In the current educational landscape, where large amounts of data are being produced by institutions, Educational Data Mining (EDM) emerges as a critical discipline that plays a crucial role in extracting knowledge from this data to help academic policymakers make decisions. EDM has a primary focus on predicting students' academic performance.…

Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…

Whether machine learning algorithms effectively predict college students' course outcomes using learning management system data is unknown. Identifying students who will have a poor outcome can help institutions plan future budgets and allocate resources to create interventions for underachieving students. Therefore, knowing the effectiveness of…

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,…

In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…

An effective educational program warrants the inclusion of an innovative construction that enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational decision support system has currently been a hot topic in educational systems, facilitating the pupil…

Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this…

The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization)…

Digital capabilities have become increasingly important in this digital age. Within a university setting, digital capability assessment is key to curriculum design and curriculum mapping, given that digital capabilities not only can help students engage and communicate with others but also succeed at work. To the best of our knowledge, however, no…

There is a dearth of knowledge on how persuasiveness of influence strategies affects students' behaviours when using online educational systems. Persuasiveness is a term used in describing a system's capability to motivate desired behaviour. Most existing approaches for assessing the persuasiveness of a system are based on subjective measures…

Every year, the national high school graduation rate is declining and impacting the number of students applying to colleges. Moreover, the majority of students are applying to more than one college. This makes a lot of colleges to be highly competitive in student recruitment for enrollment and thus, the necessity for institutions to anticipate…

Educational institutions are creating a considerable amount of data regarding students, faculty and related organs. This data is an essential asset for academic institutions as it has valuable insights, knowledge and intelligence for the policymakers. Students are the fundamental entities and primary source of data creation in any educational…

This study aimed to understand the relationship between course activities and learning progress among students enrolled in the MicroMasters certificate program offered in an affordable MOOC-based learning platform. In order to capture the relationship, the differences between the engagement patterns of learners in the MicroMasters program compared…

Accurately forecasting course enrollment rates in higher education is of great concern in order to minimize unnecessary administrative costs as well as burden to both students and faculty. This research aimed to first recreate course enrollment predictions based on a conditional probability analysis using student data from San Diego State…

In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of…