Author(s)

Feng Jiexin ¹, Bai Wei ¹, Sun Kaili ¹

Affiliation(s)

¹ School of Trade and Economic, Haojing College of Shaanxi University of Science & Technology, Xi'an, Shaanxi, China

Corresponding Author

Feng Jiexin

ABSTRACT

This paper explores the teaching reform and practice of the "Data Analysis and Visualization" course based on the OBE-CDIO concept in the context of interdisciplinary integration. First, a three-tier progressive practical teaching system—basic practice, professional practice, and comprehensive practice—is established to meet the needs of different disciplines and industries, cultivating students' data analysis and interdisciplinary application abilities. Second, a four-dimensional teaching organization model, including demonstration-based, design-based, inquiry-based, and entrepreneurship-based approaches, is developed to enhance students' practical skills and innovative thinking through case demonstrations, visualization design, problem exploration, and business applications. Additionally, a diverse data analysis toolset, incorporating Excel, Power BI, R, and Python, is constructed to support business data analysis education. Furthermore, an interdisciplinary data analysis and visualization teaching database is developed, providing real-world business data to improve students' data application capabilities. Finally, a business-oriented data analysis methodology and model library, including regression analysis, clustering algorithms, and time series analysis, is established to help students master the complete process from data cleaning to decision optimization. This study provides an effective framework for cultivating high-quality business and technology professionals with expertise, technical skills, and innovation capabilities.

KEYWORDS

Interdisciplinary Integration, Data Analysis and Visualization, OBE-CDIO Teaching Reform

CITE THIS PAPER

Feng Jiexin, Bai Wei, Sun Kaili, Teaching Research and Practice of "Data Analysis and Visualization" Course Based on OBE-CDIO Concept in the Context of Interdisciplinary Integration. Curriculum and Teaching Methodology (2025) Vol. 8: 126-134. DOI: http://dx.doi.org/10.23977/curtm.2025.080118.

REFERENCES

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[3] Cheng, M., & Liu, Y. (2018). A Review of Data Visualization Techniques for Business Decision-Making. Journal of Data Science and Business Analytics, 6(2), 213-229.
[4] Liu, Z., & Zhang, L. (2017). Integration of Data Analysis and Visualization Tools in Business Education: Challenges and Opportunities. Journal of Business Education and Technology, 9(4), 200-215.
[5] Sharma, R. (2016). The Role of Big Data Analytics in Business Decision-Making. International Journal of Business Analytics, 14(2), 130-145. 
[6] Wang, J., & Sun, Q. (2021). Innovative Teaching Models for Data Science and Visualization in Business Schools. Journal of Business and Information Technology Education, 12(5), 172-187.
[7] Zhu, Y., & Li, P. (2015). Application of Data Mining and Visualization Techniques in Retail Business Analysis. Journal of Retail Technology, 10(3), 98-112.

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