As student failure rates continue to increase in higher education, predicting
student performance in the following semester has become a significant demand.
Personalized student performance prediction helps educators gain a
comprehensive view of student status and effectively intervene in advance.
However, existing works scarcely consider the explainability of student
performance prediction, which educators are most concerned about. In this
paper, we propose a novel Explainable Student performance prediction method
with Personalized Attention (ESPA) by utilizing relationships in student
profiles and prior knowledge of related courses. The designed Bidirectional
Long Short-Term Memory (BiLSTM) architecture extracts the semantic information
in the paths with specific patterns. As for leveraging similar paths' internal
relations, a local and global-level attention mechanism is proposed to
distinguish the influence of different students or courses for making
predictions. Hence, valid reasoning on paths can be applied to predict the
performance of students. The ESPA consistently outperforms the other
state-of-the-art models for student performance prediction, and the results are
intuitively explainable. This work can help educators better understand the
different impacts of behavior on students' studies.

本文提出了一种新的、可解释的个性化关注机制的学生成绩预测模型，利用双向长短期记忆 (BiLSTM) 模型提取具有特定模式的路径中的语义信息，并提出了局部和全局级别的关注机制来区分不同学生或课程的影响，从而有效地预测学生表现，结果表明该模型在预测学生表现方面优于其他先进模型且可解释性强。

基于个性化注意力的可解释学生成绩预测：阐释学生失败的原因

Explainable Student Performance Prediction With Personalized Attention  for Explaining Why A Student Fails

News recommendation is very important to help users find interested news and
alleviate information overload. Different users usually have different
interests and the same user may have various interests. Thus, different users
may click the same news article with attention on different aspects. In this
paper, we propose a neural news recommendation model with personalized
attention (NPA). The core of our approach is a news representation model and a
user representation model. In the news representation model we use a CNN
network to learn hidden representations of news articles based on their titles.
In the user representation model we learn the representations of users based on
the representations of their clicked news articles. Since different words and
different news articles may have different informativeness for representing
news and users, we propose to apply both word- and news-level attention
mechanism to help our model attend to important words and news articles. In
addition, the same news article and the same word may have different
informativeness for different users. Thus, we propose a personalized attention
network which exploits the embedding of user ID to generate the query vector
for the word- and news-level attentions. Extensive experiments are conducted on
a real-world news recommendation dataset collected from MSN news, and the
results validate the effectiveness of our approach on news recommendation.

本研究提出了一种基于个性化注意力的神经新闻推荐模型，其中使用了卷积神经网络进行新闻表示建模并使用个性化注意力机制对用户建模，同时在一个真实世界的新闻推荐数据集上进行了广泛实验以验证模型的有效性。