中圖分類號：G456；TP311.1；TP399 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.245298 中文引用格式： 李啟鵬，曾松偉. 全過程學(xué)業(yè)預(yù)警跟蹤評價系統(tǒng)的研究與實現(xiàn)[J]. 電子技術(shù)應(yīng)用，2025，51(2)：86-92. 英文引用格式： Li Qipeng，Zeng Songwei. Research and implementation of a full-process academic early warning and tracking evaluation system[J]. Application of Electronic Technique，2025，51(2)：86-92.
Research and implementation of a full-process academic early warning and tracking evaluation system
Li Qipeng1，Zeng Songwei2
1.College of Mathematics and Computer Science， Zhejiang A&F University； 2.College of Optical， Mechanical and Electrical Engineering
Abstract： Traditional academic warning systems usually focus more on terminal indicators such as students’ grades and attendance, and trigger warnings when these indicators meet specific conditions. The academic warning system studied in this paper adopts a whole-process monitoring and warning method, which not only monitors conventional indicators such as students’ final grades, annual assessments, and attendance, but also comprehensively tracks, analyzes and evaluates students’ classroom performance, homework after class, team assessments, ideological and political assessments, and economic pressure, etc. Meanwhile, based on the implementation rules of the undergraduate tutor system, all tutors are encouraged to actively participate in academic warning activities. As important mentors in the learning process of students, they track and evaluate students’ academic performance, and provide timely, effective, and precise academic guidance, realizing the whole-process and closed-loop monitoring from issuing warnings to guiding effects. This paper uses Particle Swarm Optimization (PSO) to optimize Support Vector Machine (SVM), and combines Web and mini-program technology to implement a whole-process academic warning tracking and evaluation system, which effectively improves the accuracy and timeliness of warnings, filling in the gaps of traditional academic warning systems. This system is of great significance for improving the quality of students’ academic performance, and also provides a reference for academic warning support systems in other universities.
Key words : academic early warning；dual mentorship；whole process；mutual assistance and mutual supervision；multidimensional data-drive
