「量子運算」是21世紀受矚目的新興科技，國內外等世界經濟強權逐漸看到量子運算的前瞻性及未來發展前景，理解量子運算的相關知識，對於社會影響可謂越來越加重要。然而，該主題涉及量子力學與運算等資訊科技內涵，對於高中生而言，涉及微觀世界的量子運算概念，在現今教學現場仍舊過於抽象。而理解抽象思維概念，並能將其應用於現實世界的抽象推理能力，在學習抽象概念的過程中扮演著重要的角色。
為幫助高中生理解量子運算主題，本研究欲擬定視覺化模擬輔助教學策略，並以此策略開發視覺化模擬輔助教學平臺，將「量子疊加」、「量子糾纏」、「量子量測」三大量子運算核心概念依序以「具體表徵」、「抽象表徵」呈現，並設置「修改與觀察」功能，讓學生與所學概念互動。本研究採準實驗研究法，以評估上述視覺化模擬輔助教學策略對高中生在量子運算之學習成就（包含學習過程中的概念建構狀況、課後的學習成就）、學習態度之影響，以及探討視覺化模擬輔助教學策略對不同抽象推理能力學生學習量子運算之影響。
研究結果發現：(1) 視覺化模擬輔助教學策略並未對學生量子運算學習成就產生顯著影響。然而，從課堂中所填寫的學習單與成就測驗問答題作答結果中可以發現，相較於講述教學，使用視覺化模擬輔助教學策略學習量子運算的學生描述概念時較能引用專業術語，並能說明因果以更精確描述概念元素與元素間的關聯性(2) 視覺化模擬輔助教學策略未對學生量子運算學習態度產生顯著影響。雖然在整體教學過程中，學生有良好的學習感受，但未來繼續鑽研量子運算概念的意願較低。然而，從學生的質性回饋結果中，學生對視覺化模擬教學給予正面反饋。視覺化模擬教學的「具體表徵」幫助學生連結現實世界情境，「抽象表徵」則輔助學生理解抽象概念。此外，利用「修改與觀察」功能，學生能操作、修改各項參數以呈現不同結果，並透過觀察以加深理解。(3) 雖然不同教學策略對量子運算學習成就無顯著影響，但在採用講述式教學時，高抽象推理能力學生的學習成就顯著高於低抽象推理能力的學生，採用視覺化模擬教學策略時則無顯著差異，因此推論視覺化模擬輔助教學策略有助於縮小不同抽象推理能力學生在量子運算學習成就上的差距。在學習態度方面，視覺化模擬輔助教學策略對學生的學習態度整體而言未產生顯著影響，但在學習態度中的「抽象概念學習感受」面向，教學策略與抽象推理能力產生顯著交互作用，視覺化模擬輔助教學策略能幫助高抽象推理能力的學生增強學習抽象概念時的正面感受與自信心。高抽象推理能力學生透過視覺化模擬輔助教學策略，對於抽象概念的學習感受較好，這點也反映出本研究的視覺化模擬輔助教學策略對高抽象推理能力的學生更具正面效果。
本研究亦發現學生在進行量子運算概念學習時的困難，除了教學主題本身不易理解之外，學生的先備知識不足亦造成在學習上的阻礙。視覺化模擬輔助教學策略中的「操作與修改」能幫助學生進行概念的統整、觀察不同實驗環境下的量子現象變化，對本次研究參與者而言是最有效的輔助策略，建議未來若要針對該主題進行教學，在視覺化模擬輔助教學策略上應給予學生更多機會與概念進行更深入的互動、引導學生統整概念；在進行量子運算教學前，亦可對學生講解並複習物理基本概念，以減緩因先備知識不足導致的學習困難。

Quantum computing is a rapidly emerging technology in the 21st century. Leading economic powers are increasingly recognizing its potential and future development prospects. Thus, realizing the concepts of quantum computing has recently become more important due to its societal impact. However, the concepts of quantum computing, which involves quantum physics knowledge and pertains to the microscopic world, remain highly abstract for high school students. The ability to comprehend abstract concepts and apply them to real-world scenarios, which is known as “abstract reasoning ability,” plays a crucial role in the learning process of abstract concepts.
