Multimodel Prediction Score Based on Academic Procrastination Behavior in E-Learning - Sartana, Bruri Trya; Nugroho, Supeno Mardi Susiki; Yuhana, Umi Laili; Purnomo, Mauridhi Hery (Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 2025)
This research investigates the impact of academic procrastination on student performance in online learning environments and explores a multimodel approach for grade prediction. Academic procrastination is a well-documented issue that negatively affects learning outcomes, often leading to lower academic performance and increased dropout rates in self-paced learning platforms. This study analyzes behavioral data from 377 students, extracted from Moodle activity logs, which record real-time student interactions with learning materials. To address the gap in understanding procrastination patterns through activity logs, key procrastination-related features were derived from timestamps of task access, submission, and engagement duration. Using K-Means clustering with the Elbow method, students were categorized into three procrastination clusters: low procrastination with high academic performance, high procrastination with low performance, and moderate procrastination with average performance. Seven machine learning models were evaluated for predicting student grades, with Random Forest (RF) achieving the highest accuracy (R² = 0.812, MAE = 6.248, RMSE = 8.456). These findings highlight the potential of using activity logs to analyze procrastination patterns and predict student performance, allowing educators to develop early intervention strategies that support at-risk students and improve learning outcomes.