Tác động của mô phỏng số đến động lực học tập của sinh viên ngành kĩ thuật trong dạy học nội dung “Ứng dụng tích phân xác định vào bài toán tính công” (học phần Giải tích)
Tóm tắt
In engineering education, Calculus is a foundational subject, which is often considered difficult and abstract, especially in practical applications such as definite integrals. Integrating numerical simulations into teaching is claimed as a potential approach to enhance visual learning and improve student motivation. This study analyzes the impact of numerical simulations in teaching the topic “Applications of definite integrals to work calculation problems” (Calculus course) on learning motivation of engineering students. The pedagogical experiment was conducted using a pre-test and post-test design on 80 first-year engineering students. Data were analyzed using t-tests for paired samples and Cohen's effect size (d). The results showed that the average scores of all learning motivation components increased significantly after the intervention, with the effect ranging from moderate to large. Dynamic simulations help students visualize the process of accumulating work, thereby clarifying the practical significance of definite integrals in mechanics.
Tài liệu tham khảo
Attard, C., & Holmes, K. (2020). An exploration of teacher and student perceptions of blended learning in mathematics classrooms. Mathematics Education Research Journal. https://doi.org/10.1007/s13394-020-00359-2
Borba, M. C., Askar, P., Engelbrecht, J., Gadanidis, G., Llinares, S., & Aguilar, M. S. (2016). Blended learning, e-learning and mobile learning in mathematics education. ZDM - Mathematics Education, 48(5), 589-610. https://doi.org/10.1007/s11858-016-0798-4
Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education. 711 Third Avenue, New York, NY 10017: Routledge. https://lccn.loc.gov/2017015256
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
Do, P. N., & Tang, M. D. (2025). A systematic review of mobile learning in mathematics education: Global and Vietnamese perspectives from 2008 to 2024. Ho Chi Minh City University of Education Journal of Science, 22(3), 487-499. https://doi.org/10.54607/hcmue.js.22.3.4457(2025)
Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM - Mathematics Education, 56, 281-292. https://doi.org/10.1007/s11858-023-01530-2
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE.
Hammoudi, M. M., & Grira, S. (2023). Improving students’ motivation in calculus courses at higher education institutions: Evidence from graph-based visualization of two models. EURASIA Journal of Mathematics, Science and Technology Education, 19(01), em2209. https://doi.org/10.29333/ejmste/12771
Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153, 103897. https://doi.org/10.1016/j.compedu.2020.103897
Jong, T. D. (2006). Computer simulations. Technological advances in inquiry learning. Science, 312(5773), 532-533. https://doi.org/10.1126/science.1127750
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.
Nguyễn Quang Linh, Trịnh Hương Quỳnh, Nguyễn Thị Phượng (2024). Khám phá ảnh hưởng của việc sử dụng “Mô phỏng PhET” đến năng lực khoa học tự nhiên và động lực học tập. Tạp chí Giáo dục, 24(9), 42-47. https://tcgd.tapchigiaoduc.edu.vn/index.php/tapchi/article/view/1763
OECD (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Paris: OECD Publishing. https://doi.org/10.1787/589b283f-en
OECD (2023). Education at a Glance 2023: OECD Indicators. Paris: OECD Publishing. https://doi.org/10.1787/e13bef63-en
Podolefsky, N. S., Moore, E. B., & Perkins, K. K. (2014). Implicit scaffolding in interactive simulations: Design strategies to support multiple educational goals. ArXiv: Physics Education.
Rutten, N., Joolingen, W. R., & Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(01), 136-153. https://doi.org/10.1016/j.compedu.2011.07.017
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Schunk, D., Meece, J., & Pintrich, P. (2014). Motivation in education: Theory, research, and applications. Pearson.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Tall, D. (2013). How Humans Learn to Think Mathematically: Exploring the Three Worlds of Mathematics. Cambridge University Press. https://doi.org/10.1017/CBO9781139565202
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. https//doi.org/10.5116/ijme.4dfb.8dfd
Tran, T. B., Bui, T. T., & Dang, T. L. (2024). Applying Technology to Online Mathematics Education nn the Digital Era. Journal of Science Educational Sciences 2024, 69(5B), 168-178. https://doi.org/10.18173/2354-1075.2024-0145
Tải xuống
Đã Xuất bản
Cách trích dẫn
Số
Chuyên mục
Giấy phép
Tác phẩm này được cấp phép theo Ghi nhận tác giả của Creative Commons Giấy phép quốc tế 4.0 .
