Grading coding assignments manually is challenging due to complexity and
subjectivity. However, auto-grading with deep learning simplifies the task. It
objectively assesses code quality, detects errors, and assigns marks
accurately, reducing the burden on instructors while ensuring efficient and
fair assessment. This study provides an analysis of auto-grading of the C
programming assignments using machine learning and deep learning approaches
like regression, convolutional neural networks (CNN) and long short-term memory
(LSTM). Using a code-based transformer word embedding model called CodeBERT,
the textual code inputs were transformed into vectors, and the vectors were
then fed into several models. The testing findings demonstrated the efficacy of
the suggested strategy with a root mean squared error (RMSE) of 1.89. The
contrast between statistical methods and deep learning techniques is discussed
in the study.

利用深度学习的自动评分系统通过将程序文本输入转换为向量并利用多个模型进行分析，成功评估了 C 编程作业，取得了 1.89 的均方根误差。

利用 CodeBERT 和随机森林回归器自动评分 C 编程作业

Auto-grading C programming assignments with CodeBERT and Random Forest  Regressor

Manually grading structural changes with the modified Stoke Ankylosing
Spondylitis Spinal Score (mSASSS) on spinal X-ray imaging is costly and
time-consuming due to bone shape complexity and image quality variations. In
this study, we address this challenge by prototyping a 2-step auto-grading
pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the
cervical and lumbar vertebral units (VUs) in X-ray spinal imaging. The
VertXGradeNet utilizes VUs generated by our previously developed VU extraction
pipeline (VertXNet) as input and predicts mSASSS based on those VUs.
VertXGradeNet was evaluated on an in-house dataset of lateral cervical and
lumbar X-ray images for axial spondylarthritis patients. Our results show that
VertXGradeNet can predict the mSASSS score for each VU when the data is limited
in quantity and imbalanced. Overall, it can achieve a balanced accuracy of 0.56
and 0.51 for 4 different mSASSS scores (i.e., a score of 0, 1, 2, 3) on two
test datasets. The accuracy of the presented method shows the potential to
streamline the spinal radiograph readings and therefore reduce the cost of
future clinical trials.

通过 VertXGradeNet 自动预测用于 X 光脊柱成像的颈椎和腰椎椎体单位的修正 Stoke 强直性脊柱炎骨试分数（mSASSS）评分，从而在成本和时间上提高自动评分的效率。