To assist high school students realize quantum computing concepts, the study aims to develop simulation-assisted instruction for teaching quantum computing. The instruction focused on the three core concepts of quantum computing: ”Quantum Superposition,” “Quantum Entanglement,” and “Quantum Measurement” through both concrete and abstract representation. Additionally, through “modification and observation”, students can interact with and explore these concepts. A quasi-experimental research design will be employed to evaluate the effectiveness of the simulation-based instruction. The study will examine its impact on high school students' learning achievement in quantum computing, including their conceptual construction during the learning process and learning achievements after class, as well as their learning attitude. Furthermore, the study investigate examines how visualization simulation influences students with different abstract reasoning abilities in learning quantum computing.
The experiment results revealed that: (a) there is no significant difference in students' learning achievement in quantum computing between different instructional strategies. However, an analysis of students' worksheets and responses to achievement test questions suggests that students who learned quantum computing through simulation-assisted instruction were more likely to use technical terminology and describe causal relationships more precisely than those who were taught through traditional lecture-based instruction. They were also better able to describe the connections between various conceptual elements. (b) There is no significant difference in students' learning achievement toward quantum computing. Though the students generally reported positive learning experiences during the instruction, they showed low motivation to further explore quantum computing concepts in the future. However, qualitative feedback from students indicated a positive perception of the simulation-assisted instruction. The “concrete representation” feature helped students relate quantum computing concepts to real-world contexts, while the “abstract representation” feature enhanced their understanding of abstract concepts. Additionally, the "modification and observation" function allowed students to manipulate variables/parameters and observe corresponding outcomes, which helped deepen their understanding. (c) The visual simulation-assisted instruction effectively reduced the gap between students with different abstract reasoning abilities in learning achievement. However, it didn’t significantly impact learning attitudes for students at different levels of abstract reasoning ability. In the aspect of the learning experience with abstract concepts, a significant interaction effect was observed between different teaching strategies and students' abstract reasoning ability in “learning attitude toward abstract concepts.” The simulation-assisted instruction enhanced the confidence and positive learning experience of students with high abstract reasoning ability when engaging with abstract concepts. These findings suggest that the proposed simulation-assisted instruction had a more positive impact on students with high abstract reasoning ability, especially regarding their experience with learning abstract concepts.
The study also identified the challenges students encounter when learning quantum computing concepts. In addition to the inherent difficulty of the subject matter, a lack of prior knowledge also posed a significant obstacle to their learning process. The "modification and observation" feature in the visual simulation-assisted instruction proved to be the most effective support for the participants in this study. This feature helped students integrate concepts and observe how quantum phenomena change under different experimental conditions. Based on the findings, future instruction on this topic should provide students with more opportunities for deeper interaction with quantum computing concepts through simulation-assisted instruction to improve their understanding. To further reduce learning difficulties stemming from a lack of prior knowledge, it is also recommended that students be introduced to and review fundamental physics concepts before engaging in quantum computing lessons.

十二年國民基本教育課程綱要 國民中學暨普通型高級中等學校 科技領域
Chen, Z.-H., Chiang, Y.-T., Sung, R.-F., Chen, T.-R., & Chen, T.-Y. (2019). 普通型及技術型高級中等學校科技領域資訊科技全一冊. 科友圖書股份有限公司.
Sun, Y. Q. (2019, September 22). 超級電腦算萬年 Google量子電腦花三分鐘. 自由時報. Retrieved from https://news.ltn.com.tw/news/world/paper/1319641
Abudarin, M. (2017). The Development of Students Worksheets for Constructing Knowledge of the Fundamental Laws of Chemistry. In 5th SEA-DR (South East Asia Development Research) International Conference 2017 (SEADRIC 2017) (pp. 53-57). Atlantis Press.
