This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…

The integration of AI-chatbots into higher education offers the potential to enhance learning practices. This research aims to explore the factors influencing AI-chatbots adoption within higher education, with a focus on the moderating roles of technological proficiency and academic discipline. Utilizing a survey-based approach and advanced…

Purpose: The direction of causality between job satisfaction and job performance (known as the holy grail of industrial psychologists) is undetermined and related research findings in different organizational contexts are mixed. Based on the ample literature, mainly from Western countries, on the relationship between job satisfaction and job…

Even though big data offers new opportunities to organizations, big data adoption (BDA) is still in the early stages of introduction, and its determinants remain unclear in many sectors. Therefore, this research intended to identify the determinants of BDA in the education sector. A theoretical model was developed based on the integration of the…

Blended Learning (BL) has been implemented by lecturers in higher educations for promoting effective pedagogical practices. However, intention to use and actual usage of BL by lecturers in higher education seems to be a major setback for successful BL implementation. Therefore, this study developed a model to examine the factors that influences…

Purpose: Considering the importance of employee performance in the changes in state higher education institutions, this study aims to conceptualize the mediating role of employee engagement and empowerment in predicting employee performance. Design/methodology/approach: The study uses a quantitative survey method to collect data from staff members…

Florida State University researchers spent the last year collecting and analyzing data on corequisite developmental education (DE) models in Texas as part of a four-year study that received a $1.5M grant from the U.S. Department of Education's Institute of Education Sciences. This study was proposed in response to Texas House Bill (HB) 2223, which…

The global pandemic caused by the SARS-CoV-2 virus brought about a true revolution in the predominant teaching-learning processes (i.e., face-to-face environment) that had been implemented up to that point. In this regard, virtual teaching-learning environments (VTLEs) have gained unprecedented significance. The main objectives of our research…

How important are learning strategies or personal attributes for learning outside of domain-specific knowledge or twenty-first-century transversal skills when predicting academic success in higher education? To address this question, we conducted a longitudinal study among 1,681 students at one of the leading universities in Hungary. Students took…

Higher education institutions face heightened accountability for student success. As such, higher education relies heavily on big data to predict student outcomes. This process is problematic because predictive models are developed on historical data, are deficit based, and are focused on student factors, neglecting institutional factors. The…

This study aimed to assess the effectiveness of existing insolvency predictive models employed for non-profit Higher Education Institutions (HEIs) and test a proposed predictive model utilizing statistical and ratio analysis by comparing HEIs in operations with those that closed from 2017 to 2020. The researcher incorporated a non-experimental,…

The purpose of the present study is to analyze the digital competence of Higher Education students, as a function of their academic performance (have either repeated or a not previously), as well as to predict its significant predictors. For this, an ex-post factor and a sample of 17301 students from Chile (Latin America) were utilized. A…

The paper's main aim is to investigate and predict major factors in students' behavioral intentions toward academic use of Facebook/Meta as a virtual classroom, taking into account its adoption level, purpose, and education usage. In contrast to earlier social network research, this one utilized a novel technique that comprised a two-phase…

This dissertation implements self-determination theory to examine learning environments conducive to student motivation in higher education. Specifically, this work is concerned with measuring the satisfaction and frustration of students' three basic psychological needs (BPNs), the need for autonomy, relatedness, and competence (ARC). To do so,…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…