Elbourhamy Doaa Mohamed, Najmi Ali Hassan, Elfeky Abdellah Ibrahim Mohammed
Kafrelsheikh University, