Author(s)

Li Wang ¹

Affiliation(s)

¹ School of Economics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, China

Corresponding Author

Li Wang

ABSTRACT

Amid the rapid advancement of the digital economy, big data technology has emerged as a core driver of industrial transformation. However, big data courses for non-computer science disciplines face persistent challenges, including students' weak technical foundations, disconnects between curricula and discipline-specific demands, and insufficient practical training. This study proposes a systematic pedagogical reform framework grounded in interdisciplinary education theory, integrating constructivism and CDIO engineering education models. The framework establishes a scenario-based teaching system with three pillars: (1) Modular content architecture ("foundation-core-extension") featuring cross-disciplinary integration of computer science, statistics, and domain-specific knowledge (e.g., medical imaging analysis); (2) Hierarchical teaching strategies with dynamic remediation mechanisms (e.g., Blockly-based visual programming for humanities students, API development for engineering cohorts); (3) A three-dimensional evaluation model (process-result-development) incorporating discipline-tailored assessments (financial analysis for business majors, sentiment analysis for humanities, environmental modeling for STEM fields). Practical implementation employs enterprise collaboration platforms for authentic case simulations (e.g., e-commerce user behavior analysis), cloud-native environments (Kaggle/AliCloud), and lightweight tools to strengthen full-process data competencies. The reform embeds data ethics education and ideological elements through privacy protection modules and scenario-based decision-making exercises. By aligning pedagogical design with industry needs and leveraging cloud-based resource integration, this framework provides a replicable model for enhancing non-computer science students' analytical capabilities and interdisciplinary literacy in big data education. Future research will explore AI-driven adaptive learning systems and longitudinal graduate competency tracking to optimize curricular efficacy.

KEYWORDS

Interdisciplinary education, big data foundation courses, non-computer science disciplines, data technology application ability, Python programming practice

CITE THIS PAPER

Li Wang, Instructional Design and Exploration of Big Data Fundamentals Course for Non-Computer Science Disciplines. Curriculum and Teaching Methodology (2025) Vol. 8: 99-107. DOI: http://dx.doi.org/10.23977/curtm.2025.080414.

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Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
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