GRE Aptitude Test scores have been a key criterion for admissions to U.S. graduate programs. However, many universities lifted their standardized testing requirements during the COVID-19 pandemic, and many decided not to reinstate them once the pandemic ended. This change poses additional challenges in evaluating prospective students. In this…

The learning processes have been significantly impacted by technology. Numerous learners have adopted technology-based learning systems as the preferred form of learning. It is then necessary to identify the learning styles of learners to deliver appropriate resources, engage them, increase their motivation, and enhance their satisfaction and…

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…

It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…

Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based…

Bias influencing teachers' classroom management is increasingly clear, but the circumstances that influence the likelihood of relying on those biases are less understood. This study employed Classification and Regression Tree (CART) analysis, resulting in four models examining how teachers' appraisals of first-grade students' externalizing problem…

In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific…

The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for…

In this article, a large data set containing every course taken by every undergraduate student in a major university in Canada over 10 years is analysed. Modern machine learning algorithms can use large data sets to build useful tools for the data provider, in this case, the university. In this article, two classifiers are constructed using random…

The students' progression and attainment gap are considered as key performance indicators of many universities worldwide. Therefore, universities invest significantly in resources to reduce the attainment gap between good and poor performing students. In this regard, various mathematical models have been utilised to predict students' performances…

The current exploratory study determined the prevalence of the sport management academic degree being offered in top-ranked institutions as based on "U.S. News & World Report" rankings. A focus on the differences of bachelor's, master's, and doctoral degrees being offered, or not offered, was placed on national universities and…

To achieve Higher Education Act Title V funding goals, policymakers must reconsider approaches, respond to Hispanic-Serving Institution (HSI) diversity, and prioritize servingness. This study investigated HSI heterogeneity across traditional performance metrics and student-engagement indicators using data sources previously only examined…

With the spread of the COVID-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students' performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must…

Student dropout is a major concern in studies investigating higher education retention strategies. However, studies investigating the optimal time to identify students who are at risk of withdrawal and the type of data to be used are scarce. Our study consists of a withdrawal prediction analysis based on classification trees using both…

The developments and changes that have accompanied the COVID-19 pandemic have affected the educational world and all sectors. Educational institutions around the world have implemented emergency and online educational practises to ensure continuity of education as opposed to the planned distance education activities that were implemented for…