Predicting Learning Effectiveness: The Roles of Active Note Management, Student Note-Sharing Practices, and AI-Supported Learning from Notes

11th International Scientific Conference Technics, Informatics and Education – TIE 2026, (pp. 465-472)

АУТОР(И) / AUTHOR(S): Gordana Rendulić Davidović , Nikola Radosavljević  and Brankica Šutić Milićević

Download Full Pdf  

DOI: https://doi.org/10.46793/TIE26.465RD

САЖЕТАК /ABSTRACT:

 This study examines the relationships between Active Note Management, Student Note-Sharing Practices, AI-Supported Learning from Notes, and students’ Learning Effectiveness, with particular emphasis on their relative contribution in predicting Learning Effectiveness. A quantitative research approach was employed, with data collected through a survey administered to university students. The data were analyzed using descriptive statistics, Pearson correlation analysis, and hierarchical multiple regression analysis. The results revealed significant positive relationships between all three predictors and Learning Effectiveness at the bivariate level. However, in the final hierarchical regression model, only Active Note Management remained a statistically significant predictor, indicating its dominant role in explaining Learning Effectiveness. The findings contribute to the literature on note-based learning by highlighting the importance of Active Note Management, while suggesting that Student Note-Sharing Practices and AI-Supported Learning from Notes may serve as complementary learning practices rather than independent predictors of Learning Effectiveness.

КЉУЧНЕ РЕЧИ / KEYWORDS: 

Active Note Management; Student Note-Sharing Practices; AI-Supported Learning from Notes; Learning Effectiveness; Higher Education

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This study was supported by the Ministry of Science, Technological Development and Innovation of Serbia, and these results are parts of the Grant No. 451-03-33/2026-03/ 200132, with University of Kragujevac – Faculty of Technical Sciences Čačak.

ЛИТЕРАТУРА / REFERENCES:

  • Stacy, E. M., & Cain, J. (2015). Note-taking and handouts in the digital age. American journal of pharmaceutical education, 79(7), 107. DOI: 10.5688/ajpe797107
  • Cojean, S., & Grand, M. (2024). Note-taking by university students on paper or a computer: Strategies during initial note-taking and revision. British Journal of Educational Psychology, 94, 557–570. DOI: 10.1111/bjep.12663
  • Kitjaroonchai, N., Marshall, P., Phasook, J., & Thararuenroeng, N. (2025). University students’ perceptions on Note-Taking and its impact on academic performance. Journal of English Teaching, 11(1), 1-19. DOI: 10.33541/jet.v11i1.6373
  • Salame, I. I., & Thompson, A. (2020). Students’ Views on Strategic Note-Taking and Its Impact on Performance, Achievement, and Learning. International Journal of Instruction, 13(2), 1-16. DOI: 10.29333/iji.2020.1321a
  • Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), DOI: 10.3390/educsci15030343
  • Mollick, E., & Mollick, L. (2023). Assigning AI: Seven approaches for students with prompts. Wharton School of the University of Pennsylvania & Wharton Interactive. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4475995
  • Fan, L., Deng, K. & Liu, F. (2025). Educational impacts of generative artificial intelligence on learning and performance of engineering students in China. Sci Rep 15, 26521.
    DOI: 10.1038/s41598-025-06930-w
  • Witherby, A. E., & Tauber, S. K. (2019). The current status of students’ note-taking: Why and how do students take notes? Journal of Applied Research in Memory and Cognition, 8 (2), 139–153. DOI: 10.1016/j.jarmac.2019.04.002
  • Courtney, M., Costley, J., Baldwin, M., Lee, K., & Fanguy, M. (2022). Individual versus collaborative note-taking: Results of a quasi-experimental study on student note completeness, test performance, and academic writing. The Internet and Higher Education, 55, DOI: 10.1016/j.iheduc.2022.100873
  • Morehead, K., Dunlosky, J., Rawson, K. A., Blasiman, R., & Hollis, R. B. (2019). Note-taking habits of 21st century college students: implications for student learning, memory, and achievement. Memory, 27(6), 807-819.
    DOI: 10.1080/09658211.2019.1569694
  • Panigrahi, R., Srivastava, P. R., & Panigrahi, P. K. (2021). Effectiveness of e-learning: the mediating role of student engagement on perceived learning effectiveness. Information Technology & People, 34(7), 1840-1862.
    DOI: 10.1108/ITP-07-2019-0380
  • Ma’amor, H., Achim, N. A., Ahmad, N. L., Roszaman, N. S., Anuar, N. N. K., Azwa, N. C. A. K., Rahman, S. N. A. & Hamjah, N. A. A. (2024). The effect of artificial intelligence (AI) on students’ learning. Information Management and Business Review, 16(3), 856-867.DOI:10.22610/imbr.v16i3S(I)a.4178
  • Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International journal of educational technology in higher education, 20(1),
    DOI: 10.1186/s41239-023-00411-8
  • Salame, I. I., Tuba, M., & Nujhat, M. (2024). Note-taking and its impact on learning, academic performance, and memory. International Journal of Instruction, 17(3), 599–616. DOI: 10.29333/iji.2024.17333a
  • Sivakumar, A., Jayasingh, S., & Shaik, S. (2023). Social Media Influence on Students’ Knowledge Sharing and Learning: An Empirical Study. Education Sciences, 13(7),
    DOI: 10.3390/educsci13070745
  • Nunnally, J. C. (1978). Introduction to Psychological Measurement. New York, NY: McGraw-Hill
  • Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Field, A. (2000). Discovering statistics using SPSS for Windows. Sage Publication: Thousand Oaks
Post Views: 10