# Identifying At-Risk Students for Early Intervention--A Probabilistic Machine Learning Approach
###### Abstract  
The utilization of learning analytics to identify at-risk students for early intervention has exhibited promising results. However, most predictive models utilized to address this issue have been based on non-probabilistic machine learning models. In response, this study incorporated probabilistic machine learning for two reasons: (1) to facilitate the inclusion of domain knowledge, and (2) to enable the quantification of uncertainty in model parameters and predictions. The study developed a five-stage, probabilistic logistic regression model to identify at-risk students at different stages throughout the academic calendar. Rather than predicting a student's final or exam mark, the model was focused on predicting the at-risk probabilities for subsequent assessments--specifically, the probability of a student failing an upcoming assessment. The model incorporated student engagement data from Moodle, as well as demographic and student performance data. The study's findings indicate that the significance and certainty of student engagement and demographic variables decreased after incorporating student-performance variables, such as assignments and tests. The most effective week for identifying at-risk students was found to be week 6, when the accuracy was 92.81%. Furthermore, the average level of uncertainty exhibited by the models decreased by 60% from stage 3 to 5, indicating more reliable predictions at later than earlier stages. The study highlights the potential of a probabilistic machine learning model to aid instructors and practitioners in identifying at-risk students, and thereby to enhance academic outcomes. machine learning; probabilistic machine learning; predictive analytics; at-risk students; early intervention +
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different types of data generated [3]. Primarily, higher education provides greater variety of data than volume and velocity; thus, much of learning analytics research leverages these different types of data to optimize student learning and the environment in which learning occurs. This paper focuses on predictive learning analytics, which relies on algorithms for predicting at-risk students. In this paper, machine learning is understood as a modelling process of combining data with algorithms to form models that can uncover patterns in data to predict the future [4]. Using machine learning in the context of educational data mining has seen an upsurge in leveraging data, with a primary focus on understanding at-risk students and predicting at-risk students [5; 6; 7; 8]. Identifying at-risk students remains difficult, despite numerous studies using learning analytics to identify these students, so that institutions can improve their remedial actions [7; 8; 9; 10]. Most studies applied machine learning techniques based on regression and classification tasks to improve accuracy and performance in predicting at-risk students [7]. These studies primarily adopted non-probabilistic machine learning approaches, and generally used decision trees, random forests, neural networks, support vector machines, and naive Bayes as algorithms [7; 10]. Despite the increase in documented evidence of research on the use of machine learning in learning analytics, including the studies referred to above, and others, little is known about building models that account for uncertainty in predicting at-risk students. As stated above, big data in the education context relates more to the various types (variety) of data than to volume and velocity; thus, predicting at-risk students has a high level of uncertainty, which is not accounted for when using non-probabilistic machine learning. Even more importantly, the ability to predict at-risk students enables higher-education institutions to create opportunities for students to receive early interventions.