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.

翻译：课程成果（CO）与项目成果（PO）/项目特定成果（PSO）的对齐是确保课程体系连贯性和评估教育有效性的关键任务。课程衔接矩阵（CAM）的构建旨在量化CO与PO/PSO之间的关系，通常通过分配数值（0、1、2、3）来表示对齐程度。本研究实验了BERT家族的四个模型：BERT Base、DistilBERT、ALBERT和RoBERTa，并采用多类别分类方法评估CO与PO/PSO配对之间的对齐关系。我们首先评估了传统机器学习分类器（如决策树、随机森林和XGBoost），随后应用迁移学习评估预训练BERT模型的性能。为增强模型可解释性，我们应用可解释人工智能技术——特别是局部可解释模型无关解释（LIME），以提供决策过程的透明度。本系统实现的准确率、精确率、召回率和F1分数分别为98.66%、98.67%、98.66%和98.66%。这项工作展示了利用基于BERT模型的迁移学习自动生成CAM的潜力，为教育成果评估提供了高性能与可解释性并重的解决方案。