In engineering education, fostering problem-solving skills is essential for students to meet industry demands. Project-Based Problem Learning (PBPL) is a promising approach to enhancing these skills. This study aims to investigate the impact of PBPL on the problem-solving skills of engineering education students and explore their perceptions of PBPL. A sequential mixed-method approach combined quantitative pre-and post-test analysis with qualitative insights from interviews and questionnaires. The results indicate a significant improvement in problem-solving skills among students after implementing PBPL. Moreover, students expressed positive perceptions of PBPL, highlighting its benefits in enhancing critical thinking and collaboration. PBPL shows promise in enhancing problem-solving skills among engineering education students and has positive implications for teaching and learning in universities. The findings suggest that integrating PBPL into engineering curricula can effectively enhance students' problem-solving abilities, better preparing them for future challenges in the industry.

This study aims to analyze the effectiveness of the effect of the Project-Based Learning (PjBL) learning model on student learning outcomes. There were 26 articles analyzed in this study. The articles collected have been published in national journals, international conferences, and international journals for the last 3 years from 2018 to 2021 related to Project-Based Learning (PjBL) and learning outcomes. This research uses a meta-analysis method by calculating the effect size value in each article. Four formulas are used to calculate the effect size value for each article. This research is meta-analysis research based on the form of learning outcomes with the Project-Based Learning (PjBL) model, Year, Type of Integration, Effect Size Value, and Type of Publication. The meta-analysis results show that the average effect size of PjBL on student learning outcomes is high and significant. The Project-Based Learning (PjBL) model is mostly integrated with the Conventional Learning (CvL) model. The order of most publications starts from national journals, international journals, and international conferences, all three of which have an average effect size value in the high category, and there is no publication bias. Based on data analysis, Project-Based Learning (PjBL) can increase students' creativity in learning. Besides, it helps students to be able to think more critically in solving problems. This model can also improve students' skills to help them compete in the 21st century and the era of society 5.0.

A learning model is a form of process or way of presenting learning activities from beginning to end. This learning model serves as a means of communication in the classroom. This study aims to determine whether there are differences in the evaluation of pure learning outcomes by applying the Project-Based Learning (PjBL) and Problem Based Learning (PBL) learning models with the Pretest score as a controller. The number of subjects in this study was 42 mining engineering students in applied physics courses using the purposive sampling method. Data was collected by asking questions before being given treatment and after being given treatment. The data analysis technique used is the analysis of covariance (ANCOVA) in evaluating student learning outcomes. The findings indicate that there are significant differences in learning outcomes between the Project-Based Learning (PjBL) learning model and the Problem Based Learning (PBL) learning model by controlling the Pretest score. The significance of the PjBL learning model gives different and higher results compared to the PBL learning model. The application of the Project-Based Learning learning model provides many more meaningful and meaningful learning experiences for students in improving the evaluation of learning outcomes.

This study evaluates the validity of the Project-Based Problem Learning (PBPL) Model Book designed for higher education settings. The primary objective is to assess the model's content validity by applying Aiken's V, focusing on critical aspects such as Content, Language, Structure and Organization. To achieve this, a panel of experts with relevant expertise in education reviewed the PBPL Model Book. Their evaluations were then subjected to rigorous statistical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM), a technique that ensures the robustness and reliability of the validation process. The findings revealed that the average Aiken's V values for each category significantly exceeded the 0.85 threshold, which confirms the model's high validity and appropriateness for educational purposes. The methodological approach of this study is notable for its integration of expert judgment with advanced statistical validation techniques. This combination of qualitative and quantitative methods provides a comprehensive assessment of the PBPL Model Book's alignment with established educational standards. The results indicate that the model effectively incorporates project-based and problem-based learning principles, offering a structured and systematic framework designed to enhance student engagement, critical thinking, and problem-solving skills. The high validity scores across all evaluated categories—Content, Language, Structure and Organization—demonstrate that the model is reliable and adaptable for use in various academic contexts. In contributing to the literature on educational models, the PBPL Model Book stands out as a well-validated tool that addresses theoretical and practical educational needs. Its emphasis on active learning methodologies ensures students are better prepared to address real-world challenges. Consequently, this model is a valuable resource for educators seeking to implement innovative teaching strategies within higher education. The PBPL Model Book supports advancing pedagogical practices and educational experience by fostering a dynamic and engaging learning environment.

The rapid emergence of chat generative pretrained transformer (ChatGPT) has attracted considerable attention in higher education (HE), yet its research development remains insufficiently understood. This study provides an early-stage bibliometric analysis of 140 Scopus-indexed articles to map research trends in this field. The results show a sharp increase in publications, with China, the United States, and the United Kingdom as the leading contributors. Co-occurrence analysis identified three major themes: educational applications, learner interactions, and technological dimensions. Overlay visualization revealed emerging topics such as intention, relationship, and social influence, indicating a growing focus on adoption and behavioral aspects of ChatGPT in HE.

This study examines how Technology Readiness, Digital Learning Tools Usage, and Perceived Usefulness impact Student Engagement and Learning Outcomes within engineering and vocational education. The research framework explores 18 hypotheses focusing on both direct and indirect relationships among these variables. A quantitative approach with a cross-sectional survey design was meticulously employed, ensuring the thoroughness of the research process. The study population included students enrolled in an engineering faculty for the January-June 2024 semester at one of the state universities in Indonesia. We selected a sample size of 1,781 respondents using stratified random sampling. Data were collected through an online questionnaire using a 5-point Likert scale and analyzed with Structural Equation Modeling (SEM) using SmartPLS. The findings reveal that technology readiness significantly influences the usage of digital learning tools and perceived usefulness, affecting student engagement and learning outcomes. Additionally, using digital learning tools and perceived usefulness mediate the relationships between Technology Readiness, Student Engagement, and Learning Outcomes. This research provides valuable insights into how technology-related factors can enhance learning outcomes in technical and vocational education, offering practical recommendations for improving technology-based learning environments and equipping educators with actionable strategies.