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.
翻译:机器学习是一种在教育等不同领域建模的强大方法。 它准确预测学生成功的能力使它成为高等教育决策任务的理想工具。 机器学习模型的准确性取决于是否选择适当的超参数。 但是,由于它需要时间和专门知识来调整超参数以适应机器学习模式,这不是一项容易的任务。 在本文中,我们研究了自动超参数调技术在学生成功领域的效力。 因此,我们开发了两种自动超参数优化方法,即电网搜索和随机搜索,以评估和改进先前研究的绩效。 实验结果显示,对机器学习算法进行随机搜索和网格搜索可以提高准确性。 我们从经验上展示了在现实世界教育数据(MIDFIELD)上自动化方法的优势,用于调整传统机器学习分类器的HP的优势。 这项工作强调在教育领域应用机器学习来帮助学院、 主任 或非专家用户提高学生成功性的决定的同时,自动化超参数优化的实效。