Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan

Abstract

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students’ performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

“…They found that these methods can effectively determine best practices to predict students performance. 59 developed a generalizable hybrid machine learning model to identify attributes that regulate students performance prediction during the COVID-19 and achieved an overall accuracy of 98.6%. They compared their model with several other frequently used machine learning algorithms.…”

Improving English Teaching Strategies From the Perspective of College Students' Mental Health

“…People began to devote themselves to mining some potential information from these massive data, which led to a variety of data mining technologies (Asad et al, 2023). Among them, education data mining is a new technology born in order to make full use of the huge data stream in the operation process of digital campus (Xu et al, 2023).…”

Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning

“…Baker (2010) provided an overview of EDM, encompassing five research directions: relationship identification, model discovery, data distillation for human judgment, clustering analysis, and prediction modeling [11]. Among the five directions of EDM, prediction (evaluation) of academic performance is the most popular application [12][13][14][15]. Thai-nghe et al (2010) proposed an innovative recommendation system that evaluates students' academic performance by extracting education-related data [16].…”