Adnan Muhammad, Alarood Alaa Abdul Salam, Uddin M Irfan, Ur Rehman Izaz
Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan.
College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
PeerJ Comput Sci. 2022 Feb 21;8:e803. doi: 10.7717/peerj-cs.803. eCollection 2022.
Corona Virus Disease 2019 (COVID-19) pandemic has increased the importance of Virtual Learning Environments (VLEs) instigating students to study from their homes. Every day a tremendous amount of data is generated when students interact with VLEs to perform different activities and access learning material. To make the generated data useful, it must be processed and managed by the proper machine learning (ML) algorithm. ML algorithms' applications are many folds with Education Data Mining (EDM) and Learning Analytics (LA) as their major fields. ML algorithms are commonly used to process raw data to discover hidden patterns and construct a model to make future predictions, such as predicting students' performance, dropouts, engagement, . However, in VLE, it is important to select the right and most applicable ML algorithm to give the best performance results. In this study, we aim to improve those ML and DL algorithms' performance that give an inferior performance in terms of performance, accuracy, precision, recall, and F1 score. Several ML algorithms were applied on Open University Learning Analytics (OULA) dataset to reveal which one offers the best results in terms of performance, accuracy, precision, recall, and F1 score. Two popular ML algorithms called Decision Tree (DT) and Feed-Forward Neural Network (FFNN) provided unsatisfactory results. They were selected and experimented with various techniques such as grid search cross-validation, adaptive boosting, extreme gradient boosting, early stopping, feature engineering, and dropping inactive neurons to improve their performance scores. Moreover, we also determined the feature weights/importance in predicting the students' study performance, leading to the design and development of the adaptive learning system. The ML techniques and the methods used in this research study can be used by instructors/administrators to optimize learning content and provide informed guidance to students, thus improving their learning experience and making it exciting and adaptive.
2019冠状病毒病（COVID-19）大流行增加了虚拟学习环境（VLE）的重要性，促使学生在家学习。学生与虚拟学习环境进行交互以执行不同活动并获取学习材料时，每天都会产生大量数据。为了使生成的数据有用，必须通过适当的机器学习（ML）算法对其进行处理和管理。机器学习算法在教育数据挖掘（EDM）和学习分析（LA）等多个领域有广泛应用。机器学习算法通常用于处理原始数据，以发现隐藏模式并构建模型进行未来预测，例如预测学生的成绩、辍学情况、参与度等。然而，在虚拟学习环境中，选择正确且最适用的机器学习算法以获得最佳性能结果非常重要。在本研究中，我们旨在提高那些在性能、准确性、精确率、召回率和F1分数方面表现较差的机器学习和深度学习算法的性能。我们将几种机器学习算法应用于开放大学学习分析（OULA）数据集，以揭示哪种算法在性能、准确性、精确率、召回率和F1分数方面能提供最佳结果。两种流行的机器学习算法，即决策树（DT）和前馈神经网络（FFNN），给出了不尽人意的结果。我们选择了它们，并采用了各种技术进行实验，如网格搜索交叉验证、自适应增强、极端梯度增强、提前停止、特征工程以及删除不活跃神经元，以提高它们的性能分数。此外，我们还确定了预测学生学习成绩时的特征权重/重要性，从而设计并开发了自适应学习系统。本研究中使用的机器学习技术和方法可供教师/管理人员用于优化学习内容，并为学生提供明智的指导，从而改善他们的学习体验，使其变得令人兴奋且具有适应性。