Aiello, C. D., Awschalom, D. D., Bernien, H., Brower, T., Brown, K. R., Brun, T. A., Caram, J. R., Chitambar, E., Di Felice, R., Edmonds, K. M., Fox, M. F. J., Haas, S., Holleitner, A. W., Hudson, E. R., Hunt, J. H., Joynt, R., Koziol, S., Larsen, M., Lewandowski, H. J., … Zwickl, B. M. (2021). Achieving a quantum smart workforce. Quantum Science and Technology, 6(3), 030501. https://doi.org/10.1088/2058-9565/abfa64
Alnoukari, M., Shafaamry, M., & Aytouni, K. (2013). Simulation for Computer Sciences Education.
Balamurugan, K. S., Sivakami, A., Mathankumar, M., & Ahmad, I. (2024). Quantum computing basics, applications and future perspectives. Journal of Molecular Structure, 1308, 137917.
Bawah, L. (2009, Senin, Oktober). Lantai Tanah: Definition of learning achievement. Lantai Tanah. http://lantaitanah.blogspot.com/2009/10/definition-of-learning-achievement.html
Bouchée, T., De Putter - Smits, L., Thurlings, M., & Pepin, B. (2022). Towards a better understanding of conceptual difficulties in introductory quantum physics courses. Studies in Science Education, 58(2), 183–202. https://doi.org/10.1080/03057267.2021.1963579
Bowen, B. (2015). Comparing traditional versus alternative sequencing of instruction when using simulation modeling. 16(1), 6.
Campos, N., Nogal, M., Caliz, C., & Juan, A. A. (2020). Simulation-based education involving online and on-campus models in different European universities. International Journal of Educational Technology in Higher Education, 17(1), 8. https://doi.org/10.1186/s41239-020-0181-y
Chiofalo, M. L., Foti, C., Michelini, M., Santi, L., & Stefanel, A. (2022). Games for Teaching/Learning Quantum Mechanics: A Pilot Study with High-School Students. Education Sciences, 12(7), 446. https://doi.org/10.3390/educsci12070446
Cook, D. A., Hamstra, S. J., Brydges, R., Zendejas, B., Szostek, J. H., Wang, A. T., ... & Hatala, R. (2013). Comparative effectiveness of instructional design features in simulation-based education: systematic review and meta-analysis. Medical teacher, 35(1), 867-898.
Costa, A., Morais, C., Freitas, A., Belmiro, J., Rocha, R., Vanderlei, I., Camboim, K., & Araujo, J. (2023). Using Visual Resources to Assist in the Teaching-Learning Process of Operating Systems Through the Development of a Simulator. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), 1–6. https://doi.org/10.23919/CISTI58278.2023.10211759
Dangur, V., Avargil, S., Peskin, U., & Dori, Y. J. (2014). Learning quantum chemistry via a visual-conceptual approach: students' bidirectional textual and visual understanding. Chemistry Education Research and Practice, 15(3), 297-310.
Davis, N. A., & La Cour, B. R. (2022, September). Quantum computing for the faint of heart. In 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (pp. 669-672). IEEE.
Deshpande, A. A., & Huang, S. H. (2011). Simulation games in engineering education: A state‐of‐the‐art review. Computer Applications in Engineering Education, 19(3), 399–410. https://doi.org/10.1002/cae.20323
Develaki, M. (2017). Using Computer Simulations for Promoting Model-based Reasoning: Epistemological and Educational Dimensions. Science & Education, 26(7–9), 1001–1027. https://doi.org/10.1007/s11191-017-9944-9
Fowler, M., Hallstrom, J., Hollingsworth, J., Kraemer, E., Sitaraman, M., Sun, Y.-S., Wang, J., & Washington, G. (2021). Tool-Aided Learning of Code Reasoning with Abstraction in the CS Curriculum. Informatics in Education, 20(4), 533–566. https://doi.org/10.15388/infedu.2021.24
Gómez-Veiga, I., Vila Chaves, J. O., Duque, G., & García Madruga, J. A. (2018). A new look to a classic issue: Reasoning and academic achievement at secondary school. Frontiers in psychology, 9, 400.
