PATH ANALYSIS APPROACH ON FACTORS AFFECTING GRADE REPETITION
Grade repetition is a practice of retaining children in the same grade when they have not reached the expected academic goals. It has several adverse effects, which may lead to broader social problems, and investigating relevant factors is necessary to alleviate it. This paper aimed to determine the direct and indirect factors of students’ grade repetition using the secondary data from the UCI Machine of Learning repository. It contained 5820 evaluation scores from the university students. The factors considered were teachers’ performance (TP), perceived subject difficulty (PSD), grade level (GL), and attendance (ATT). Pearson Moment of Correlation and Multiple regression were employed for data analyses to create a path model for grade repetition (GR). Results showed that those factors mentioned above were significantly (p<0.01) correlated except for the relationship between GL and ATT. The path model obtained in this study consisted of three dependent variables, ATT, PSD, and GR. TP was the sole significant predictor of ATT, which explained approximately 3.5% of the variance. Also, ATT and GL were the critical factors of PSD, explaining 19.2% in its variation and with ATT as its most substantial caused effect. GR has three significant predictors, ATT, PSD, and GL, which explained approximately 4.1% in its variance. Also, PSD has the most significant impact on GR. Hence, educators should consider such factors for the improvement of learners’ school experiences, which will result in the reduction of grade repetition.