COMPUTER DOMAIN
Published August 12, 2024 | Version v1
Journal article Open

Personalized Learning Assistance for Slow Learners

Authors/Creators

Description

The "Personalized Learning Assistance for Slow Learners" initiative introduces a groundbreaking platform meticulously crafted to meet the unique requirements of students in need of extra academic support. Featuring an intuitive interface equipped with secure facial recognition login, learners gain access to tailored subject lists aligned with their individual preferences and academic goals. Interactive quizzes and dynamically generated study materials adapt in real-time according to the student's performance, fostering deeper comprehension and addressing areas of weakness proactively. Advanced features such as text-to-speech functionality and instant feedback mechanisms offer invaluable assistance to students grappling with reading and writing difficulties, while an AI-powered chatbot facilitates collaborative learning opportunities. Through continuous progress tracking, educators and parents can swiftly intervene to ensure sustained academic progress, nurturing a lifelong love for learning in every student.

Files

Personalized Learning Assistance -Formatted Paper.pdf

Files (124.5 kB)

Additional details

References

  • 1. Wang, H., Tlili, A., Huang, R., Cai, Z., Li, M., Cheng, Z., ... & Fei, C. (2023). Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective. Education and information technologies, 28(7), 9113-9148.
  • 2. Hlosta, M., Herodotou, C., Papathoma, T., Gillespie, A., & Bergamin, P. (2022). Predictive learning analytics in online education: A deeper understanding through explaining algorithmic errors. Computers and Education: Artificial Intelligence, 3, 100108..
  • 3. Rott, K. J., Lao, L., Petridou, E., & Schmidt-Hertha, B. (2022). Needs and requirements for an additional AI qualification during dual vocational training: Results from studies of apprentices and teachers. Computers and education: Artificial intelligence, 3, 100102
  • 4. Yang, A. C., Chen, I. Y., Flanagan, B., & Ogata, H. (2022). How students' self-assessment behavior affects their online learning performance. Computers and Education: Artificial Intelligence, 3, 100058.
  • 5. Viswanathan, N., Meacham, S., & Adedoyin, F. F. (2022). Enhancement of online education system by using a multi-agent approach. Computers and Education: Artificial Intelligence, 3, 100057.
  • 6. Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., ... & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074.
  • 7. Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050.
  • 8. Syed, T. A., Palade, V., Iqbal, R., & Nair, S. S. K. (2018, September). Building a Dataset for Personalized Learning Recommendation System. In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2080-2086). IEEE.
  • 9. Wang M, Liang Y, Wenjun Wu.(2020). A Novel Intelligent System for Learning Programming. Beijing University of Technology.
  • 10. Graesser, A. C., Hu, X., Nye, B. D., VanLehn, K., Kumar, R., Heffernan, C., ... & Baer, W. (2018). ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International journal of STEM education, 5, 1-21.