Exploration of AIGC-Enabled Teaching Model for “Big Data Platform Construction and Maintenance” Course in Higher Vocational Education

Abstract

In response to the challenges of disconnection between theory and practice, outdated resources, and significant variations in student proficiency in the traditional teaching of Big Data Platform Construction and Maintenance courses in higher vocational education, this paper proposes an AIGC-enabled course teaching model from the perspective of constructivist learning theory. The model deeply integrates AIGC tools into the entire process of “pre-class preparation, in-class guidance, and post-class consolidation,” constructing four key dimensions: professional resource generation, personalized learning diagnosis, situated practice support, and iterative professional capability development. Through practical application in actual teaching, results demonstrate that this model effectively enhances students’ troubleshooting and cluster maintenance capabilities, with students’ final comprehensive scores and willingness for independent learning significantly improved compared to the traditional model. This research provides a practical reference for the human-machine collaborative teaching reform of big data courses in higher vocational education.