Advancing Outcome-Based Education Through Student Engagement and Computational Innovation: A Case Study in Number Theory and Combinatorics

Authors

  • Hui Li Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Author
  • Shining Yu Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Author
  • Yichi Zhang Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Author
  • Chenlong Liu Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Author

DOI:

https://doi.org/10.65638/2978-5634.2025.01.01

Keywords:

Outcome-based education, Curriculum design, Knowledge graph, Mathematical statistics

Abstract

We present a comprehensive case study of implementing Outcome-Based Education (OBE) in the “Number Theory and Combinatorics" course for computer science majors. By systematically aligning curriculum design, assessment methods, and teaching strategies with clearly defined learning outcomes, we show the case how OBE principles can be effectively operationalized in STEM education. The reform integrates advanced computational technologies—including self-developed knowledge graphs for conceptual structuring and data-driven visualization tools for performance analytics. Through multi-dimensional assessment and active learning initiatives, students are transformed from passive recipients into active co-creators of the educational process.

References

Ahmad, A., Ray, S., & Nawaz, A. (2024). Current and emerging trends in python programming learning. 2024 9th International STEM Education Conference (iSTEM-Ed), 1-6. https://doi.org/10.1109/iSTEM-Ed62750.2024.10663187

Asbari, M., & Novitasari, D. (2024). Outcome-based education model: Its impact and implications for lecturer creativity and innovation in higher education. International Journal of Social and Management Studies, 5(5), 22-31.

Bitar, N., & Davidovich, N. (2024). Transforming pedagogy: The digital revolution in higher education. Education Sciences, 14(8). https://doi.org/10.3390/educsci14080811

Broy, M., Brucker, A. D., Fantechi, A., Gleirscher, M., Havelund, K., Kuppe, M. A., Mendes, A., Platzer, A., Ringert, J. O., & Sullivan, A. (2024). Does every computer scientist need to know formal methods? Form. Asp. Comput., 37(1). https://doi.org/10.1145/3670795

Cao, J., Fang, J., Meng, Z., & Liang, S. (2024). Knowledge graph embedding: A survey from the perspective of representation spaces. ACM Comput. Surv., 56(6). https://doi.org/10.1145/3643806

Córdova-Esparza, D.-M., Romero-González, J.-A., Córdova-Esparza, K.-E., Terven, J., & López-Martínez, R.-E. (2024). Active learning strategies in computer science education: A systematic review. Multimodal Technologies and Interaction, 8(6). https://doi.org/10.3390/mti8060050

Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01

Deng, T. (2024). The interdependence of educational alienation and equality: Rethinking china’s examination-oriented education system. Cogent Education, 11(1), 2385770. https://doi.org/10.1080/2331186X.2024.2385770

Forum, W. E. (2023). The future of jobs report 2023. https://www.weforum.org/reports/the-future-of-jobs-report-2023/.

Ji, Z., Lee, N., Fries, J., Yu, T. B., Sun, Y., Wang, H. J., et al. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys (CSUR). https://doi.org/10.1145/3571730

Jia, J., Wang, T., Zhang, Y., & Wang, G. (2024). The comparison of general tips for mathematical problem solving generated by generative AI with those generated by human teachers. Asia Pacific Journal of Education, 44(1), 8-28. https://doi.org/10.1080/02188791.2023.2286920

Le, B., Lawrie, G. A., & Wang, J. T. H. (2022). Student self-perception on digital literacy in STEM blended learning environments. Journal of Science Education and Technology, 31, 303-321. https://doi.org/10.1007/s10956-022-09956-1

Li, H. (2020). Pseudo-random scalar multiplication based on group isomorphism. Journal of Information Security and Applications, 53, 102534. https://doi.org/10.1016/j.jisa.2020.102534

Makda, F. (2025). Digital education: Mapping the landscape of virtual teaching in higher education - a bibliometric review [Journal Article]. Education and Information Technologies, 30(2), 2547-2575. https://doi.org/10.1007/s10639-024-12899-2

Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2023). Automated grading and feedback tools for programming education: A systematic review. ACM Trans. Comput. Educ., 24(1). https://doi.org/10.35542/osf.io/wpsgk

Mouratidis, K., & Papagiannakis, A. (2021). COVID-19, internet, and mobility: The rise of telework, telehealth, e-learning, and e-shopping. Sustainable Cities and Society, 74, 103182. https://doi.org/10.1016/j.scs.2021.103182

Mufanti, R., Carter, D., & England, N. (2024). Outcomes-based education in indonesian higher education: Reporting on the understanding, challenges, and support available to teachers. Social Sciences and Humanities Open, 9, 100873. https://doi.org/10.1016/j.ssaho.2024.100873

Ortega-Alvarez, J. D., Mohd-Addi, M., Guerra, A., Krishnan, S., & Mohd-Yusof, K. (2025). Creating student-centric learning environments through evidence-based pedagogies and assessments. In R. Kandakatla, S. Kulkarni, & M. E. Auer (Eds.), Academic leadership in engineering education: Learnings and case studies from educational leaders around the globe (pp. 123-142). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-68282-7_7

Ouyang, F., Dinh, T. A., & Xu, W. (2023). A Systematic Review of AI-Driven Educational Assessment in STEM Education. Journal for STEM Education Research, 6(3), 408-426. https://doi.org/10.1007/s41979-023-00112-x

Roehrig, G. H., Dare, E. A., Ring-Whalen, E., & Wieselmann, J. R. (2021). Understanding coherence and integration in integrated STEM curriculum. International Journal of STEM Education, 8(1), 2. https://doi.org/10.1186/s40594-020-00259-8

Samala, A. D., Rawas, S., Criollo-C, S., Bojic, L., Prasetya, F., Ranuharja, F., & Marta, R. (2024). Emerging technologies for global education: A comprehensive exploration of trends, innovations, challenges, and future horizons [Journal Article]. SN Computer Science, 5(8), 1175. https://doi.org/10.1007/s42979-024-03538-1

Shahzad, T., Mazhar, T., Tariq, M. U., Ahmad, W., Ouahada, K., & Hamam, H. (2025). A comprehensive review of large language models: Issues and solutions in learning environments [Journal Article]. Discover Sustainability, 6(1), 27. https://doi.org/10.1007/s43621-025-00815-8

Soto Rodríguez, E. A., Fernández Vilas, A., & Díaz Redondo, R. P. (2021). Impact of computer-based assessments on the science’s ranks of secondary students. Applied Sciences, 11(13), 6169. https://doi.org/10.3390/app11136169

Spady, W. G. (1988). Organizing for results: The basis of authentic restructuring and reform. Educational Leadership, 46(2), 4-8.

Taş, E. (2024). Data literacy education through university-industry collaboration [Journal Article]. Information and Learning Sciences, 125(5/6), 389-405. https://doi.org/10.1108/ILS-06-2023-0077

Wang, Y., & Liu, Y. (2024). Construction of a virtual simulation practical teaching system for intelligent manufacturing under the background of new engineering. Computer Applications in Engineering Education, 32(5), e22768. https://doi.org/10.1002/cae.22768

Yang, X., Wang, Z., Zhang, H., Ma, N., Yang, N., Liu, H., Zhang, H., & Yang, L. (2022). A review: Machine learning for combinatorial optimization problems in energy areas. Algorithms, 15(6). https://doi.org/10.3390/a15060205

Zhu, Y. (2024). A knowledge graph and BiLSTM-CRF-enabled intelligent adaptive learning model and its potential application. Alexandria Engineering Journal, 91, 305-320. https://doi.org/10.1016/j.aej.2024.02.011

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Published

2025-07-18

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