Analyzing the Contribution of Programming and Database Courses to Capstone Performance Using Multiple Linear Regression
DOI:
https://doi.org/10.24002/ijis.v9i1.13756Abstract
Capstone courses in software engineering require students to integrate knowledge and skills acquired from multiple prerequisite courses. However, not all prerequisite courses contribute equally to student success in capstone projects. This study investigates the contribution of individual programming and database courses to student performance in a software development capstone course using Multiple Linear Regression (MLR). Academic records from 482 undergraduate students were analyzed, including grades from prerequisite programming and database courses, with Grade Point Average (GPA) included as a control variable. Ordinary Least Squares (OLS) was used to estimate the regression model, while LASSO regression and partial correlation analysis were applied as supporting analyses to assess robustness and interpret direct relationships. The results indicate that Data Structures, Object-Oriented Programming, Web Programming, Software Project Management, and Platform-Based Programming have a significant positive contribution to capstone performance. In contrast, introductory courses show mainly indirect effects. Database Systems exhibits a reduced unique contribution after controlling for other courses, suggesting that its impact is embedded within broader development skills. These findings demonstrate that course-level academic data can provide actionable insights for curriculum evaluation and support data-driven improvements in capstone preparation.
References
[1] M. C. Bastarrica, D. Perovich, and M. M. Samary, “What can students get from a software engineering capstone course?,” in Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering and Education Track, ICSE-SEET 2017, Institute of Electrical and Electronics Engineers Inc., Jun. 2017, pp. 137–145. doi: 10.1109/ICSE-SEET.2017.15.
[2] S. Tenhunen, T. Männistö, M. Luukkainen, and P. Ihantola, “A systematic literature review of capstone courses in software engineering,” Inf Softw Technol, vol. 159, p. 1, Jul. 2023, doi: 10.1016/j.infsof.2023.107191.
[3] S. Howe, L. Rosenbauer, and S. Poulos, “The 2015 Capstone Design Survey Results: Current Practices and Changes over Time,” International Journal of Engineering Education, vol. 33, no. 5, pp. 1393–1421, 2017, [Online]. Available: https://scholarworks.smith.edu/egr_facpubs/9
[4] R. P. Bringula, A. D.V. Aviles, Ma. Ymelda C. Batalla, Ma. Teresa F. Borebor, M. Anthony D. Uy, and B. E. San Diego, “Factors Affecting Failing the Programming Skill Examination of Computing Students,” International Journal of Modern Education and Computer Science, vol. 9, no. 5, pp. 1–8, May 2017, doi: 10.5815/ijmecs.2017.05.01.
[5] S. Valstar, W. G. Griswold, and L. Porter, “The relationship between prerequisite proficiency and student performance in an upper-division computing course,” in SIGCSE 2019 - Proceedings of the 50th ACM Technical Symposium on Computer Science Education, Association for Computing Machinery, Inc, Feb. 2019, pp. 794–800. doi: 10.1145/3287324.3287419.
[6] E. D. Canedo, I. N. Bandeira, and P. H. T. Costa, “Challenges of Database Systems Teaching Amidst the Covid-19 Pandemic,” in Frontiers in Education Conference, FIE, Lincoln, NE, USA: Institute of Electrical and Electronics Engineers Inc., 2021, pp. 1–9. doi: 10.1109/FIE49875.2021.9637223.
[7] O. D. Oyerinde and P. A. Chia, “Predicting Students’ Academic Performances-A Learning Analytics Approach using Multiple Linear Regression,” Int J Comput Appl, vol. 157, no. 4, pp. 37–44, Jan. 2017.
[8] S. J. H. Yang, O. H. T. Lu, A. Y. Q. Huang, J. C. H. Huang, H. Ogata, and A. J. Q. Lin, “Predicting students’ academic performance using multiple linear regression and principal component analysis,” Journal of Information Processing, vol. 26, pp. 170–176, Jan. 2018, doi: 10.2197/ipsjjip.26.170.
[9] O. T. Omolewa, A. T. Oladele, A. A. Adeyinka, and O. R. Oluwaseun, “Prediction of student’s academic performance using k-means clustering and multiple linear regressions,” Journal of Engineering and Applied Sciences, vol. 14, no. 22, pp. 8254–8260, 2019.
[10] S. M. Khan, S. Muhammad, and S. A. Haider, “Performance Prediction of Computer Science Students in Capstone Software Engineering Course Through Educational Data Mining,” in 2021 ASEE Virtual Annual Conference Content Access, Arkansas Tech University, Jul. 2021. [Online]. Available: https://orc.library.atu.edu/faculty_pub_curr
[11] S. A. Licorish, M. Galster, G. M. Kapitsaki, and A. Tahir, “Understanding students’ software development projects: Effort, performance, satisfaction, skills and their relation to the adequacy of outcomes developed,” Journal of Systems and Software, vol. 186, pp. 1–0, Apr. 2022, doi: 10.1016/j.jss.2021.111156.
[12] A. M. Shahiri, W. Husain, and N. A. Rashid, “A Review on Predicting Student’s Performance Using Data Mining Techniques,” Procedia Comput Sci, vol. 72, pp. 414–422, 2015, doi: 10.1016/j.procs.2015.12.157.
[13] P. Dabhade, R. Agarwal, K. P. Alameen, A. T. Fathima, R. Sridharan, and G. Gopakumar, “Educational data mining for predicting students’ academic performance using machine learning algorithms,” in Materials Today: Proceedings, Elsevier Ltd, 2021, pp. 5260–5267. doi: 10.1016/j.matpr.2021.05.646.
[14] A. A. Saa, “Educational Data Mining & Students’ Performance Prediction,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 7, no. 5, pp. 212–220, 2016, [Online]. Available: www.ijacsa.thesai.org
[15] M.-S. Kim, Y.-H. Jeong, and G.-W. Kwak, “Factors affecting the performance of student project teams in capstone design programmes,” Global Journal of Engineering Education, vol. 26, no. 1, pp. 13–19, 2024.
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