PREDICTIVE IDENTIFICATION OF AT-RISK LEARNERS IN SKILL-BASED LEARNING PLATFORMS.
Keywords:
.Abstract
This project focuses on the predictive identification of at-risk learners in skill-based online learning platforms using machine learning techniques. With the rapid growth of digital education and Massive Open Online Courses (MOOCs), online platforms have made learning accessible and flexible for a wide range of learners across the world. However, one of the major challenges faced by these platforms is the high dropout rate, where many learners discontinue courses before completion due to lack of motivation, low engagement, time constraints, or insufficient academic support. To address this issue, the proposed system aims to develop an Early Dropout Risk Prediction System that can identify students who are likely to disengage from a course at an early stage. The system collects learner interaction data such as login frequency, course progress, video engagement, quiz attempts, assignment submissions, and forum participation. This data undergoes preprocessing steps including data cleaning, handling missing values, removing duplicate records, and transforming raw data into meaningful engagement features through feature engineering. These processed features are then used to train machine learning models such as Logistic Regression, Random Forest, and XGBoost to classify learners as “At-Risk” or “Safe.” The trained model predicts the probability of dropout using a test dataset and generates risk scores for individual learners. Based on these predictions, instructors can identify at-risk students and provide timely interventions such as personalized feedback, guidance, or additional learning resources. By enabling early detection and proactive support, the system improves learner engagement, increases course completion rates, and supports data-driven decision-making in modern skill-based online learning environments.
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Published
2026-03-15
How to Cite
N. ANIL KUMAR, VEMULA DEVI SREE, DEVANABOINA NANDINI, KODURU INDU, MOHAMMAD MASTAN, & DOI: 10.48047/IJCNIS.18.3.60. (2026). PREDICTIVE IDENTIFICATION OF AT-RISK LEARNERS IN SKILL-BASED LEARNING PLATFORMS. International Journal of Communication Networks and Information Security (IJCNIS), 18(3), 51–60. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/8869
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Research Articles
