Machine Learning-based Model for Prediction of Student's Performance in Higher Education

During the pandemic time, most students are learning in online mode without any physical interaction with a trainer. In this pandemic time, in the absence of physical interaction with students, it became very difficult to predict the performance of students. It's important in particular to supp...

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Bibliographic Details
Main Authors: Garg, Atul, Lilhore, Umesh Kumar, Ghosh, Pinaki, Prasad, Devendra, Simaiya, Sarita
Format: Conference Proceeding
Language:English
Subjects:
advanced learners
average
Education
Machine Learning
Naive Bayes
Organizations
Pandemics
Prediction model
Predictive models
Random Forest
Signal processing
Slow
Software
Support vector machines
SVM
Online Access:Request full text
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Summary:During the pandemic time, most students are learning in online mode without any physical interaction with a trainer. In this pandemic time, in the absence of physical interaction with students, it became very difficult to predict the performance of students. It's important in particular to support high-risk learners and ensure his\her retention, and perhaps to provide outstanding teaching materials and experiences, and also to improve the institution's rating and brand. This research article presents a machine learning-based model for predicting students' performance in higher education. The work also looks at the possibilities of utilizing visualizations & classification techniques to find significant factors in a small number of features that are used to build a predictive model. The research study analysis revealed that SVM (support vector machine), K*, random forest, and Naive Bayes techniques effectively train limited samples and generate appropriate prediction performance based on various parameters, i.e. precision, recall, F-measure.
ISSN:2688-769X
DOI:10.1109/SPIN52536.2021.9565999