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…

"Education is the key to success," one of the most heard motivational statements by all of us. People engage in education at different phases of our lives in various forms. Among them, university education plays a vital role in our academic and professional lives. During university education many undergraduates will face several…

Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…

This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…

Predicting student's successful completion of academic programs and the features that influence their performance can have a significant effect on improving students' completion, and graduation rates and reduce attrition rates. Therefore, identifying students are at risk, and the courses where improvements in content, delivery mode, pedagogy, and…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

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…

The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. Their objective is to provide timely information that may enable educators to channel the most effective remedial treatments towards precisely targeted students in an efficient manner. The present…

Purpose: This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student…

This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it…

This study investigates the alignment of a predictive model created to categorize first semester students by risk level of not completing their studies with the faculty identification of students displaying risk behaviours of the same cohort at Mohawk College. Data created by Finnie et al. (2017), is compared to a sample of first semester students…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

The purpose of this study was to investigate a predictive model of online learners' learning outcomes through machine learning. To create a model, we observed students' motivation, learning tendencies, online learning-motivated attention, and supportive learning behaviors along with final test scores. A total of 225 college students who were…

Improving graduation rates is one of the biggest missions in many universities across the country and it is surely the case on the campus of this institution. The work here presents a statistical tool box to use early academic performance as a predictor for graduation with logistic regression and machine learning techniques. The methods described…

The six-year university graduation rate for minority, first-generation, and low-income students is significantly lower than it is for non-minority students, students whose parents went to college, and students from high-income families. The problem addressed in this study was small private, faith-based institutions do not have a viable model for…