Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions

Abstract

Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An attrition model was built and validated among support vector machine, decision tree, multilayer perceptron, and random forest algorithms. The machine learning algorithms were tested for accuracy, precision, recall, F-measure, and ROC using the 10-fold and the 5-fold comparative cross-validation techniques. In addition to the cross-validation technique, the chi-square feature selection mechanism was implemented to understand the algorithms’ training time and accuracy. The random forest emerged as the best-performing algorithm, with an accuracy of 70.98% and 69.74% for the 10-fold and the 5-fold cross-validation implementations, respectively.

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

“…In binary scenarios, researchers meticulously define the thresholds for high class imbalance, typically when the minority class constitutes less than 8% of the dataset [17,20,21,23,25] while acknowledging imbalance when the minority class falls below 35% [18,31]. For example, in a recent study, a random split allocated 85% of the dataset to training data, revealing a distribution of 53.38% for on-time graduations and 46.62% for late graduations [11].…”

“…To develop and evaluate predictive models in identifying the likelihood of students graduating on time during their studies in university.   [11]  [12]    [13]  [14]  [15]  [16]  [17]  [18]  [19]  [20]  [21]  [22]  [23]  [24]  [25]  [26]    [27]  [28]  [29]  [30]  [31]  Table 1 illustrates the prevalence of minority classes within datasets utilized by researchers to tackle the challenge of class imbalance in identifying GOT. This imbalance arises when the dominance of the majority class eclipses the presence of minority classes, resulting in biased predictive models that yield unpredictable outcomes [12,15,16,27,28,30].…”

Comparison and Analysis of Multiple Machine Learning Algorithms for Predicting Student Adaptation Levels in Online Education

“…For some students who lack computer skills and internet skills, they may encounter some difficulties, which will affect their learning [4]. Secondly, students need to have some self-management and self-study skills to be able to achieve good results in online learning [5]. The learning mode of online learning is very different from the traditional classroom teaching, students need to make their own study plan, manage their study time, evaluate their learning effect and so on, which will also cause some difficulties for some students who lack self-management and self-learning ability [6].…”

Using ML to Predict User Satisfaction with ICT Technology for Educational Institution Administration

“…All models were trained and validated using 5-fold cross-validation, and their performance was evaluated based on various metrics, including accuracy, precision, recall, and F1-score. The best-performing model was selected based on its overall performance and its ability to balance between precision and recall [47].…”