Predicting students’ performance has emerged as an attractive task among researchers. They use supervised and unsupervised educational data mining (EDM) techniques to build an understandable and effective model. This helps decision makers enhance the performance of the students. The challenge of finding an optimal model leads to appearance of many techniques from both EDM techniques. Hence, we propose a unified framework to build a novel supervised cluster-based (CB) classifier model. The unified framework uses clustering technique to group historical records of students into a set of homogeneous clusters. Then, classifier model for each cluster is built and the final unified classifiers along with the centroids at each cluster are used as CB classifier model. The experimental results show that the CB model gains a high accuracy performance reached 96.25%. In addition, we use feature selection techniques for selecting the relevant features from a space of features. The model obtains a high accuracy performance using relevant features reached to 96.96% where the percentage of relevant features on average is 57.4% of overall features.

在研究人员中，预测学生的表现已成为一项有吸引力的任务。他们使用有监督和无监督的教育数据挖掘（EDM）技术来建立一个可理解和有效的模型。这有助于决策者提高学生的表现。寻找最佳模型的挑战导致了两种EDM技术中许多技术的出现。因此，我们提出了一个统一的框架来构建新颖的基于监督的基于聚类的（CB）分类器模型。统一的框架使用聚类技术将学生的历史记录分组为一组同质的聚类。然后，建立每个群集的分类器模型，并将最终统一的分类器以及每个群集的质心用作CB分类器模型。实验结果表明，CB模型获得的高精度性能达到96.25％。此外，我们使用特征选择技术从特征空间中选择相关特征。该模型使用高达96.96％的相关特征获得了高精度性能，其中相关特征的百分比平均为总体特征的57.4％。