Predicting at-risk students in a higher educational institution in Ghana for early intervention using machine learning

Abstract

Learning analytics (LA) uses data and evidence to suggest a better learning approach that suits a particular student. This data and evidence are gathered from students’ online engagement with systems such as Blackboard, Moodle, Sakai, eLibrary platforms, and other e-learning platforms. LA continues to gain much attention as digitization of the learning environment is advancing. It allows educators to analyze and interpret data correctly, setting in motion strategies that offer points of leverage and performance for and among students. The use of predictive systems and Early Warning Systems (EWS) in education addressed the issue of student dropouts and suggested interventions for improving students’ performance. High dropout rates in education continue to be a global challenge; however, EWS provide a solution to curb the menace in education in various developed nations, such as the United States, Australia, and the United Kingdom. Developing countries face similar problems of dropouts in the educational sector, but not much research has been undertaken in LA to address the intervention needed to leverage the situation. Some studies have designed models predicting student failure and success, student attrition, student performance and final grades. Most of these studies have focused on only virtual learning environments (VLE) datasets. Nonetheless, this study uses student “activity logs”, “student courses”, “demographics”, and “student assessments” to design a predictive model to identify at-risk students (ARS) from not graduating. The purpose of this study is to use LA and Machine Learning (ML) to analyse the characteristics and behaviours of students in order to identify those who may need support to improve their academic performance. The study adopted the systematic literature review (SLR) approach to determine which emerging ML tools/techniques have been applied successfully in designing predictive systems in education. The SLR enabled the study to identify ML methods and the features that have been used in the domain of predictive systems in education. The study used an integrated 5-step LA process and ML workflow to predict which students are likely to dropout. Using the OULAD dataset, the findings indicated that non-graduated students had habits of not revising the learning materials early before the final exams. Although it was noted that both graduated and non-graduated students access the learning materials simultaneously, variations were recorded in the habits of assignment submission and revision patterns. Graduated students recorded higher clicks for accessing VLE activities than non-graduated students, which signifies that the graduated students interacted more with course activities than non-graduated students. The study also compared different ML algorithms and determined the method that achieved the best predictive accuracy that could be adapted in higher educational institutions. The evaluation of the models concluded that the ensemble machine-learning methods outperformed the traditional methods. The Random Forest ensemble learning algorithms outperformed the GB, Catboost, KNN, LG and NB on the accuracy, precision, recall and f-1 score. The study identified important features such as “date of-assignment-submission”, “sum_clicks-of-activities”, “score on the assessment”,”date-of registration”, “date-of-assignment-submission”, “studied-credits”, and “date-the-student unregistered” for predicting students dropout in higher educational institution (HEI). The model was trained with the important features to predict ARS and achieved an accuracy of 92% in less time than using all the features. The research indicated that implementing LA and ML techniques can effectively identify students at risk of withdrawing from higher education. In view of this, the study concluded that targeted interventions can be developed to mitigate the risk of students dropping out of school through improved learning outcomes