Online education becomes increasingly important since traditional learning is shocked heavily by COVID-19. To better develop personalized learning plans for students, it is necessary to build a model that can automatically evaluate students’ performance in online education. For this purpose, in this study we propose an ensemble learning method named light gradient boosting channel attention network (LGBCAN), which is based on label distribution estimation. First, the light gradient boosting machine (LightGBM) is used to predict the performance in online learning tasks. Then The Channel Attention Network (CAN) model further improves the function of LightGBM by focusing on better results in the K-fold CrossEntropy of LightGBM. The results are converted into predicted classes through post-processing methods named approximation of label distribution to complete the classification task. The experiments are employed on two datasets, data science bowl (DSB) and answer correctness prediction (ACP). The experimental results in both datasets suggest that our model has better robustness and generalization ability.

中文翻译：

---

基于标签分布的在线教育预测集成学习方法的近似

由于传统学习受到 COVID-19 的严重冲击，在线教育变得越来越重要。为了更好地为学生制定个性化的学习计划，有必要建立一个能够自动评估学生在线教育表现的模型。为此，在本研究中，我们提出了一种名为光梯度提升通道注意网络（LGBCAN）的集成学习方法，该方法基于标签分布估计。首先，使用光梯度提升机（LightGBM）来预测在线学习任务中的性能。然后通道注意力网络（CAN）模型通过关注 LightGBM 的 K-fold CrossEntropy 中更好的结果，进一步改进了 LightGBM 的功能。通过标签分布近似的后处理方法将结果转化为预测类，完成分类任务。这些实验用于两个数据集，数据科学碗 (DSB) 和答案正确性预测 (ACP)。两个数据集的实验结果表明我们的模型具有更好的鲁棒性和泛化能力。