用BERT模型自动构建课程对齐矩阵，准确率达98.66%
BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AI
- 基于BERT的多分类模型判断课程目标与培养目标匹配度
- 准确率、精确率、召回率和F1值均达98.66%以上
- 结合LIME解释技术，适合教育评估与教学改革参考
课程目标（CO）与项目目标（PO）/项目特定目标（PSO）的对齐是确保课程连贯性和评估教育效果的关键任务。课程关联矩阵（CAM）通过数值（0, 1, 2, 3）量化CO与PO/PSO之间的关联程度。本研究对比了BERT Base、DistilBERT、ALBERT和RoBERTa四种BERT家族模型，采用多分类方法评估CO与PO/PSO对的匹配度。首先测试了决策树、随机森林和XGBoost等传统机器学习模型，随后应用迁移学习评估预训练BERT模型的表现。为提升模型可解释性，引入局部可解释模型无关解释（LIME）技术，揭示决策过程。系统在测试集上达到98.66%的准确率、98.67%的精确率、98.66%的召回率和98.66%的F1分数。研究表明，基于BERT的迁移学习在自动化生成CAM方面具有高精度与可解释性，适用于教育成效评估。
原文摘要 · Abstract (English)
Course Outcome (CO) and Program Outcome (PO)/Program-Specific Outcome (PSO) alignment is a crucial task for ensuring curriculum coherence and assessing educational effectiveness. The construction of a Course Articulation Matrix (CAM), which quantifies the relationship between COs and POs/PSOs, typically involves assigning numerical values (0, 1, 2, 3) to represent the degree of alignment. In this study, We experiment with four models from the BERT family: BERT Base, DistilBERT, ALBERT, and RoBERTa, and use multiclass classification to assess the alignment between CO and PO/PSO pairs. We first evaluate traditional machine learning classifiers, such as Decision Tree, Random Forest, and XGBoost, and then apply transfer learning to evaluate the performance of the pretrained BERT models. To enhance model interpretability, we apply Explainable AI technique, specifically Local Interpretable Model-agnostic Explanations (LIME), to provide transparency into the decision-making process. Our system achieves accuracy, precision, recall, and F1-score values of 98.66%, 98.67%, 98.66%, and 98.66%, respectively. This work demonstrates the potential of utilizing transfer learning with BERT-based models for the automated generation of CAMs, offering high performance and interpretability in educational outcome assessment.
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