Student Personality Analysis In Blended Mode Using Big Five

The study aimed to predict the student personalities in the blended learning environment using Big Five model. The dataset was collected through a survey and included information such as age, gender, college name, course, and 20 questions related to the Big Five model and based on these information the personality scores of students was analyzed. The one-way ANDVA, k-means clustering, and Support Vector Machines (SVM) algorithm were employed for data analysis and prediction. The one-way ANDVA test was used to examine the statistical differences among different groups and then K-means clustering technique was applied to group the students into 10 clusters. Support Vector Machines (SVM) was used for classification tasks to predict the Big Five personality trait scores of students. The result showed that SVM algorithm achieved an accuracy of 95% in predicting student personality traits based on the collected data. The findings provide valuable insights into the influence of blended learning on student personalities and have implications for personalized educational approaches and interventions.