Graph convolutional neural networks-based assessment of students' collaboration ability

As 21st-century skills have become increasingly important, collaboration ability is now considered essential in many areas of life. Different theoretical frameworks and assessment tools have emerged to measure this skill. However, more applied studies on its implementation and assessment in current educational settings are required. This research accordingly uses Graph Convolutional Neural Networks (GCNs) to assess students' collaboration ability from students' assignments. The Pearson correlation coefficient is used to measure the similarity of the level of students' collaboration ability, and similar students are linked together to establish an adjacency matrix. By sorting through relevant literature and selecting the feature words that represent the strength of collaboration ability, calculating the similarity between the preprocessed student data and each selected feature word, after which the highest value of the similarity as the feature value of the student for this feature and establish the student feature matrix. Finally, the GCNs are jointly trained by the adjacency matrix and the feature matrix. The results show that this method can effectively assess students' collaboration ability. Moreover, compared with other text classification methods, the GCNs selected in this paper has higher accuracy.