基于Transformer的课程与成绩联合预测模型
Jointly Predicting Courses and Grades Using a Transformer-Based Model
- St. Edward’s University（圣爱德华大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出TRACE模型，通过按学期编码课程并采用结合课程集合与成绩预测的损失函数，联合预测学生下一学期的选课与成绩，大幅降低成绩预测误差，优于LSTM及图神经网络模型，可用于高校早期预警系统。
AI中文摘要：
现有学习分析领域的预测模型通常将学生学业历史视为简单序列，忽略了同一学期内所选课程的并发特性，这种简化会导致成绩预测不准确，尤其针对课程负担重或课程难度大的学生。本文提出一种用于学术课程-成绩估计的Transformer模型（TRACE），该模型通过联合预测学生下一学期将选的课程集合及其对应成绩来解决上述局限。我们的方法按学期对课程进行编码，以捕捉课程并发的影响，并采用一种结合课程集合预测与成绩预测的新型损失函数。我们证明，除了预测成绩外，同时预测所选课程能显著提升预测质量。该联合预测模型在十年的机构数据上训练后，与仅预测成绩的相同架构相比，平均绝对误差降低了近50%；该模型还优于传统基于LSTM的序列模型及基于图神经网络的方法，且能自然地纳入学生属性数据。本研究证明了现代神经架构在构建可解释模型方面的实用性，这类模型可通过重新训练和校准适配新机构，同时也证明了训练期间预测所选课程等关键技术的重要性。我们还探讨了该模型如何被纳入高等教育机构的早期预警系统。
英文摘要：
Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester. This simplification can lead to inaccurate performance predictions, particularly for students with heavy or challenging course loads. This paper introduces a TRansformer for Academic Course-grade Estimation (TRACE) that addresses this limitation by jointly predicting both the set of courses a student will take and their corresponding grades for an upcoming semester. Our approach encodes courses on a per-semester basis to capture the effects of course concurrency and utilizes a novel loss function combining course-set prediction with grade prediction. We demonstrate that predicting courses taken in addition to the grades in those courses leads to significant improvements in prediction quality. Trained on ten years of institutional data, our joint prediction model reduces mean absolute error by nearly 50% compared to an identical architecture that predicts grades alone. The model also outperforms traditional LSTM-based sequential models, as well as graph neural network-based approaches, and offers natural ways to incorporate student attribute data. This work demonstrates the utility of modern neural architectures for creating interpretable models that can be adapted to new institutions via retraining and recalibration, as well as the importance of key techniques, such as predicting courses taken during training. We discuss how this model could be incorporated into early detection systems at institutions of higher education.
