Abstract
Higher Education is considered vital for societal development. It leads to many benefits including a prosperous career and financial security. Virtual learning through cloud platforms has become fashionable as it is expediency and flexible to students. New student learning models and prediction outcomes can be developed by using these platforms. The appliance of machine learning techniques in identifying students at-risk is a challenging and concerning factor in virtual learning environment. When there are few students, it is easy for identification, but it is impractical on larger number of students. This study included 530 higher education students from various regions in India and the outcomes generated from online survey data were analyzed. The main objective of this research is to predict early identification of students at-risk in cloud virtual learning environment by analyzing their demographic characteristics, previous academic achievement, learning behavior, device type, mode of access, connectivity, self-efficacy, cloud platform usage, readiness and effectiveness in participating online sessions using four machine learning algorithms namely K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Random Forest (RF). Predictive system helps to provide solutions to low performance students. It has been implemented on real data of students from higher education who perform various courses in virtual learning environment. Deep analysis is performed to estimate the at-risk students. The experimental results exhibited that random forest achieved higher accuracy of 88.61% compared to other algorithms.
Highlights
During COVID-19 crisis, the entire education system all over the world has shifted towards virtual learning
We considered K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Random Forest (RF) as they are the most preferable algorithms by researchers to resolve related issues
Accuracy from Random Forest is 88.61% it is representing good performance based on accuracy, sensitivity and specificity compared to KNN, SVM and LDA
Summary
During COVID-19 crisis, the entire education system all over the world has shifted towards virtual learning. Cloud-based Virtual Learning Environment (VLE) is vital component of education in university environments This interactive platform enables learners to achieve education objectives during pandemic outbreak. Benefits of cloud computing includes collaborative learning environment, expense reduction, scalability, shareable content, usability, and global education. This induces the way online learning can be shared and distributed on diverse types of devices and platforms. Educators can widen most crucial sections, upload necessary audio/video materials to support contents in cloud-based platforms, etc. Despite of these advantages, students’ learning behaviors and interaction with digital contents is still limited. Prediction using classification techniques is an efficient and significant way to deliver timely intervention for dropout
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