Machine learning is a powerful method for modeling in different fields such as education. Its capability to accurately predict students' success makes it an ideal tool for decision-making tasks related to higher education. The accuracy of machine learning models depends on selecting the proper hyper-parameters. However, it is not an easy task because it requires time and expertise to tune the hyper-parameters to fit the machine learning model. In this paper, we examine the effectiveness of automated hyper-parameter tuning techniques to the realm of students' success. Therefore, we develop two automated Hyper-Parameter Optimization methods, namely grid search and random search, to assess and improve a previous study's performance. The experiment results show that applying random search and grid search on machine learning algorithms improves accuracy. We empirically show automated methods' superiority on real-world educational data (MIDFIELD) for tuning HPs of conventional machine learning classifiers. This work emphasizes the effectiveness of automated hyper-parameter optimization while applying machine learning in the education field to aid faculties, directors', or non-expert users' decisions to improve students' success.

中文翻译：

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自动超参数调整的搜索算法

机器学习是在教育等不同领域进行建模的强大方法。它具有准确预测学生成功的能力，使其成为与高等教育有关的决策任务的理想工具。机器学习模型的准确性取决于选择适当的超参数。但是，这并不是一件容易的事，因为它需要时间和专业知识来调整超参数以适合机器学习模型。在本文中，我们研究了自动超参数调整技术对学生成功领域的有效性。因此，我们开发了两种自动化的超参数优化方法，即网格搜索和随机搜索，以评估和改进先前研究的性能。实验结果表明，将随机搜索和网格搜索应用于机器学习算法可以提高准确性。我们根据经验显示自动化方法在调整传统机器学习分类器的HP的现实世界教育数据（MIDFIELD）方面的优势。这项工作强调了自动超参数优化的有效性，同时在教育领域应用机器学习来帮助教职员工，董事或非专业用户的决策，以提高学生的成功率。