Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms

Abstract

The global trend of student social well-being has steadily declined in recent years. As a result, the need for a personalized recommendation classification model that can accurately assess and identify the individual student’s social well-being has become increasingly important. This article will discuss the development of an adaptive personalized recommendation classification model for students’ social well-being based on personality trait determinants. Social well-being is a field that analyses society, individual behavioural patterns, behavioural networks, and cultural elements of daily life. Social well-being develops critical thinking by understanding the social frameworks that affect humans by exposing the social basis of daily actions. For instance, when students are pleased, their academic achievement, behaviour, social integration, and happiness improve. This study classifies the effects of the Big 5 Personality Traits (Extraversion, Openness, Agreeableness, Emotional Stability, and Conscientiousness) on students’ Industry 4.0 Social Well-being levels by analyzing their demographic and personality traits. A dataset was gathered through a survey distributed to students in a selected institution. The classifier’s accuracy was assessed using the WEKA tool on a data set of 286 occurrences and 19 traits, and a confusion matrix was constructed. After analyzing the results of all algorithms, it was determined that the IBk and Randomizable Filtered Classifier algorithms give the best accuracy on social well-being readiness, with a comparable percentage value of 91.26%. The agreeableness personality trait, which represents a person’s level of pleasantness, politeness, and helpfulness, had the greatest influence on the social well-being of the students. They have a positive outlook on human behaviour and get along well with others. Since social well-being contributes to a person’s increased quality of life and happiness, improving students’ current quality of life would lead to the development of a social parameter that can assess the growth of a country and the increased happiness offamilies and communities. Personality traits models have become an increasingly important tool for understanding and predicting human behavior. By analyzing different personality trait models, we can gain insights into how accurately and reliably they can predict individual behavior. This is especially useful in fields such as psychology, marketing, and recruitment, where understanding the nuances of individual personalities can be critical to success. In this study, how different personality trait models compare in terms of accuracy and reliability is explored using different machine learning algorithms using the WEKA tool. Personality trait models are increasingly being used to measure social well-being. This model is based on the idea that individuals’ personalities are composed of a set of underlying traits which can be measured and compared. By understanding these traits, we can better understand the students’ social well-being and how the environment around them may impact it.