Our project is motivated by the early dropout and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer and higher granularity information to make more accurate predictions of dropout and performance. The results show that finer-grained temporal information increases the predictive power in the…

Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification…

Many of the first-year undergraduate students who enrol in universities, particularly in top-ranked private universities in Bangladesh, struggle with getting good grades. As a result, many students look forward to a bleak future, dropping out midway through their studies. Thus, improving the rates of graduation and reducing the rates of attrition…

Emphasis in school dropout literature has shifted from exploring wide-ranging causes of dropping out to soliciting a smaller number of predictive indicators to identify students at increased risk for dropping out. However, much of the past decade's Early Warning research excludes indicators that do not add to the predictive nature of the model…

In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph…

Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student…

The issues of poor academic outcomes, dismissal, high attrition, and dropout rates among college students have long concerned for many educators and college communities. Several scholars have posited that these problems can be addressed through the development of emotional intelligence and increased student engagement. Considering these problems,…

This paper presents the work done to support student dropout risk prevention in a real online e-learning environment: A Spanish distance university with thousands of undergraduate students. The main goal is to prevent students from abandoning the university by means of retention actions focused on the most at-risk students, trying to maximize the…

The true purpose of this study is to contribute to researchers' and practitioners' understanding of the power of Teacher-Student Relationships (TSRs) relative to at-risk students' graduation status. Much of the research surrounding dropouts focuses on root causes like attendance, retention, and families' economic status. However, minimal research…

Disadvantaged Mexican dropouts aged 16-22 (n=25) and 25 Hispanic elementary students completed dropout-prediction instruments. Elementary students were unable to consider their future and self-report was not viable for them. Among dropouts, pregnancy and moving around were salient predictors. Modifications of the instruments were recommended. (SK)

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…

Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates…

The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need…

School dropout is a growing concern across Latin America because of its negative social and economic consequences. Identifying who is likely to drop out, and therefore could be targeted for interventions, is a well-studied prediction problem in countries with strong administrative data. In this paper, we use new data in Guatemala and Honduras to…

The article aims to develop a machine-learning algorithm that can predict student's graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics…