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Examination design and course quality evaluation of “project-based learning” process based on front-door adjustment |
QIAN Chen1, KUANG Yi1, ZHANG Jing2,3 |
1.College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hu'nan 411105; 2. College of Electrical and Information Engineering, Hu'nan University, Changsha 410006; 3. Accreditation Guidance Center, Xiangtan University, Xiangtan, Hu'nan 411105 |
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Abstract Engineering education is an important part of higher education in China, and engineering education professional certification is an internationally accepted engineering education quality assurance system, and it is also an important basis for achieving international mutual recognition of engineering education and engineer qualification. “Project-based learning” is a new course teaching method to meet the requirements of engineering education professional certification to examine students' ability. Because of the influence of students' “inertia” and other hybrid factors, the examination results in the conventional course quality evaluation can not truly reflect the ability improvement that students can obtain in learning. This paper introduces the “front-door adjustment” of causal inference science. When facing the confounding factors that are difficult to control and observe, on the one hand, it guides and optimizes the teaching and examination design of project-based learning to improve the adverse effects of confounding factors on learning and results. On the other hand, it is used to evaluate the real causal effect of project-based teaching method on students' learning results.
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Received: 01 July 2022
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Cite this article: |
QIAN Chen,KUANG Yi,ZHANG Jing. Examination design and course quality evaluation of “project-based learning” process based on front-door adjustment[J]. Electrical Engineering, 2022, 23(10): 74-79.
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URL: |
http://dqjs.cesmedia.cn/EN/Y2022/V23/I10/74
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[1] 王东明, 陈都鑫. 因果推断:起源和发展[J]. 控制工程, 2022, 29(3): 464-473. [2] 王天思. 大数据中的因果关系及其哲学内涵[J]. 中国社会科学, 2016(5): 22-42, 204-205. [3] 白永梅, 孙华鸽, 杜建. 知识图谱:一种系统性构建因果图的方法[J]. 首都医科大学学报, 2022, 43(4): 584-591. [4] 朱迪亚·珀尔, 达纳·麦肯齐. 为什么[M]. 江生, 于华, 译. 北京: 中信出版社, 2019. [5] SCRIVEN M.“The methodology of evaluation”. In perspectives of curriculum evaluation[M]. Chicago: Rand McNaliy and Company, 1967. [6] 金华, 方积乾. 因果推断中的混杂控制[J]. 生物数学学报, 2001(3): 362-366. [7] PEARL J.Causality: models, reasoning, and infer- ence[M]. Camb: Cambridge University Press, 2000. [8] GILLIES D.Causality: models, reasoning, and infer- ence by Judea Pearl[J]. The British Journal for the Philosophy of Science, 2001, 52(3): 613-622. [9] 郭娇, 吴寒天. 大数据时代的因果推断——教育政策评估的新路径[J]. 重庆高教研究, 2022, 10(4): 39-48. [10] 涂良川. 因果推断证成强人工智能的哲学叙事[J]. 哲学研究, 2020(12): 110-121. [11] 李家宁, 熊睿彬, 兰艳艳, 等. 因果机器学习的前沿进展综述[J/OL]. 计算机研究与发展, 2021. |
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