In this paper, we present a transformer architecture for predicting student performance on standardized tests. Specifically, we leverage students historical data, including their past test scores, study habits, and other relevant information, to create a personalized model for each student. We then use these models to predict their future performance on a given test. Applying this model to the RIIID dataset, we demonstrate that using multiple granularities for temporal features as the decoder input significantly improve model performance. Our results also show the effectiveness of our approach, with substantial improvements over the LightGBM method. Our work contributes to the growing field of AI in education, providing a scalable and accurate tool for predicting student outcomes.

翻译：本文提出了一种用于预测学生在标准化测试中表现的Transformer架构。具体而言，我们利用学生的历史数据（包括过往考试成绩、学习习惯及其他相关信息），为每位学生构建个性化模型。进而使用这些模型预测其未来在给定测试中的表现。将该模型应用于RIIID数据集后，我们证明采用多粒度时间特征作为解码器输入能够显著提升模型性能。实验结果同样验证了本方法的有效性，相较于LightGBM方法实现了实质性改进。本研究为教育人工智能领域的发展做出了贡献，提供了一种可扩展且精准的学生成绩预测工具。