[论文解读] Reliable Deep Grade Prediction with Uncertainty Estimation

Qian Hu, Huzefa Rangwala|arXiv (Cornell University)|Feb 26, 2019

Online Learning and Analytics参考文献 27被引用 4

一句话总结

本文提出针对特定课程的贝叶斯深度学习模型——多层感知机（MLP）和长短期记忆（LSTM）网络——用于实现可靠的下一学期成绩预测，并提供不确定性估计。通过建模学生的课程修读序列模式并提供校准后的不确定性评分，该方法提高了预测准确性，并支持可解释的、可靠的早期预警系统，以识别处于风险中的学生。

ABSTRACT

Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.

研究动机与目标

• 解决浅层线性模型在捕捉学生学习过程中复杂非线性交互关系方面的局限性。
• 通过引入不确定性估计，提升成绩预测的可靠性，这对于教育环境中的指导与决策至关重要。
• 通过识别对目标课程未来表现影响最大的先修课程，实现可解释的预测。
• 通过结合准确预测与不确定性量化，增强早期预警系统，以支持及时、个性化的干预措施。
• 在真实世界大学数据上评估所提出的模型，证明其性能优于当前最先进的基线方法。

提出的方法

• 采用课程特定的框架，每个目标课程仅使用相关先修课程作为输入特征进行预测。
• 实现贝叶斯多层感知机（MLP），用于建模先修课程成绩的静态特征表示。
• 使用贝叶斯长短期记忆（LSTM）网络，捕捉学生在多个学期中知识演化的时序动态。
• 应用贝叶斯深度学习技术估计预测不确定性，为每个成绩预测提供置信区间。
• 采用基于梯度的显著性方法，计算并排序先修课程对目标课程预测成绩的影响程度。
• 使用随机梯度下降结合变分推理训练模型，以近似网络权重的后验分布。

实验结果

研究问题

• RQ1 贝叶斯深度学习模型是否在预测学生未来课程成绩方面优于传统浅层模型？
• RQ2 不确定性估计在识别不可靠预测并提升早期预警系统可靠性方面效果如何？
• RQ3 对先修课程影响未来表现的解释与可视化程度在多大程度上具有实际意义？
• RQ4 与静态MLP模型相比，通过LSTM建模时序动态是否能提升预测性能？
• RQ5 所提出的框架是否能比现有最先进的方法更有效地识别处于风险中的学生？

主要发现

• 所提出的贝叶斯LSTM模型在预测性能上优于当前最先进的基线模型，成绩预测的准确性显著提升。
• 模型提供了校准后的不确定性估计，能够识别出最需要导师干预的预测结果。
• 影响度分析显示，先修课程在每门目标课程中始终位列最具影响力的前五名。
• 该系统成功识别出预测成绩较低的学生，特别是那些在关键基础课程中表现不佳的学生。
• 可解释性组件有效突出了具体先修课程（如ECE-301对CS-367的影响）对后续表现预测的最高影响力。
• 贝叶斯方法在检测不可靠预测方面展现出更强能力，支持构建更可信的早期预警系统。