Hoai, V. T. T., Son, P. N., Em, V. V. D., & Duc, N. M. (2023). Using 3D molecular structure simulation to develop chemistry competence for Vietnamese students. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2300. https://doi.org/10.29333/ejmste/13345
Hurrion, R. D. (1978). An investigation of visual interactive simulation methods using the job-shop scheduling problem. Journal of the Operational Research Society, 29(11), 1085-1093.
Kamat, V. R., & Martinez, J. C. (2001). Visualizing simulated construction operations in 3D. Journal of computing in civil engineering, 15(4), 329-337.
Khodaeifaal, S. (2022). Updated and adapted curriculum and pedagogy of physics with the fourth industrial revolution and quantum revolution: From waves principles to quantum mechanics fundamentals. In 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (pp. 653-668). IEEE.
Kohnle, A., Bozhinova, I., Browne, D., Everitt, M., Fomins, A., Kok, P., Kulaitis, G., Prokopas, M., Raine, D., & Swinbank, E. (2014). A new introductory quantum mechanics curriculum. European Journal of Physics, 35(1), 015001. https://doi.org/10.1088/0143-0807/35/1/015001
Kohnle, A., & Rizzoli, A. (2017). Interactive simulations for quantum key distribution. European Journal of Physics, 38(3), 035403. https://doi.org/10.1088/1361-6404/aa62c8
Lateef, F. (2010). Simulation-based learning: Just like the real thing. Journal of Emergencies, Trauma, and Shock, 3(4), 348. https://doi.org/10.4103/0974-2700.70743
Lee, H., Plass, J. L., & Homer, B. D. (2006). Optimizing cognitive load for learning from computer-based science simulations. Journal of educational psychology, 98(4), 902.
Lin, S., Hao, J., & Sun, L. (2018, October). Quflow: Visualizing parameter flow in quantum circuits for understanding quantum computation. In 2018 IEEE Scientific Visualization Conference (SciVis) (pp. 37-41). IEEE.
Macal, C. M. (2001). Simulation and Visualization.
Markovits, H., & Doyon, C. (2011). Using analogy to improve abstract conditional reasoning in adolescents: Not as easy as it looks. European Journal of Psychology of Education, 26(3), 355–372. https://doi.org/10.1007/s10212-010-0054-1
Mashhadi, A., & Woolnough, B. (1999). Insights into students’ understanding of quantum physics: Visualizing quantum entities. European Journal of Physics, 20(6), 511–516. https://doi.org/10.1088/0143-0807/20/6/317
McKagan, S. B., Perkins, K. K., Dubson, M., Malley, C., Reid, S., LeMaster, R., & Wieman, C. E. (2008). Developing and researching PhET simulations for teaching quantum mechanics. American Journal of Physics, 76(4), 406–417. https://doi.org/10.1119/1.2885199
Mnguni, L. E. (2014). The theoretical cognitive process of visualization for science education. SpringerPlus, 3, 1-9.
Nyirahabimana, P., Minani, E., Nduwingoma, M., & Kemeza, I. (2023). Students’ perceptions of multimedia usage in teaching and learning quantum physics: Postassessment. Journal of Baltic Science Education, 22(1), 37–56. https://doi.org/10.33225/jbse/23.22.37
Olsen, R. V. (2002). Introducing quantum mechanics in the upper secondary school: A study in Norway. International Journal of Science Education, 24(6), 565–574. https://doi.org/10.1080/09500690110073982
Perkins, K., Adams, W., Dubson, M., Finkelstein, N., Reid, S., Wieman, C., & LeMaster, R. (2006). PhET: Interactive Simulations for Teaching and Learning Physics. The Physics Teacher, 44(1), 18–23. https://doi.org/10.1119/1.2150754
Podolefsky, N. S., & Finkelstein, N. D. (2006). Use of analogy in learning physics: The role of representations. Physical Review Special Topics - Physics Education Research, 2(2), 020101. https://doi.org/10.1103/PhysRevSTPER.2.020101
Quweider, M., & Khan, F. (2017). Visualization as Effective Instructional and Learning Tools in the Computer Science Curriculum. In 2017 ASEE Annual Conference & Exposition. https://doi.org/10.18260/1-2--29107
