Decoding Student Personalities with Machine Learning
DOI:
https://doi.org/10.31305/rrijm.2024.v09.n06.046Keywords:
Student personality prediction, machine learning, supervised and unsupervised algorithmsAbstract
To create a curriculum that works, educational institutions must have a thorough understanding of each student's individual abilities. A universal learning program might not adequately meet the demands of all pupils since they differ in their understanding levels and rates of learning. Institutions can develop individualized strategies that accommodate students' skills by acknowledging the significance of individual learning styles. This enables students to finish the course within the allotted time while learning at their own speed. The creation of an intuitive application for predicting students' personalities is suggested by this study. This study employs student data as the main dataset and makes use of machine learning techniques, which are well known for their dependability in data analysis and prediction. In order to predict student personalities, the study uses both supervised and unsupervised learning algorithms. Ensemble approaches and parameter tuning are used to further increase accuracy.
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