Predicting student performance for educational organizations such as universities, community colleges, schools, and training centers will enhance the overall results of these organizations. Big data can be extracted from the internal systems of these organizations, such as exam records, statistics about virtual courses, and e-learning systems. Finding meaningful knowledge from extracted data is a challenging task. In this paper, we proposed a modified version of Harris Hawks Optimization (HHO) algorithm by controlling the population diversity to overcome the early convergence problem and prevent trapping in a local optimum. The proposed approach is employed as a feature selection algorithm to discover the most valuable features for student performance prediction problem. A dynamic controller that controls the population diversity by observing the performance of HHO using the k-nearest neighbors (kNN) algorithm as a clustering approach. Once all solutions belong to one cluster, an injection process is employed to redistribute the solutions over the search space. A set of machine learning classifiers such as kNN, Layered recurrent neural network (LRNN), Naïve Bayes, and Artificial Neural Network are used to evaluate the overall prediction system. A real dataset obtained from UCI machine learning repository is adopted in this paper. The obtained results show the importance of predicting students’ performance at an earlier stage to avoid students’ failure and improve the overall performance of the educational organization. Moreover, the reported results show that the combination between the enhanced HHO and LRNN can outperform other classifiers with accuracy equal to \(92\%\), since LRNN is a deep learning algorithm that is able to learn from previous and current input values.

预测大学，社区学院，学校和培训中心等教育组织的学生表现将提高这些组织的整体效果。可以从这些组织的内部系统中提取大数据，例如考试记录，有关虚拟课程的统计信息和电子学习系统。从提取的数据中找到有意义的知识是一项艰巨的任务。在本文中，我们通过控制总体多样性提出了一种改进版的Harris Hawks优化（HHO）算法，以克服早期收敛问题并防止陷入局部最优。所提出的方法被用作特征选择算法，以发现针对学生成绩预测问题的最有价值的特征。一种动态控制器，通过使用k最近邻居（kNN）算法作为聚类方法，通过观察HHO的性能来控制种群多样性。一旦所有解决方案都属于一个群集，就可以使用注入过程在搜索空间上重新分配解决方案。一组机器学习分类器（例如kNN，分层递归神经网络（LRNN），朴素贝叶斯和人工神经网络）用于评估整个预测系统。本文采用了从UCI机器学习存储库中获得的真实数据集。获得的结果表明，在早期阶段预测学生的表现对于避免学生的失败并提高教育机构的整体绩效的重要性。此外，\（92 \％\），因为LRNN是一种深度学习算法，能够从先前和当前输入值中进行学习。