Within the context of higher education continuing studies, students exhibit significant heterogeneity in disciplinary foundations, technical experience, and learning motivation. The traditional model of uniform textbooks, uniform pacing and uniform assessment has resulted in low learning efficacy for data analytics courses. This research, grounded in differentiated instruction theory, constructs a pedagogical model featuring tiered objectives, graded content and diversified assessment. Leveraging an online learning platform, it enables data-driven precision decision-making and dynamic adjustments. Practice demonstrates that this model significantly enhances students’ sense of learning achievement and project completion rates, with student satisfaction exceeding 90%. Differentiated instruction effectively alleviates the challenge of teaching homogenisation in continuing education, providing a replicable paradigm for personalised talent development within this sector.

Jieting Yin, Haojie Gu. Differentiated Teaching Model for Data Analysis Courses in Higher Education Continuing Education: A Comprehensive Exploration. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 8-12. https://doi.org/10.25236/FER.2026.090502.

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