Onyema Edeh Michael, Almuzaini Khalid K, Onu Fergus Uchenna, Verma Devvret, Gregory Ugboaja Samuel, Puttaramaiah Monika, Afriyie Rockson Kwasi
Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria.
National Center for Cybersecurity Technologies (C4C), King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia.
Comput Intell Neurosci. 2022 Jun 17;2022:5624475. doi: 10.1155/2022/5624475. eCollection 2022.
The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events. Many gaps in the traditional forecasting techniques have greatly been bridged by the use of artificial intelligence-based machine learning algorithms thereby aiding timely decision-making by education stakeholders. ML algorithms are deployed by educational institutions to predict students' learning behaviours and academic achievements, thereby giving them the opportunity to detect at-risk students early and then develop strategies to help them overcome their weaknesses. However, despite the benefits associated with the ML approach, there exist some limitations that could affect its correctness or deployment in forecasting academic events, e.g., proneness to errors, data acquisition, and time-consuming issues. Nonetheless, we suggest that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.
该研究探讨了机器学习（ML）应用于学术预测的前景与挑战。通过机器学习算法预测学术活动，为准确预测学术事件提供了一种更有效的方法，这些学术事件包括学生的学业表现和学习风格。使用诸如K近邻（KNN）、随机森林、装袋法、人工神经网络（ANN）和贝叶斯神经网络（BNN）等机器学习算法具有诸多潜力，目前已在教育领域用于预测未来事件。基于人工智能的机器学习算法极大地弥补了传统预测技术中的许多不足，从而有助于教育利益相关者及时做出决策。教育机构部署ML算法来预测学生的学习行为和学业成绩，从而使他们有机会尽早发现有风险的学生，进而制定策略帮助他们克服弱点。然而，尽管ML方法有诸多益处，但仍存在一些可能影响其在学术事件预测中的正确性或应用的局限性，例如容易出错、数据获取和耗时问题。尽管如此，我们认为机器学习仍是有前景的预测技术之一，有能力加强有效的学术预测，这将有助于教育行业进行规划并做出更好的决策，以提高教育质量。
