rajansaluja@yahoo.com
@chandigarh university
Assistant Professor
Assistant Professor
EDUCATION
MCA/Ph.D.
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Science, Software, Multidisciplinary
Scopus Publications
Scopus Publications
Rajan Saluja, Munishwar Rai, and Rashmi Saluja
Frontier Scientific Publishing Pte Ltd
Academic success for students in any educational institute is the primary requirement for all stakeholders, i.e., students, teachers, parents, administrators and management, industry, and the environment. Regular feedback from all stakeholders helps higher education institutions (HEIs) rise professionally and academically, yet they must use emerging technologies that can help institutions to grow at a faster pace. Early prediction of students’ success using trending artificial intelligence technologies like machine learning, early finding of at-risk students, and predicting a suitable branch or course can help both management and students improve their academics. In our work, we have proposed a new student performance prediction model in which we have used ensemble machine learning with stacking of four multi-class classifiers, decision tree, k-nearest neighbor, Naïve Bayes, and One vs. Rest support vector machine classifiers. The proposed model predicts the final grade of a student at the earliest possible time and the suitable stream for a new student. A student dataset of over a thousand students from five different branches of an engineering institute has been taken to test the results. The proposed model compares the four-machine learning (ML) techniques being used and predicts the final grade with an accuracy of 93%.
Rajan Saluja and Munishwar Rai
IEEE
Providing good academic environment, infrastructure and various learning facilities is not sufficient for Higher Educational Institutions in this competitive era. HIEs have to adopt more trending technologies that can help students to achieve best professional growth. Students’ performance prediction in advance using Supervised and Unsupervised Machine Learning techniques is very much trending for learning contexts in HIEs as it helps administrators to design strategies for improving final results. This is possible as high-volume data about student’s personal, academic performance at school level and at the admission time is available in school, college and universities. Historical data can be utilized to analyze various learning capabilities of different students in different environment and that analysis can be used to predict performance of a newly admitted student. This research mentions an analysis of existing ML techniques in higher education for prediction of students’ success based on previous work done in the related area. We have studied a total of 40 relevant papers in which ML techniques are being proposed or implemented for students’ success prediction. A systematic analysis was done to synthesize and report the main results.