This study presents a comprehensive analysis of student satisfaction within the emerging discipline of quantum courses at a higher vocational education institution. With the rapid advancement of the quantum industry, the development of robust and effective undergraduate curricula is paramount. Utilizing a structured questionnaire administered to students enrolled in the quantum major at Shenzhen Polytechnic University, this research quantitatively and qualitatively assesses satisfaction levels across multiple dimensions, including curriculum structure, course typology, scheduling, pedagogical methods, and assessment systems. K-means clustering was employed to segment students into distinct groups based on their satisfaction patterns. The findings indicate a generally positive reception of the curriculum's relevance to the field. However, areas necessitating improvement were identified, such as the need for more dynamic teaching methodologies, refined course scheduling, and enhanced practical alignment with industry needs. The analysis reveals significant variations in satisfaction based on demographic variables like gender and academic year. Based on the empirical results, this paper proposes a multifaceted set of evidence-based recommendations aimed at curriculum reform, pedagogical enhancement, and the fostering of a more engaging and effective learning environment. These recommendations are designed to elevate overall student satisfaction, thereby improving learning outcomes and better preparing graduates for careers in the high-tech quantum sector.

Zhougeng Lin, Yihao Huang. Cluster Algorithm Analysis of Course Satisfaction in Quantum-Related Disciplines. International Journal of New Developments in Education (2026), Vol. 8, Issue 5: 27-38. https://doi.org/10.25236/IJNDE.2026.080505.

[1] Biggs J, Tang C. Teaching for Quality Learning at University[M]. 4th ed. Maidenhead: Open University Press, 2011.

[2] National Research Council. Adapting to a Changing World: Challenges and Opportunities in Undergraduate Physics Education[R]. Washington, DC: The National Academies Press, 2013.

[3] Elliott C, Oty K, McArthur J, et al. The effect of an interdisciplinary science course on students' attitudes and beliefs[J]. Journal of the Scholarship of Teaching and Learning, 2001, 1(1): 1-15.

[4] Felder R M, Brent R. Understanding Student Differences[J]. Journal of Engineering Education, 2005, 94(1): 57-72.

[5] Kolb A Y, Kolb D A. Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education[J]. Academy of Management Learning & Education, 2005, 4(2): 193-212.

[6] Hattie J. Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement[M]. London: Routledge, 2009.

[7] Pascarella E T, Terenzini P T. How College Affects Students: A Third Decade of Research[M]. San Francisco: Jossey-Bass, 2005.

[8] Astin A W. What Matters in College? Four Critical Years Revisited[M]. San Francisco: Jossey-Bass, 1993.

[9] Lattuca L R, Stark J S. Shaping the College Curriculum: Academic Plans in Context[M]. 2nd ed. San Francisco: Jossey-Bass, 2009.

[10] Ramsden P. Learning to Teach in Higher Education[M]. 2nd ed. London: RoutledgeFalmer, 2003.

[11] Prosser M, Trigwell K. Understanding Learning and Teaching: The Experience in Higher Education [M]. Buckingham: Open University Press, 1999.

[12] Entwistle N, Tait H. Approaches to learning, evaluations of teaching, and preferences for contrasting academic environments[J]. Higher Education, 1990, 19(2): 169-194.

[13] Ambrose S A, Bridges M W, DiPietro M, et al. How Learning Works: Seven Research-Based Principles for Smart Teaching[M]. San Francisco: Jossey-Bass, 2010.

[14] Bligh D A. What's the Use of Lectures?[M]. San Francisco: Jossey-Bass, 2000.

[15] Wiggins G, McTighe J. Understanding by Design[M]. 2nd ed. Alexandria, VA: Association for Supervision and Curriculum Development, 2005.

[16] Wieman C E. Large-scale comparison of science teaching methods sends clear message[J]. Proceedings of the National Academy of Sciences, 2014, 111(23): 8319-8320.

[17] Fink L D. Creating Significant Learning Experiences: An Integrated Approach to Designing College Courses[M]. San Francisco: Jossey-Bass, 2003.

[18] Zywno M S, Waalen J K. The Effect of Individual Learning Styles on Student Outcomes in Technology-Enabled Education[J]. Global Journal of Engineering Education, 2002, 6(1): 35-44.

[19] Hughes G. Using blended learning to increase learner support and improve retention[J]. Teaching in Higher Education, 2007, 12(3): 349-363.

[20] Prince M. Does Active Learning Work? A Review of the Research[J]. Journal of Engineering Education, 2004, 93(3): 223-231.

[21] Freeman S, Eddy S L, McDonough M, et al. Active learning increases student performance in science, engineering, and mathematics[J]. Proceedings of the National Academy of Sciences, 2014, 111(23): 8410-8415.

[22] Deslauriers L, Schelew E, Wieman C. Improved Learning in a Large-Enrollment Physics Class[J]. Science, 2011, 332(6031): 862-864.

[23] Boud D, Feletti G. The Challenge of Problem-Based Learning[M]. 2nd ed. London: Kogan Page, 1997.

[24] Barrows H S. Problem-based learning in medicine and beyond: A brief overview[J]. New Directions for Teaching and Learning, 1996, 1996(68): 3-12.

[25] Garrison D R, Kanuka H. Blended learning: Uncovering its transformative potential in higher education[J]. The Internet and Higher Education, 2004, 7(2): 95-105.

[26] Lazowski R A, Hulleman C S. Motivation Interventions in Education: A Meta-Analytic Review[J]. Review of Educational Research, 2016, 86(2): 602-640.

[27] Ryan R M, Deci E L. Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions[J]. Contemporary Educational Psychology, 2000, 25(1): 54-67.

[28] Bransford J D, Brown A L, Cocking R R. How People Learn: Brain, Mind, Experience, and School [M]. Washington, D.C.: National Academy Press, 2000.