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Collaborative testing has been demonstrated the ability to improve students' performance, enhance students' learning, and aid in knowledge retention in many different courses. However, this examination mode lacks the process of teacher feedback. Herein, a short teacher feedback from was added immediately after the collaborative testing to improve the students' performance. A parasitology class of 121 undergraduates was randomized into two groups: "group A" and "group B." Collaborative testing was carried out at the end of theoretical teaching. During the test, students would first answer questions as individuals for 20 minutes. Then, students from "group A" answered the same questions in groups (5 students in each group) for 20 minutes, while the group-testing duration was only 15 minutes in "group B." Immediately after the group testing, teachers conducted a 5-minute feedback about the morphology identification according to the analysis of the answers by "group B." Four weeks later, a final test was conducted in an individual test. The total scores and scores for each examination content were analyzed. The results showed that there was no significant difference in the final exam scores between both groups (t = -1.278, P = 0.204). However, the morphological and diagnostic test results of the final examination in group B were significantly higher than those of the midterm examination, while there was no significant change in "group A" (t = 4.333, P = 0.051). The results confirmed that the teacher feedback after the collaborative testing can effectively make up for the students' knowledge gaps.

The airborne LiDAR point cloud has its own characteristics, however, the classification method always fails to capture these characteristics. In this paper, a classification method named GOGCN was designed that adopts a U-Net network structure and uses a directionally constrained nearest neighbourhood search during down-sampling to generate the directionally aware feature. The point cloud geometric structure is obtained through geometry-aware information extraction, and then a graph attention convolution is utilised to learn the most representative features. A comparative experiment on GML(B) dataset and one engineering dataset demonstrated that GOGCN network have well performance and can be widely used in classification. [ABSTRACT FROM AUTHOR]

This work proposes an effective and accurate method for solving the partial geometry modification problem of original scattering. The current and scattering characteristics after structure modification can be efficiently calculated by using the information of the initial impedance matrix. These modifications include adding geometry or moving and rotating different parts of geometry. Although the changes in each impedance matrix are local for partial modified geometries, the associated computational cost is high because the whole analysis is iterative. By combining the binomial series method and the reduced basis method, the proposed method can effectively evaluate the scattering field without solving the inverse impedance matrix of the modified geometry. The proposed algorithm has a computational cost of O(n2), which is much smaller than O(n3) of the traditional method of moments (MoM), where n is the number of unknowns. Through several examples, the traditional MoM and the proposed method are compared, and the efficiency and accuracy of the proposed method are proved. [ABSTRACT FROM AUTHOR]