Region - Educational Research and Reviews

In response to the challenges faced by the Data Science and Big Data Technology program in practical teaching—such as insufficient software and hardware resources, delayed updates of instructional software, fragmentation among theoretical courses, and difficulties in quantitatively assessing teaching outcomes—it is imperative to explore a teaching reform pathway that can effectively integrate theoretical instruction with practical application. Taking into account the interdisciplinary nature and strong application orientation of the Data Science and Big Data Technology program, this paper proposes a practice-oriented teaching reform model based on the concept of “competition-driven learning.” By introducing high-level discipline competitions with strong alignment to the major, competition projects are systematically embedded into both theoretical and practical courses, thereby restructuring the organization of course content. Furthermore, competition outcomes and certificates are employed as objective criteria for evaluating the teaching process and learning effectiveness. Practical implementation demonstrates that this teaching model effectively breaks down knowledge barriers between courses, promotes deep integration of theoretical knowledge and engineering practice, and significantly enhances students’ engineering practice capabilities, innovative application skills, and teamwork awareness. Moreover, it provides a replicable and referential implementation framework for reforming talent cultivation models in related disciplines.