During the past few decades, cognitive diagnostics modeling has attracted
increasing attention in computational education communities, which is capable
of quantifying the learning status and knowledge mastery levels of students.
Indeed, the recent advances in neural networks have greatly enhanced the
performance of traditional cognitive diagnosis models through learning the deep
representations of students and exercises. Nevertheless, existing approaches
often suffer from the issue of overconfidence in predicting students' mastery
levels, which is primarily caused by the unavoidable noise and sparsity in
realistic student-exercise interaction data, severely hindering the educational
application of diagnostic feedback. To address this, in this paper, we propose
a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the
confidence of the diagnosis feedback and is flexible for different cognitive
diagnostic functions. Specifically, we first propose a Bayesian method to
explicitly estimate the state uncertainty of different knowledge concepts for
students, which enables the confidence quantification of diagnostic feedback.
In particular, to account for potential differences, we suggest modeling
individual prior distributions for the latent variables of different ability
concepts using a pre-trained model. Additionally, we introduce a logical
hypothesis for ranking confidence levels. Along this line, we design a novel
calibration loss to optimize the confidence parameters by modeling the process
of student performance prediction. Finally, extensive experiments on four
real-world datasets clearly demonstrate the effectiveness of our ReliCD
framework.

本文提出了一种可靠的认知诊断（ReliCD）框架，通过量化诊断反馈的置信度，解决了现有方法在预测学生掌握水平时存在的过度自信问题。通过使用贝叶斯方法估计学生不同知识概念的状态不确定性，引入逻辑假设和校准损失函数优化置信参数，并在四个真实的数据集上进行了广泛实验证明了 ReliCD 框架的有效性。

ReliCD: 一种带有置信度意识的可靠认知诊断框架

ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence  Awareness

We consider the problem of learning to repair programs from diagnostic
feedback (e.g., compiler error messages). Program repair is challenging for two
reasons: First, it requires reasoning and tracking symbols across source code
and diagnostic feedback. Second, labeled datasets available for program repair
are relatively small. In this work, we propose novel solutions to these two
challenges. First, we introduce a program-feedback graph, which connects
symbols relevant to program repair in source code and diagnostic feedback, and
then apply a graph neural network on top to model the reasoning process.
Second, we present a self-supervised learning paradigm for program repair that
leverages unlabeled programs available online to create a large amount of extra
program repair examples, which we use to pre-train our models. We evaluate our
proposed approach on two applications: correcting introductory programming
assignments (DeepFix dataset) and correcting the outputs of program synthesis
(SPoC dataset). Our final system, DrRepair, significantly outperforms prior
work, achieving 68.2% full repair rate on DeepFix (+22.9% over the prior best),
and 48.4% synthesis success rate on SPoC (+3.7% over the prior best).

本文提出了一种基于程序 -- 反馈图的图神经网络及一种用非监督学习方法构建大量程序修复样例的自监督学习范式。最终应用的 DrRepair 算法在两个应用领域上都取得了比之前的工作更好的修复效果。