Home⇛Asia Pacific Journal of Management and Sustainable Development⇛vol. 13 no. No. 1, Part 2 (2025)

Data-Driven Teaching: Using Course Feedback To Inform And Improve It Instruction

Maria Cristina M. Ramos

Discipline: Education

 

Abstract:

This study explores the role of student course feedback in enhancing pedagogical practices within Information Technology (IT) education. Recognizing the importance of adapting to evolving student needs, the research focuses on the Bachelor of Science in Information Technology (BSIT) program at Lyceum of the Philippines University – Batangas. Using a quantitative descriptive research design, feedback was gathered from 108 students across multiple semesters through a standardized online questionnaire. The data was analyzed using descriptive statistics and Pearson correlation to determine relationships between feedback and academic performance. Findings reveal that student satisfaction is shaped by several key factors: course relevance, organization, teaching quality, assessment methods, learning environment, and counseling services. The study also establishes a positive, albeit weak, correlation between student feedback and final grades, suggesting that while feedback reflects perceptions of course effectiveness, other factors may also influence academic outcomes. The results emphasize the value of data-driven teaching and advocate for a proactive approach to curriculum enhancement. Recommendations include regular updates to course content, the use of diverse teaching methods, improved student-instructor communication, timely feedback, and the integration of technology in instruction. Ultimately, this research supports the use of student feedback as a critical tool for continuous improvement in IT education, benefiting both learners and educators.



References:

  1. Katawazai, R. (2021). Implementing outcome-based education and student-centered learning in Afghan public universities: the current practices and challenges. Heliyon, 7(5).
  2. Woodrow, J., Malik, A., & Piech, C. (2024, March). Ai teaches the art of elegant coding: Timely, fair, and helpful style feedback in a global course. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 1442-1448).
  3. Lin, L., Zhou, D., Wang, J., & Wang, Y. (2024). A systematic review of big data driven education evaluation. Sage Open, 14(2), 21582440241242180.
  4. Reichgelt, J., & Smith, T. C. (2024). The Effect of Course Delivery Mode on Student Performance and Student Satisfaction: A Case Study. Trends in Higher Education, 3(4), 872-884.
  5. Pereira, E., Nsair, S., Pereira, L. R., & Grant, K. (2024). Constructive alignment in a graduate-level project management course: An innovative framework using large language models. International Journal of Educational Technology in Higher Education, 21(1), 25.
  6. Valtonen, T., López-Pernas, S., Saqr, M., Vartiainen, H., Sointu, E. T., & Tedre, M. (2022). The nature and building blocks of educational technology research. Computers in Human Behavior, 128, 107123.
  7. Hingpit, S. P. (2024). The On-The-Job Training Experiences of Business Administration Students of Philippine Electronics and Communication Institute of Technology Butuan City. JPAIR Multidisciplinary Research, 57(1), 83-112.
  8. Masitoh, F., & Sufirmansyah, S. (2022). Google Classroom Application in Blended Learning: Indonesian EFL Learners’ Perception. Education and Linguistics Knowledge Journal, 4(1), 1-13.
  9. Afzal, A., Kamal, M., Diyanti, N., Proano, R. D., & Jaan, A. (2021). Implementation of Mandatory Counseling Sessions and Availability of Support Centers for Mental Well Being of Heath Care Workers during the COVID-19 Pandemic. Archives of Internal Medicine Research, 4(01).
  10. Stamov Roßnagel, C., Fitzallen, N., & Lo Baido, K. (2021). Constructive alignment and the learning experience: relationships with student motivation and perceived learning demands. Higher Education Research & Development, 40(4), 838-851.
  11. Tsai, C. L., Ku, H. Y., & Campbell, A. (2021). Impacts of course activities on student perceptions of engagement and learning online. Distance Education, 42(1), 106-125.
  12. Ocan, J., Tukwasibwe, C., & Oketch, C. (2021). Analysis of stakeholders involvement in the implementation of the MA literature curriculum at Kabale University using e-learning approach.
  13. Elumalai, K. V., Sankar, J. P., Kalaichelvi, R., John, J. A., Menon, N., Alqahtani, M. S. M., & Abumelha, M. A. (2021). Factors affecting the quality of e-learning during the COVID-19 pandemic from the perspective of higher education students. COVID-19 and education: Learning and teaching in a pandemic-constrained environment, 189(3), 169.
  14. Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292.
  15. Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292.
  16. Messer, M., Brown, N. C., Kölling, M., & Shi, M. (2024). Automated grading and feedback tools for programming education: A systematic review. ACM Transactions on Computing Education, 24(1), 1-43.
  17. Garcia, A., & Bongo, S. M. (2021, September). A Predictive Analysis Approach on the Academic Performance of BSIT Students Basis for Teaching-Learning Augmentation Program. In 2021 4th International Conference on Information Communication and Signal Processing (ICICSP) (pp. 588-592). IEEE.
  18. Olipas, C. N. P., Leona, R. F., Villegas, A. C. A., Cunanan Jr, A. I., & Javate, C. L. P. (2021). The Academic Performance and the Computer Programming Anxiety of BSIT Students: A Basis for Instructional Strategy Improvement. Online Submission, 7(6), 125-129.

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic