In traditional education, there is not much difference between assessment tasks designed for learners. However, learners’ learning performance may vary due to a number of factors, e.g., learning ability, academic emotion, and learners’ and teachers’ academic expectations. Considering those factors, accurately recommending personalized assessment tasks for each learner is challenging. To overcome the limitations in the current work, this paper proposed an autonomous-agent-based approach to recommend personalized assessment tasks considering multiple factors. Contributions of the proposed approach contain three aspects: (1) Considering objective factors, the proposed approach dynamically adjusts the assessment tasks recommended for students. (2) Considering subjective factors, the proposed approach can dynamically predict learners’ learning performances by applying autonomous agent-based negotiation. (3) The proposed recommendation algorithm can handle the problems of cold start in the research of typical recommendation algorithms. A set of experiments evaluates the effectiveness of the proposed approach in this paper, and the experimental results show that the proposed approach can accurately and adaptively generate personalized assessment tasks considering objective and subjective factors.

In computer vision, fine-grained classification has become an important issue in recognizing objects with slight visual differences. Usually, it is challenging to generate good performance when solving fine-grained classification problems using traditional convolutional neural networks. To improve the accuracy and training time of convolutional neural networks in solving fine-grained classification problems, this paper proposes a tree-structured framework by eliminating the effect of differences between clusters. The contributions of the proposed method include the following three aspects: (1) a self-supervised method that automatically creates a classification tree, eliminating the need for manual labeling; (2) a machine-learning matcher which determines the cluster to which an item belongs, minimizing the impact of inter-cluster variations on classification; and (3) a pruning criterion which filters the tree-structured classifier, retaining only the models with superior classification performance. The experimental evaluation of the proposed tree-structured framework demonstrates its effectiveness in reducing training time and improving the accuracy of fine-grained classification across various datasets in comparison with conventional convolutional neural network models. Specifically, for the CUB 200 2011, FGVC aircraft, and Stanford car datasets, the proposed method achieves a reduction in training time of 32.91%, 35.87%, and 14.48%, and improves the accuracy of fine-grained classification by 1.17%, 2.01%, and 0.59%, respectively.