EduMe – Student Guidance and Intelligent System for Personalized Learning Path

Abstract

EduMe, a web application aimed at enhancing the e-learning experience for students. EduMe comprises four core components: an Automated Personalized Timetable Generator, a Student Behavior Tracking Unit, a Text Summarizer and an Automatic Question Generator and Answer Assessment. These components address challenges such as personalized time management, study focus tracking, efficient content summarization, and interactive question generation. The application provides students with an adaptive learning environment, empowering them to excel in their academic pursuits through personalized resources and guidance. The system is developed using a blend of cutting-edge technologies including image processing, Natural Language Processing, machine learning algorithms and reinforcement learning. Overall, EduMe serves as a valuable tool to support self-study methods for undergraduate students, providing them with the necessary resources and guidance to optimize their learning experiences.

Country : Sri Lanka

1 A.H.L.R. Weerasinghe2 P.R.K. Peramuna3 V.D.M.H.D. Rathnayake4 K.A.D.A.U. Kanakasekra

  1. Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 11, November 2023 pp. 147-153

doi.org/10.47001/IRJIET/2023.711021

References

  1. D. Srinivasan, T. H. Seow, J. X. Xu, "Automated Time Table Generation Using Multiple Context Reasoning for University Modules," in IEEE Transactions on Education, vol. 55, no. 3, pp. 361-369, August 2012, doi: 10.1109/TE.2011.2163481.
  2. M. R. Bagul, S. C. Chaudhari, S. N. Nagare, P. R. Patil, and K. S. Kumavat, "A Novel Approach for Automatic Timetable Generation," in International Journal of Computer Applications, vol. 127, no. 10, pp. 26-31, October 2015, doi: 10.5120/ijca2015906511.
  3. M. S. Knowles, "Self-directed learning: a guide for learners and teachers," Association Press, 1975.
  4. Widyassari, A.P., Rustad, S., Shidik, G.F., Basiron, H., Syukur, A. and Affandy, A. (2021). Review of Automatic Text Summarization Techniques & Methods. In Proceedings of the International Conference on Advanced Computer Science and Information Technology (ICACSIT).
  5. M.D. Boud, "The role of self-assessment in self-directed learning," Higher Education, vol. 14, no. 6, pp. 725-735, 1985.
  6. D. Srinivasan, T. H. Seow, J. X. Xu, "Automated Time Table Generation Using Multiple Context Reasoning for University Modules," in IEEE Transactions on Education, vol. 55, no. 3, pp. 361-369, August 2012, doi: 10.1109/TE.2011.2163481.
  7. A.Bhaduri, "University Time Table Scheduling Using Genetic Artificial Immune Network," 2009 International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, India, 2009, pp. 289-292, doi: 10.1109/ARTCom.2009.117.
  8. S. Kwon, S. Lee, S. Lee, and J. Kim, "A machine learning approach for emotion recognition using EEG signals," IEEE Transactions on Affective Computing, vol. 10, no. 1, pp. 93-102, 2019.
  9. X. Li, J. Zhang, and J. Xu, "Facial expression recognition based on deep learning: a survey," Journal of Visual Communication and Image Representation, vol. 68, pp. 102908, 2020.
  10. B. Jiang, F. Huang, S. Shao, and X. Wang, "Multi-modal emotion recognition using physiological signals and facial expressions," IEEE Transactions on Affective Computing, vol. 12, no. 1, pp. 1-1, 2021.
  11. J. Liu, X. Guo, and Z. Huang, "A novel head movement-based teleoperation system for robot arm," IEEE Access, vol. 7, pp. 156731-156740, 2019.
  12. C. Lin, S. Yang, Y. Lin, and W. Chen, "Head movement-based neurological disorder detection system," IEEE Access, vol. 9, pp. 101316-101323, 2021.
  13. Patil, S.P. and Lanjewar, U.A. (2012). Text Summarization Techniques: A Brief Survey. International Journal of Computer Applications, 47(7), pp.42-46.
  14. Saikia, M. et al. (2018) “Aptitude question paper generator and answer verification system,” Advances in Intelligent Systems and Computing, pp. 129–136. Available at: https://doi.org/10.1007/978-981-13-1280-9_12.
  15. Sutton, R. S., & Barto, E. G. (2018). Reinforcement learning: An introduction. https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
  16. Balasubramanian, V., Anouneia, S. M., & Abraham, G. (2013). Reinforcement learning approach for adaptive e-learning systems using learning styles. Information Technology Journal, 12, 2306-2314.
  17. M. M. Rahman and F. H. Siddiqui, “An optimized abstractive text summarization model using peephole convolutional LSTM,” Symmetry (Basel)., vol. 11, no. 10, 2019, doi: 10.3390/sym11101290.
  18. A.Virani, R. Yadav, P. Sonawane, and S. Jawale, “Automatic question answer generation using T5 and NLP,” 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 2023.