A Longitudinal Evaluation of Student Knowledges and Skills Development Using Artificial Intelligence - Wahyu Sasongko Putro; Rosita Rosita; Rina Dwijuliani (JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian, 2025)
This study seeks to examine the progression of students' knowledge (P) and skills (K) scores in an Indonesian Vocational High School (SMK), focused on Multimedia, through an artificial intelligence (AI)–driven longitudinal analysis methodology. The research data comprises academic scores from 32 students gathered over five semesters and examined via data preprocessing, descriptive statistical analysis, and machine learning modelling employing the Random Forest algorithm. The results show that both knowledge and skills scores have been going up steadily over the semesters. The predictive model based on Random Forest works very well, with a high level of accuracy and a low level of prediction error. Additionally, Pearson correlation analysis and simple linear regression demonstrate that knowledge significantly and positively influences students' skills (p < 0.05), suggesting that proficiency in cognitive dimensions directly facilitates the enhancement of practical skills in vocational education. These results validate that the amalgamation of longitudinal analysis and artificial intelligence can enhance data-driven learning assessment and promote more precise academic decision-making in vocational education