发表机构
University of Birjand（比尔詹德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一个混合AI框架，结合集成成绩预测与规则专家系统，在比尔詹德大学数据集上实现RMSE 2.35，提供实时学业建议。
AI 中文摘要
学生人数的快速增长对传统的学术咨询过程构成了严峻挑战。本研究设计并实现了一个多用途智能系统，以支持学生的学业进展，该系统基于一个由两部分组成的混合框架：（1）一个先进的成绩预测模型和（2）一个基于规则的推荐引擎。使用包含来自比尔詹德大学的416,558条教育记录的数据集，首先利用高斯混合模型（GMM）将学生划分为同质聚类。随后，针对每个聚类专门训练了一个结合随机森林、梯度提升和MLP的堆叠集成模型。评估结果表明，堆叠模型在所有聚类中均优于基础模型，最终聚合均方根误差（RMSE）达到2.35。第二个组件是一个专家系统，通过将教育法规与第一个组件预测的成绩相结合，提供智能推荐。该系统已作为实用工具部署在比尔詹德大学门户网站上，为学生提供实时反馈，如学期平均绩点预测、学业预警风险警告以及用于提高平均绩点的课程建议。
英文摘要
The rapidly increasing student population has posed serious challenges to the traditional academic advising process. This study designs and implements a multi-purpose intelligent system to support students' academic progress, based on a two-part hybrid framework: (1) an advanced model for grade prediction and (2) a rule-based recommendation engine. Using a dataset containing 416,558 educational records from the University of Birjand, students were first divided into homogeneous clusters using the Gaussian Mixture Model (GMM). Subsequently, a Stacking Ensemble model combining Random Forest, Gradient Boosting, and MLP was trained specifically for each cluster. Evaluation results demonstrated that the Stacking model outperformed base models across all clusters, achieving a final aggregated RMSE of 2.35. The second component is an expert system that provides intelligent recommendations by synergizing educational regulations with the grades predicted by the first component. This system has been implemented as a practical tool on the University of Birjand portal, offering students real-time feedback such as semester GPA prediction, probation risk warnings, and course suggestions for GPA improvement.
