Research on student performance prediction has evolved from the early application of statistical techniques to later use of computational techniques. Results in this field are varied, thus, we have to take advantage of previous research results. This study proposes a Multi-layer Adaptive Neuro-Fuzzy Inference System (MANFIS) for student performance prediction in online Higher Education settings. The MANFIS was trained and tested using a dataset integrated by the scores obtained by students in four online Higher Education courses. The MANFIS prediction accuracy was compared against the accuracies of Multilayer neural network, Radial Basis Function Neural Network, and General Regression Neural Network. The accuracy of the MANFIS prediction statistically outperformed at least one neural network (out of three possible) in each dataset. The Results indicate that MANFIS is an alternative model to predict student performance in online Higher Education settings.

中文翻译：

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用于预测在线高等教育学生表现的多层自适应模糊推理系统


学生成绩预测的研究已经从早期的统计技术的应用发展到后来的计算技术的使用。该领域的结果多种多样，因此，我们必须利用以前的研究成果。本研究提出了一种多层自适应神经模糊推理系统（MANFIS），用于在线高等教育环境中预测学生成绩。 MANFIS 使用由学生在四门在线高等教育课程中获得的分数整合而成的数据集进行训练和测试。将 MANFIS 预测精度与多层神经网络、径向基函数神经网络和广义回归神经网络的精度进行了比较。从统计数据来看，MANFIS 预测的准确性优于每个数据集中的至少一个神经网络（三种可能中的神经网络）。结果表明，MANFIS 是预测在线高等教育环境中学生表现的替代模型。