Predicting students’ learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to develop accurate learning models at low cost. Fuzzy logic constitutes an appropriate approach for building models of high performance and transparency. In addition, active learning reduces both the time and cost of labeling effort, by exploiting a small set of labeled data along with a large set of unlabeled data in the most efficient way. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameter setting is a demanding task, considering the high-dimensional input space and the complexity of machine learning algorithms. As such, exploring the potential of automated machine learning (autoML) strategies from the perspective of machine learning adeptness is important. In this context, the present study introduces a fuzzy-based active learning method for predicting students’ academic performance which combines, in a modular way, autoML practices. A lot of experiments was carried out, revealing the efficiency of the proposed method for the accurate prediction of students at risk of failure. These insights may have the potential to support the learning experience and be useful the wider science of learning.

预测学生的学习成果是教育数据挖掘和学习分析领域感兴趣的主要主题之一。为此，已经将大量的机器学习方法成功地用于解决各种预测问题。但是，对于教育者和数据科学家而言，以低成本开发准确的学习模型至关重要。模糊逻辑是构建高性能和透明性模型的适当方法。此外，主动学习通过以最有效的方式利用少量标记数据和大量未标记数据来减少标记工作的时间和成本。此外，为给定的问题表述选择适当的方法并配置最佳参数设置是一项艰巨的任务，考虑到高维输入空间和机器学习算法的复杂性。因此，从机器学习熟练度的角度探索自动机器学习（autoML）策略的潜力很重要。在这种情况下，本研究引入了一种基于模糊的主动学习方法来预测学生的学习成绩，该方法以模块化的方式结合了autoML实践。进行了大量的实验，揭示了所提出的方法对有失败风险的学生进行准确预测的效率。这些见解可能具有支持学习经验的潜力，并且对更广泛的学习科学很有用。从机器学习熟练度的角度探索自动化机器学习（autoML）策略的潜力很重要。在这种情况下，本研究引入了一种基于模糊的主动学习方法来预测学生的学习成绩，该方法以模块化的方式结合了autoML实践。进行了大量的实验，揭示了所提出的方法对有失败风险的学生进行准确预测的效率。这些见解可能具有支持学习经验的潜力，并且对更广泛的学习科学很有用。从机器学习熟练度的角度探索自动化机器学习（autoML）策略的潜力很重要。在这种情况下，本研究引入了一种基于模糊的主动学习方法来预测学生的学习成绩，该方法以模块化的方式结合了autoML实践。进行了大量的实验，揭示了所提出的方法对有失败风险的学生进行准确预测的效率。这些见解可能具有支持学习经验的潜力，并且对更广泛的学习科学很有用。揭示了所提出方法对有失败风险的学生进行准确预测的效率。这些见解可能具有支持学习经验的潜力，并且对更广泛的学习科学很有用。揭示了所提出方法对有失败风险的学生进行准确预测的效率。这些见解可能具有支持学习经验的潜力，并且对更广泛的学习科学很有用。