This study investigates non-academic employees’ perceptions of their line managers’ leadership styles at a private university in Malaysia and how these perceptions influence their intention to remain employed. Employing a qualitative approach and the path-goal theory as a theoretical framework, data were collected through purposive sampling from 10 non-academic employees and analyzed thematically using NVivo 12 software. The findings reveal that a supportive and participative leadership style fosters an informal leadership dynamic between line managers and subordinates. Informal leadership behaviors encompass affective qualities and effective communication that enable the development of close relationships outside the workplace, facilitating increased employee engagement and motivation levels. Consequently, this approach notably improves employee retention. This study offers a comprehensive understanding of informal leadership styles contributing to enhanced human resource management at the private university while providing an inclusive perspective on employees’ perceptions and their intention to remain employed. Finally, we propose a model of employees’ perception of leadership styles as the main driver that better serves their intention to stay in organizations.

Technical Pedagogical Content Knowledge (TPACK) encompasses teachers’ understanding of the intricate interplay among technology, pedagogy, and subject matter expertise, serving as the essential knowledge base for integrating technology into subject-specific instruction. Over the decade, advancements in information technology have led to the consistent application of the TPACK framework within studies on instructional technology and technology-enhanced learning, significantly advancing the evolution of contemporary teacher education in technology integration. In this paper, we utilize the Teaching and Learning Knowledge of Subjects Based on Integrated Technology (TPACK) framework to administer a questionnaire survey to teacher trainees at Chinese colleges and universities. This survey aims to evaluate the current status of their integrated technology-based subject teaching and learning knowledge. Based on the research findings, we propose strategies aimed at enhancing the educational technology integration knowledge of students pursuing integrated technology courses in colleges and universities. Furthermore, we integrate the smart classroom setting to develop a comprehensive TPACK-integrated model teaching framework. Our final objective is to offer valuable references for the progress of modern teaching skills among education students in higher education institutions.

This research explores the role of social media in the political construction of identity, analyzing how these platforms mediate the expression and formation of individual and group political identities. The focus is on how social media changes the dynamics of communication and social interaction, facilitating the formation of “echo chambers” and increasing political polarization. Additionally, this study highlights challenges such as disinformation and the implications of social media for the health of democracy. As a researcher, I aim to highlight the broader implications of using social media in identity politics. By analyzing the impact of social media on political dynamics in Indonesia, this study reveals how social media influences public perception and political decisions. This study identifies how social media can be used as a tool to mobilize political support, but also how these platforms can spread disinformation and reinforce political polarization. Based on these concerns, researchers have not yet found research results that examine how social media specifically impacts the construction of political identity. This research aims to highlight how social media not only acts as a communication tool but also as a medium that influences the way individuals view and express their political identity. Through a qualitative approach, this study provides new insights into the impact of social media in contemporary political dynamics and the importance of digital literacy in addressing issues of identity politics in the digital era.

This study aims to investigate the impact of dance training on the mental health of college students. Utilizing experimental research methods, we established an experimental group and a control group to compare changes in mental health dimensions—including anxiety, depression, self-esteem, and social skills—between the two groups before and after 12 weeks of dance training. The findings indicate that dance training significantly reduces levels of anxiety and depression, while also improving self-esteem and social skills, thereby enhancing social adaptability. These results provide empirical support for the use of dance as an intervention for mental health and offer new insights for mental health education in colleges and universities.

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.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.