Machine-Learning Based Solution for Enhancing the Performance of Undergraduate Research Projects

Abstract

Research projects are a critical point of a student’s life as they help to generate new knowledge and understanding in a particular field. University students struggle immensely when completing their research projects due to lack of knowledge about the research process. In order to assist students in this process, “LEARNBOOST’’ progressive web application provides dashboards with valuable and meaningful insights about the research projects, research areas, research groups, publications and competitions. These dashboards will help the students to get a clear understanding about the past research projects in a more effective manner. In addition, the system provides a research area prediction system that predicts the students research area of interest when they provide the research topic. These predictions are generated using Natural Language Processing (NLP) Transformers so that the students’ students research topics will be identified more accurately. Moreover, “LEARNBOOST” student performance enhancement system provides a recommendation system that identifies student research area of interests through text inputs and suggest leading research papers where students can contribute to the further development of new ideas, technologies, and innovations. And through an abstract summarization, students can easily get the abstract summarized into few sentences. We present the results of a pilot study in which the proposed system was used to support a group of students and demonstrate its effectiveness in improving student performance.

Country : Sri Lanka

1 W. K. C. Thiwanka2 Subasinghe M. P.3 Dissanayake D. M. M. A.4 Wijewardane W. A. M. B.5 H. M. Samadhi Chathuranga6 Wishalya Tissera

  1. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka
  2. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka
  3. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka
  4. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka
  5. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka
  6. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka

IRJIET, Volume 7, Issue 11, November 2023 pp. 461-468

doi.org/10.47001/IRJIET/2023.711062

References

  1. I.K. Seneviratne, B. A. S. D. Perera, R. S. D. Fernando,L. K. B. Siriwardana and U. U. S. K. Rajapaksha, "Student and Lecturer Performance Enhancement System using Artificial Intelligence," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India,2020,pp.    88-93, doi: 10.1109/ICISS49785.2020.9315981.
  2. H. Samin and T. Azim, "Knowledge Based Recommender System for Academia Using Machine Learning: A Case Study on Higher Education Landscape of Pakistan," in IEEE Access, vol. 7, pp. 67081-67093, 2019, doi: 10.1109/ACCESS.2019.2912012.
  3. J. Beel, B. Gipp, S. Langer, and C. Breitinger, “Research-paper recommender systems: a literature survey,” International Journal on Digital Libraries, vol. 17, no. 4, pp. 305–338, Jul. 2015, doi: https://doi.org/10.1007/s00799-015- 0156-0.
  4. O. O. Adebola, C. T. Tsotetsi, and B. I. Omodan, “Enhancing Students’ Academic Performance in University System: The Perspective of Supplemental Instruction,” International Journal of Learning, Teaching and Educational Research, vol. 19, no. 5, pp. 217–230, May 2020, doi: https://doi.org/10.26803/ijlter.19.5.13.
  5. B. Albreiki, N. Zaki, and H. Alashwal, “A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques,” Education Sciences, vol.  11, no. 9, p.             552, Sep.               2021,      doi: https://doi.org/10.3390/educsci11090552.
  6. “Sri Lanka Number of Graduating | Economic Indicators| CEIC,”  www.ceicdata.com. https://www.ceicdata.com/en/sri-lanka/university-education- statistics/number-of-graduating (accessed Apr. 01, 2023).
  7. F. Zhang, G. An and Q. Ruan, "Transformer-based Natural Language Understanding and Generation," 2022 16th IEEE International Conference on Signal Processing (ICSP), Beijing, China, 2022, pp. 281-284, doi: 10.1109/ICSP56322.2022.9965301.
  8. “Natural Language Processing with Transformers, Revised Edition [Book],” www.oreilly.com. https://www.oreilly.com/library/view/naturallanguage-processing/9781098136789/ (accessed Apr. 01, 2023).
  9. D. Rothman, Transformers for Natural Language Processing. Packt Publishing Ltd, 2021.
  10. O. Chavarriaga, B. Florian-Gaviria and O. Solarte P., "Recommender system based on student competencies assessment results," 2014 9th Computing Colombian Conference (9CCC), Pereira, Colombia, 2014, pp. 103-108, doi: 10.1109/ColumbianCC.2014.6955351.
  11. T. T. Dien, B. H. Loc and N. Thai-Nghe, "Article Classification using Natural Language Processing and Machine Learning," 2019 International Conference on Advanced Computing and Applications (ACOMP), Nha Trang, Vietnam, 2019, pp. 78-84, doi: 10.1109/ACOMP.2019.00019.
  12. A. Duran-Dominguez, J. A. Gomez-Pulido, D. Rodriguez-Lozano and F. Pajuelo-Holguera, "Selecting latent factors when predicting student performance in online campus by using recommender systems," 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Caceres, Spain, 2018, pp. 1-6, doi: 10.23919/CISTI.2018.8399227.
  13. N. Yanes, A. M. Mostafa, M. Ezz and S. N. Almuayqil, "A Machine Learning-Based Recommender System for Improving Students Learning Experiences," in IEEE Access, vol. 8, pp. 201218-201235, 2020, doi: 10.1109/ACCESS.2020.3036336.
  14. N. Torres, "Recommender Systems for Education: A case of Study Using Formative Assessments," 2022 41st International Conference of the Chilean Computer Science Society (SCCC), Santiago, Chile, 2022, pp. 1-6, doi: 10.1109/SCCC57464.2022.10000363.
  15. M. Ceyhan, S. Okyay, Y. Kartal and N. Adar, "The Prediction of Student Grades Using Collaborative Filtering in a Course Recommender System," 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 2021, pp. 177-181, doi: 10.1109/ISMSIT52890.2021.9604562.
  16. Department of Census and Statistics. Available at: http://www.statistics.gov.lk/abstract2021/CHAP14 (Accessed: 20 October 2023).