Raymer, M. G., & Monroe, C. (2019). The US National Quantum Initiative. Quantum Science and Technology, 4(2), 020504. https://doi.org/10.1088/2058-9565/ab0441
Riedel, M. F., Binosi, D., Thew, R., & Calarco, T. (2017). The European quantum technologies flagship programme. Quantum Science and Technology, 2(3), 030501. https://doi.org/10.1088/2058-9565/aa6aca
Rizman Herga, N., Glažar, S. A., & Dinevski, D. (2015). Dynamic visualization in the virtual laboratory enhances the fundamental understanding of chemical concepts. Journal of Baltic Science Education, 14(3), 351–365. https://doi.org/10.33225/jbse/15.14.351
Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136–153. https://doi.org/10.1016/j.compedu.2011.07.017
Satanassi, S., Fantini, P., Spada, R., & Levrini, O. (2021). Quantum Computing for high school: An approach to interdisciplinary in STEM for teaching. In Journal of Physics: Conference Series, 1929(1), 012053. https://doi.org/10.1088/1742-6596/1929/1/012053
Scalise, K., Timms, M., Moorjani, A., Clark, L., Holtermann, K., & Irvin, P. S. (2011). Student learning in science simulations: Design features that promote learning gains. Journal of Research in Science Teaching, 48(9), 1050–1078. https://doi.org/10.1002/tea.20437
Seegerer, S., Michaeli, T., & Romeike, R. (2021). Quantum Computing As a Topic in Computer Science Education. In Proceedings of the 16th Workshop in Primary and Secondary Computing Education, 1–6. https://doi.org/10.1145/3481312.3481348
Šiaulys, T. (2021). Developing Interactive Visualizations Focusing on Computational Thinking in K-12 Computer Science Education. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 2, 680–681. https://doi.org/10.1145/3456565.3460025
Snir, J., Smith, C., & Grosslight, L. (1993). Conceptually enhanced simulations: A computer tool for science teaching. Journal of Science Education and Technology, 2, 373-388.
Sorby, S. A. (2009). Educational Research in Developing 3‐D Spatial Skills for Engineering Students. International Journal of Science Education, 31(3), 459–480. https://doi.org/10.1080/09500690802595839
Statter, D., & Armoni, M. (2020). Teaching abstraction in computer science to 7th grade students. ACM Transactions on Computing Education (TOCE), 20(1), 1-37.
Stoddart, T., Abrams, R., Gasper, E., & Canaday, D. (2000). Concept maps as assessment in science inquiry learning-a report of methodology. International Journal of Science Education, 22(12), 1221-1246.
St-Aubin, B., & Wainer, G. (2023). A Survey of Visualization Capabilities for Simulation Environments. In 2023 Annual Modeling and Simulation Conference (ANNSIM) (pp. 13-24). IEEE.
Vavra, K. L., Janjic-Watrich, V., Loerke, K., Phillips, L. M., Norris, S. P., & Macnab, J. (2011). Visualization in science education. Alberta Science Education Journal, 41(1), 22-30.
Vermaas, P. E. (2017). The societal impact of the emerging quantum technologies: A renewed urgency to make quantum theory understandable. Ethics and Information Technology, 19(4), 241–246. https://doi.org/10.1007/s10676-017-9429-1
Weidner, C. A., Ahmed, S. Z., Jensen, J. H. M., Sherson, J. F., & Lewandowski, H. J. (2020). Investigating student use of a flexible tool for simulating and visualizing quantum mechanics. In 2020 Physics Education Research Conference. https://doi.org/10.1119/perc.2020.pr.Weidner
Wen, C.-T., Liu, C.-C., Chang, H.-Y., Chang, C.-J., Chang, M.-H., Fan Chiang, S.-H., Yang, C.-W., & Hwang, F.-K. (2020). Students’ guided inquiry with simulation and its relation to school science achievement and scientific literacy. Computers & Education, 149, 103830. https://doi.org/10.1016/j.compedu.2020.103830
Zable, A., Hollenberg, L., Velloso, E., & Goncalves, J. (2020, November). Investigating immersive virtual reality as an educational tool for quantum computing. In Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology (pp. 1-11).