A plethora of research has been done in the past focusing on predicting student’s performance in order to support their development. Many institutions are focused on improving the performance and the education quality; and this can be achieved by utilizing data mining techniques to analyze and predict students’ performance and to determine possible factors that may affect their final marks. To address this issue, this work starts by thoroughly exploring and analyzing two different datasets at two separate stages of course delivery (20% and 50% respectively) using multiple graphical, statistical, and quantitative techniques. The feature analysis provides insights into the nature of the different features considered and helps in the choice of the machine learning algorithms and their parameters. Furthermore, this work proposes a systematic approach based on Gini index and $p$-value to select a suitable ensemble learner from a combination of six potential machine learning algorithms. Experimental results show that the proposed ensemble models achieve high accuracy and low false positive rate at all stages for both datasets.

过去，已经进行了大量研究，重点是预测学生的表现以支持他们的发展。许多机构致力于提高绩效和教育质量；这可以通过利用数据挖掘技术来分析和预测学生的表现并确定可能影响其最终成绩的可能因素来实现。为了解决这个问题，这项工作首先使用多种图形，统计和定量技术，在课程交付的两个不同阶段（分别为20％和50％）彻底探索和分析两个不同的数据集。特征分析可洞悉所考虑的不同特征的性质，并有助于选择机器学习算法及其参数。此外，$p$值，以从六种潜在的机器学习算法的组合中选择合适的整体学习器。实验结果表明，所提出的集成模型在两个数据集的所有阶段均达到了较高的准确性和较低的误报率。