Based on the introduced students’ analysis, we conclude that the proposed particular person exercise task concept fosters self-directed and reflective studying. A cross-modal data augmentation approach is proposed to resolve this drawback. Therefore, assisting college students find appropriate exercises turns into a significant downside. Thus, the precise problem addressed on this paper is the best way to advocate workout routines with high representativeness and informativeness from a big pool of questions. Because classification and similarity comparability are two different problems, we target our downside using a different baseline however related construction overall. We suggest and prepare a suitable neural network structure for the task, and present that conditioning the model’s output for a given input text on an instance exercise of the envisioned exercise sort, results in an increased effectiveness, compared to an example-independent baseline model. At the same time, unreflected copying of duties already solved does not foster the understanding of the topic and results in a false self-evaluation.
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