Leveraging Ai-Powered Predictive Analysis To Reduce Dropout Rates in Online Learning Systems

Registration ID: IJNRD_307247

Published ID: IJNRD2202051

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Nagaraj Parvatha

predictive analytics, online learning, artificial intelligence, dropout rates, student retention, personalized interventions, dropout rates.

Online learning has grown to redefine education, and it is now known as unprecedented flexibility and worldwide accessibility for learners. Despite its transformative potential, online education faces a persistent challenge: The high dropout rates, between 40% and 80%, are much higher than in traditional classrooms. The issue is, however, negatively impacting the effectiveness and even the sustainability of online education. Factors contributing, key factors are lack of personalized support, little interaction with instructors and peers, and varying levels of learner motivation. The focus of this study is on using Artificial Intelligence (AI) powered predictive analytics to prevent dropout rates in online learning environments. The machine learning model that determines which students are at risk was first built using a simulated dataset containing demographic behavioral and engagement metrics. Finally, the model has an impressive 92.3% accuracy in predicting disengaged learners based on indicators of login frequency, quiz performance, and forum participation. Intervention strategies that could be simulated in the classroom, such as customized learning paths, mentorship programs as well as personalized feedback, reduced dropout rates by 40 percent compared to high-risk students. These findings make clear the potential for AI to revolutionize the timing, data-driven, and individualized interventions that will impact student retention. The results from that hypothetical simulation are promising but are important to validate in real-world scenarios. Finally, future research should build on these predictive models while working with them in more settings, with external variables (e.g. Socioeconomic variables) and ethical considerations (e.g. Data privacy and algorithmic fairness). By using AI-powered responsibility predictive analysis, we can move online learning into a more engaging, more supportive, and more equitable place.

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Nagaraj Parvatha (February-2022). Leveraging Ai-Powered Predictive Analysis To Reduce Dropout Rates in Online Learning Systems. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(2), 462-468. https://ijnrd.org/papers/IJNRD2202051.pdf

Volume 7 Issue 2, February-2022

Pages : 462-468

Paper Reg. ID: IJNRD_307247

Published Paper Id: IJNRD2202051

Research Area: Science and Technology

Author Type: Indian Author

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2202051.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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