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…

Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and private university to predict student dropout as a basis for a targeted…

Educational data mining has gained a lot of attention among scientists in recent years and constitutes an efficient tool for unraveling the concealed knowledge in educational data. Recently, semisupervised learning methods have been gradually implemented in the educational process demonstrating their usability and effectiveness. Cotraining is a…

Although numerous survey-based studies have found that students who identify as lesbian, gay, bisexual, or questioning (LGBQ) have elevated risk for many negative academic, disciplinary, psychological, and health outcomes, the validity of the types of data on which these results rest have come under increased scrutiny. Over the past several years,…

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…

This qualitative study focuses on capturing students' understanding two visualizations often utilized by learning analytics-based educational technologies: bar graphs, and line graphs. It is framed by Achievement Goal Theory--a prominent theory of students' academic motivation--and utilizes interviews (n = 60) to investigate how students at risk…

Students who are at risk for or show low-intensity behavioral problems may need targeted, Tier 2 interventions. Often, Tier 2 problem-solving teams are charged with monitoring student responsiveness to intervention. This process may be difficult for those who are not trained in data collection and analysis procedures. To aid practitioners in these…

The persistent difficulty of retaining college students through graduation has become a global problem. The purpose of this quantitative, descriptive, and retrospective study was to apply data mining methods, tools, and algorithms to analyze enrollment data for issues affecting STEM students' retention at an historically black college (HBCU). The…

One tool that many families own, across the globe, is a basic mobile phone. The use of low-cost basic mobile phones for educational purposes in humanitarian settings is critical where access to connectivity and higher cost devices is limited. The portability of mobile phones, combined with their communication features, offers multiple uses to…

This post is one in a series highlighting MDRC's methodological work. Contributors discuss the refinement and practical use of research methods being employed across the organization. Across policy domains, practitioners and researchers are benefiting from a trend of greater access to both more detailed and frequent data and the increased…

Research shows that chronic absenteeism can affect academic performance in later grades and is a key early warning sign that a student is more likely to drop out of high school. Several states enacted legislation to address this issue, and many states are currently discussing the utility of chronic absenteeism as an indicator of school quality or…

There are good reasons for higher education institutions to use learning analytics to risk-screen students. Institutions can use learning analytics to better predict which students are at greater risk of dropping out or failing, and use the statistics to treat "risky" students differently. This paper analyses this practice using…