The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…

Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…

The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in…

Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…

Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we…

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

Mentoring is useful in career development for the sciences and professions due to the cultures, skill sets, and experience-based learning in these fields. A framework for mentoring based on observations and data gathered as part of an international research and education project is presented. Students with multiple levels of experience and…

The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

The problem of racial and ethnic discipline disproportionality is both long-standing and widespread. Educators must address this issue by identifying rates of discipline disproportionality, taking steps to reduce it, and monitoring the effects of intervention on disproportionality. The purpose of this guide is to provide a reference for…

The purpose of this study was to analyze admissions policies pertaining to the declaration of academic majors for incoming students and structures of academic advising at American universities and how they relate to student outcomes. The student outcomes considered for the study were first to second year retention rates and graduation rates. …

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…

This is the first of two reports that summarize the findings from an impact evaluation of a violence prevention intervention for middle schools. This report discusses findings after 1 year of implementation. A forthcoming report will discuss the findings after 2 years and 3 years of implementation. In 2004, the U.S. Department of Education (ED)…

This qualitative study highlighted student perspectives on barriers to success at a community college located in a south Texas city. The study examined barriers to student success, the knowledge that successful students possess to overcome the barriers, and the actions that successful students undertake to overcome the barriers. Padilla's (2004)…

The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…