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,…

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…

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,…

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…

Employee turnover continues to be discussed as an outcome in Human Resources (HR), but comparatively few studies have examined the relationship between turnover as the independent variable and institutional outcomes. Although the call to HR practitioners has often been made over the past 20 years regarding the importance of tying HR programs and…

For outsiders to higher education institutions (HEIs) in South Korea, predicting the outcomes of the International Education Quality Assurance System (IEQAS)--a Korean institutional accreditation system for HEIs--is challenging. The annual IEQAS accreditation has been conducted behind closed doors; the assessment process is confidential, and there…

In this study, we work towards a strategy to measure and enhance the quality of interactions in discussion forums at scale. We present a machine learning (ML) model which identifies the phase of cognitive presence exhibited by a student's post and suggest future applications of such a model to help online students develop higher-order thinking. We…

This dissertation's two primary purposes were to construct an alternative socioeconomic status model and estimate how it predicts student success in higher education. This research filled a gap in knowledge about the widely acknowledged disparities in higher education based on socioeconomic status. Prior research has often relied on parental…

The purpose of the study is to analyse and identify the stages of adoption of the blended learning (BL or b-learning) methodology in higher education contexts, and to assess the relationship of these stages with a set of variables related to personal and professional characteristics, attributes perceived on BL and contextual variables. About 980…