Instructional Design and Exploration of Big Data Fundamentals Course for Non-Computer Science Disciplines
Li Wang · Curriculum and Teaching Methodology · 2025
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.