hogares, ENIGH) from 2020. Results: Empirical evidence suggests that individuals that have HE have a positive and greater impact on their salary income compared to those with a lower educational level, being women that do not have access to ICT those with the lowest wage return. Policy: Access to ICT should be considered as one of the criteria that integrate social deprivation in the measurement of multidimensional poverty. Likewise, it is necessary to design public policies that promote the strengthening and creation of educational and/or training systems in technological matters for women. Limitations: No distinction was made between individuals that graduated from public or private schools, nor was income from sources other than work considered. Originality: This investigation evaluates the impact of access to ICT on the returns to higher education in Mexico, in 2020, addressing gender disparity.

The study’s objective is to identify the challenges and limitations faced by the current vocational education system in preparing graduates in the era of the industrial revolution in the evolving job market in Tangerang, Indonesia. The study primarily examines vocational high schools and adopts a quantitative and quasi-experimental research approach, using control groups to conduct pre- and posttests. The experimental group experiences demonstrations, whereas the control group receives explanations. Instructors employ a blend of demonstration and explanation techniques to explain equipment operation before allowing students to engage in vocational training. The study, led by students in various engineering fields, evaluates technical competencies, work ethics, and foundational knowledge using tests and observations. Job preparation is assessed using the minimal completeness criteria (MCC), which focuses on the importance of proper knowledge, attitudes, and skills. The results indicate that vocational teachers have the potential to play a pivotal role in introducing cutting-edge, technology-based teaching methods, therefore enabling students to make well-informed decisions about their careers. This research enhances vocational education by incorporating practical skills and attitudes with academic knowledge, effectively addressing the changing requirements of the work market.

Improving the practical skills of Science, Technology, Engineering and Mathematics (STEM) students at a historically black college and university (HBCU) was done by implementing a transformative teaching model. The model was implemented on undergraduate students of different educational levels in the Electrical Engineering (EE) Department at HBCU. The model was also extended to carefully chosen high and middle schools. These middle and high school students serve as a pipeline to the university, with a particular emphasis on fostering growth within the EE Department. The model aligns well with the core mission of the EE Department, aiming to enhance the theoretical knowledge and practical skills of students, ensuring that they are qualified to work in industry or to pursue graduate studies. The implemented model prepares students for outstanding STEM careers. It also increases enrolment, student retention, and the number of underrepresented minority graduates in a technology-based workforce.

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.

The flipped classroom (FC) model has long brought significant benefits to higher education, secondary, and elementary education, particularly in improving the quality and effectiveness of learning. However, the implementation of FC model to support elementary students in developing self-learning skills (autonomous learning, independent study, self-directed learning) through technology still faces numerous challenges in Vietnam due to various influencing factors. Data for the study were collected through direct questionnaires and online surveys from 517 teachers at elementary schools in Da Nang, Vietnam. Based on SEM analysis, the study identified factors such as perceived usefulness, accessibility, desire, teaching style, and facilitating conditions. The research findings indicate that factors like the perceived effectiveness of the model, teaching style, and facilitating conditions have a positive correlation with the decision to adopt the FC model. Therefore, to encourage the use of the FC model in teaching, it is essential to raise awareness of the model’s effectiveness, improve teaching styles, and create favorable conditions for implementation.

With the development and reform of education, the cultivation of core competencies for normal school students is receiving increasing attention. This article analyzes the connotation of the core literacy of preschool education teacher students, the difficulties faced in cultivating core literacy, and explores how to use flipped classrooms to enhance the core literacy of preschool education teacher students.