Predicting Student Graduation Outcomes: An Evaluation of Project-Based Learning and Implementation of Naïve Bayes

Abstract

This study explores the application of the Naïve Bayes algorithm within the framework of Project-Based Learning (PjBL) to predict student graduation timing and likelihood. The evaluation of student competencies across several performance dimensions, such as problem analysis, project planning, data preparation, feature extraction, and algorithm implementation, demonstrates the effectiveness of the approach. Predictive analysis and result interpretation were successful, indicating a strong correlation between project-based learning outcomes and graduation success. Additionally, the research uncovers insights into the role of creativity and innovation in predicting student graduation. This study highlights the potential benefits of integrating PjBL into educational curricula and underscores the utility of the Naïve Bayes method in forecasting graduation outcomes in higher education. © 2025 Khairi Budayawan et al.; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. The article is published with Open Access at https://www.temjournal.com/

Keywords

Author Keywords

Project-Based Learning naive Bayes learning assessment project competency evaluation student graduation

Index Keywords

Author Affiliations

Universitas Negeri Padang, Padang, West Sumatra, Indonesia

Centre for Higher Education Research and Evaluation, Lancaster, Lancashire, United Kingdom

Funding & Acknowledgements

No funding information

References 10 References

1 Anggraeni, Desak Made, Systematic review of problem based learning research in fostering critical thinking skills, Thinking Skills and Creativity, 49, (2023)

2 Howard, Natalie Jane, Navigating blended learning, negotiating professional identities, Journal of Further and Higher Education, 45, 5, pp. 654-671, (2021)

3 Samala, Agariadne Dwinggo, Blockchain Technology in Education: Opportunities, Challenges, and Beyond, International Journal of Interactive Mobile Technologies, 18, 1, pp. 20-42, (2024)

4 Anwar, Muhammad, Blended Learning Based Project In Electronics Engineering Education Courses: A Learning Innovation after the Covid-19 Pandemic, International Journal of Interactive Mobile Technologies, 16, 14, pp. 107-122, (2022)

5 undefined, (2021)

6 Ahmad, Selamat Triono, Project-based Learning in Vocational Education: A Bibliometric Approach, International Journal of Modern Education and Computer Science, 15, 4, pp. 43-56, (2023)

7 Yu, Hao, Enhancing creative cognition through project-based learning: An in-depth scholarly exploration, Heliyon, 10, 6, (2024)

8 Fadli, Radinal, Effectiveness of Mobile Virtual Laboratory Based on Project-Based Learning to Build Constructivism Thinking, International Journal of Interactive Mobile Technologies, 18, 6, pp. 40-55, (2024)

9 Hung, Huichun, Applying educational data mining to explore students' learning patterns in the flipped learning approach for coding education, Symmetry, 12, 2, (2020)

10 Chen, Xieling, Two Decades of Artificial Intelligence in Education: Contributors, Collaborations, Research Topics, Challenges, and Future Directions, Educational Technology and Society, 25, 1, pp. 28-47, (2022)

Predicting Student Graduation Outcomes: An Evaluation of Project-Based Learning and Implementation of Naïve Bayes

How to Cite