In cognitive diagnostic models (CDMs), a set of fine-grained attributes is required to characterize complex problem solving and provide detailed diagnostic information about an examinee. However, it is challenging to ensure reliable estimation and control computational complexity when The test aims to identify the examinee's attribute profile in a…

Performance management in university-based scientific research institutions is essential for driving reform, advancing education quality, and fostering innovation. However, current performance evaluation models often focus solely on research indicators, neglecting the critical interdependence between the education and research systems. This…

This research aims to investigate information asymmetry in e-commerce supply chain channels and the impact of the fair preference model on the behavior and returns of channel members. Therefore, by contrasting it with the model in the completely rational case, this research establishes a more realistic principal-agent model and incorporates the…

This study provides insight into the role of expertise in the process of team creativity. Existing process models, that is, the random variation model and the creative synthesis model, either fail to treat team as an independent creative entity or neglect the differentiation in the voice of individual members in team creative process. In contrast,…

This article is about making correction in Tarray, Singh, and Zaizai model and further improving it when stratified random sampling is necessary. This is done by using optional randomized response technique in stratified sampling using a combination of Mangat and Singh, Mangat, and Greenberg et al. models. The suggested model has been studied…

Knowledge applied to innovation is increasingly recognized as an explanatory factor of economic growth. Innovation derives from applying knowledge to generate new products or processes. National Innovation Systems (NIS) performs as the formal or informal network of people within institutions interacting to produce and apply knowledge to…

Background: Although comprehending illustrated texts is essential, adult readers in this era may not have acquired reading comprehension strategies. Eye-movement modelling example (EMME) is promising for helping less-skilled learners master these strategies; however, its benefits for adults remain unknown. Another understudied factor in the EMME…

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage RRT model to estimate the prevalence of sensitive attribute ([pi]). A simulation study shows that the empirical mean and variance of proposed estimator…

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial,…

It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased…

This article examines the politics of curriculum design and evaluation in school settings globally. It examines the role of ideology and dominant meta-narratives of standards and academic achievement culture and its impact on education policy, curriculum design and implementation. The article discusses major models of curriculum design and their…

Modern societies, new technical equipment and technology confirm the importance of knowledge acquisition in everyday life, especially in economy. An education system is a non-profit system. Since it strongly affects economic efficiency, its quantification becomes a very complex process. In order to make the quantification process possible, this…

Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D…

Internationalization impacts universities and changes their core missions. Consequently, many western universities adopted a business model approach to deal with opportunities and challenges internationalization poses to their missions. Resulting from increased scrutiny from the public and policy makers on the ability of universities to…

Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…