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Citation: Jingjie Rao, Wenwen Cai, Jiahui Zhao, Xu Yang, Ziyan Yan, Tianjin Zhang, Hang Zhang. Digital Exploration of Analytical Chemistry Experiments in the Context of Machine Learning and Big Data: A Case Study on Water Hardness Measurement[J]. University Chemistry, ;2026, 41(1): 276-288. doi: 10.12461/PKU.DXHX202504104 shu

Digital Exploration of Analytical Chemistry Experiments in the Context of Machine Learning and Big Data: A Case Study on Water Hardness Measurement

1.
National Demonstration Center for Experimental Chemistry Education, College of Chemistry and Materials, Jiangxi Normal University, Nanchang 330022, China;
2.
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China;
3.
School of Software, Jiangxi Normal University, Nanchang 330022, China

Corresponding author: Hang Zhang, zhanghangjx@163.com
Received Date: 28 April 2025
Revised Date: 30 September 2025

Within the framework of digital-intelligent education, this study develops a digital teaching assistance system for analytical chemistry experiments by integrating machine learning and big data technologies, using water hardness determination as a representative example. The system captures titration images and employs color histograms combined with a support vector machine (SVM) model to determine titration endpoints, enabling automated result analysis, evaluation, and personalized learning feedback. This research successfully bridges traditional experimental methods with digital technologies, fostering students’ competencies in data analysis, interdisciplinary thinking, and practical skills. The proposed approach offers an innovative methodology for promoting digital transformation in university-level analytical chemistry laboratory education.
- Digital teaching,
- University-level chemistry experiments,
- Machine learning,
- Digital and intelligent
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