This study explores the efficacy of a multi-perspective hybrid neural network
(HNN) for scoring student responses in science education with an analytic
rubric. We compared the accuracy of the HNN model with four ML approaches
(BERT, AACR, Naive Bayes, and Logistic Regression). The results have shown that
HHN achieved 8%, 3%, 1%, and 0.12% higher accuracy than Naive Bayes, Logistic
Regression, AACR, and BERT, respectively, for five scoring aspects (p<0.001).
The overall HNN's perceived accuracy (M = 96.23%, SD = 1.45%) is comparable to
the (training and inference) expensive BERT model's accuracy (M = 96.12%, SD =
1.52%). We also have observed that HNN is x2 more efficient in training and
inferencing than BERT and has comparable efficiency to the lightweight but less
accurate Naive Bayes model. Our study confirmed the accuracy and efficiency of
using HNN to score students' science writing automatically.

使用多角度混合神经网络（HNN）对学生科学教育中的分析性评分标准进行评估，结果显示 HNN 在比较四种机器学习方法（BERT，AACR，朴素贝叶斯和逻辑回归）后，对于五个评分方面的准确度分别比朴素贝叶斯，逻辑回归，AACR 和 BERT 高 8％，3％，1％和 0.12％（p <0.001） 。HNN 的整体准确度（M = 96.23％，SD = 1.45％）与（训练和推理）复杂的 BERT 模型的准确度（M = 96.12％，SD = 1.52％）相当。我们还观察到 HNN 在训练和推理方面比 BERT 高 2 倍的效率，并且在准确度较低的朴素贝叶斯模型上具有可比较的效率。本研究证实了使用 HNN 自动评分学生科学写作的准确性和效率。