Emotions can influence cognitive development and are key elements to the teachinglearning process. Positive emotions (e.g., engagement) can improve the ability to solve problems, store information, and make decisions. On the other hand, negative emotions (e.g., boredom) reduce the capacity to process information at a deeper level, preventing learning to become effective. Therefore, students’ emotions must be regulated to hinder negative and to promote positive emotions during learning. To support the choice of the best intervention to regulate individual emotions, this article proposes an algorithm based on simulated data considering different individual performances in solving Algebra exercises. The results suggest that the proposed model has high success rates (over 90%) in the choice of interventions and may be applied in real scenarios.

中文翻译：

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智能学习环境中情绪调节模型的提出

情绪可以影响认知发展，是教学过程的关键要素。积极的情绪（例如，参与）可以提高解决问题、存储信息和做出决定的能力。另一方面，负面情绪（例如，无聊）会降低更深层次处理信息的能力，从而阻止学习变得有效。因此，在学习过程中，必须调节学生的情绪，抑制消极情绪，促进积极情绪。为了支持选择调节个人情绪的最佳干预措施，本文提出了一种基于模拟数据的算法，该算法考虑了解决代数练习中不同的个人表现。结果表明，所提出的模型在干预措施的选择上具有较高的成功率（超过 90%），可以应用于实际场景。