THE SMART EXPERIMENTAL REFORM OF PRINCIPLES OF CHEMICAL ENGINEERING LABORATORY COURSE FOR CHEMICAL ENGINEERING MAJORS UNDER THE EMERGING ENGINEERING EDUCATION

Authors

  • BinBin Xin School of Chemical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.
  • HuiBo Qin School of Chemical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.
  • Chen Pan Architecture and Civil Engineering Institute, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.
  • Jun Yang School of Chemical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.
  • SuHua Wang School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.
  • Long Yu (Corresponding Author) School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.
  • Lei Wang (Corresponding Author) School of Chemical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China.

Keywords:

Emerging engineering education, Principles of chemical engineering experiment, Smart course, Blended online and offline teaching, Knowledge graph, Virtual simulation

Abstract

Driven by both the Emerging Engineering Education initiative and the digital transformation of education, experimental teaching in chemical engineering-related majors faces an urgent need to transition from "verification-oriented operations" to "smart education-oriented cultivation". Taking the "Principles of Chemical Engineering Experiment" course at Guangdong University of Petrochemical Technology as an example, this paper systematically elaborates on the existing foundation, construction goals, main contents, and expected outcomes of the smart reform of this course. The study proposes that by constructing a knowledge graph, recording MOOC experiment videos, improving the virtual simulation platform, establishing a blended online and offline teaching system, and empowering with AI technology, the traditional experimental teaching limitations of "teacher-centered, fixed procedures, and single evaluation" can be effectively broken. The practical path shows that the smart experimental reform not only enhances students' independent design and innovation abilities but also strengthens the cultivation of engineering ethics and the awareness of industry-university-research integration, providing a replicable reference paradigm for the high-quality development of similar experimental courses in chemical engineering.

References

[1] Jesiek B K, Woo S E, Parrigon, et al. Development of a situational judgment test for global engineering competency. Journal of Engineering Education, 2020, 109(3): 470-490.

[2] Konstantinidou Evi, Ronny Scherer. Teaching with technology: A large-scale, international, and multilevel study of the roles of teacher and school characteristics. Computers & Education, 2022, 179: 104424.

[3] Ren Shuming, Xu Mengjie, Wang Qian. M&A and firm' s R&D: evidence from Chinese listed manufacturing firms. China Industrial Economics, 2017(7): 137-155.

[4] Hadgraft Roger G, Anette Kolmos. Emerging learning environments in engineering education. Australasian Journal of Engineering Education, 2020, 25(1): 3-16.

[5] Wankat Phillip C. Progress in reforming chemical engineering education. Annual review of chemical and biomolecular engineering, 2013(4): 23-43.

[6] Zhang Mingqian (John), Newton Cheryl, Grove Jason, et al. Design and assessment of a hybrid chemical engineering laboratory course with the incorporation of student-centred experiential learning. Education for Chemical Engineers, 2020(30): 1-8.

[7] Kolil Vysakh Kani, Krishnashree Achuthan. Virtual labs in chemistry education: A novel approach for increasing student's laboratory educational consciousness and skills. Education and Information Technologies, 2024, 29(18): 25307-25331.

[8] Brinson James R. Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 2015, 87: 218-237.

[9] Zhang MJ, Newton C, Grove J, et al. Virtual and remote labs in education: A bibliometric analysis. Computers & Education, 2016, 98: 14-38.

[10] Chu Hui-Chun, Hwang Gwo-Haur, Tu Yun-Fang, et al. Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology, 2022, 38(3): 22-42.

[11] Chen, Xieling, Xie Haoran, Zou Di, et al. Application and theory gaps during the rise of artificial intelligence in education. Computers and education: Artificial intelligence, 2020(1): 100002.

[12] Domingues Lucília, Rocha Isabel, Dourado Fernando, et al. Virtual laboratories in (bio) chemical engineering education. Education for Chemical Engineers, 2010, 5(2): e22-e27.

[13] Abdel-Badeeh M Salem, Elena V Mikhalkina, Anastasia Y. Nikitaeva. Establishment of smart education system in modern universities: Concept, technologies and challenges. International journal of education and information technologies, 2019, 13: 180-188.

[14] Wang Gang, Jing He. A bibliometric analysis of recent developments and trends in knowledge graph research (2013–2022). IEEE Access, 2024, 12: 32005-32013.

[15] Alkhatib Omar J. An interactive and blended learning model for engineering education. Journal of Computers in Education, 2018, 5(1): 19-48.

Downloads

Published

2026-06-11

Issue

Vol. 4 No. 7 (2026)

Section

Research Article

DOI:

https://doi.org/10.61784/wjes3177

How to Cite

BinBin Xin, HuiBo Qin, Chen Pan, Jun Yang, SuHua Wang, Long Yu, Lei Wang. The Smart Experimental Reform Of Principles Of Chemical Engineering Laboratory Course For Chemical Engineering Majors Under The Emerging Engineering Education. World Journal of Educational Studies. 2026, 4(7): 1-7. DOI: https://doi.org/10.61784/wjes3177.