Research on the Application of Artificial Intelligence Technology in Data-Driven Course Scheduling of Private Colleges

Abstract

Course scheduling is a fundamental and core task in academic affairs management of private colleges. The uncertainty and lag in student data restrict the efficiency of course scheduling and the quality of teaching operation. Based on the actual school-running conditions of private colleges, this paper systematically sorts out four typical problems, including fluctuations in freshman data, lag in student status change information, delay in course selection data for students upgrading from junior college to undergraduate programs, and complicated student information of innovative classes. It deeply analyzes their cumulative impacts on the progress of course scheduling, teacher allocation and classroom resources. Relying on artificial intelligence, big data and intelligent algorithm technologies, this paper constructs a data-driven course scheduling system featuring “data integration – intelligent prediction – algorithm-based course scheduling – dynamic adjustment”. It proposes specific application strategies from five dimensions: platform construction, predictive modeling, system development, process enhancement and quality evaluation, so as to promote the transformation of the course scheduling mode of private colleges from experience-driven to data-driven, and further improve the digital and intelligent level of academic administration.