In the cultivation of computer science-related talent, data structures serve as a fundamental cornerstone course. They directly influence students’ mastery of subsequent specialized subjects and their ability to solve engineering problems. At present, students commonly encounter difficulties such as conceptual abstractness, a disconnection between theory and practice, and weak knowledge-transfer skills. These challenges make it difficult for them to flexibly apply foundational structures to complex scenarios, thereby limiting the development of programming and algorithmic thinking. This study explores strategies to enhance the efficiency of data-structure learning, including strengthening prior knowledge reserves, deepening theoretical comprehension, reinforcing hierarchical practical training, and expanding the boundaries of knowledge application. These strategies operate across four interconnected levels: foundational support, cognitive deepening, ability transformation, and vision expansion. Specifically, early preparation lays the groundwork for efficient learning; theoretical study builds a systematic knowledge framework; practical training enables the transition from theory to skill; and knowledge expansion aligns learning with emerging disciplinary demands. Overall, these strategies help bridge the gap between theoretical learning and real-world application, equipping computer science students with both structural understanding and applied competence needed to address complex challenges in operating systems, databases, artificial intelligence, and related fields.

Jingwen Ye, Xiangqing Zhao, Zhuangqi Ding, Shiyin Zhao. Strategies for Improving the Teaching Efficiency of Computer Related Courses in Big Data Majors: A Case Study of Data Structures. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 231-237. https://doi.org/10.25236/FER.2026.090531.

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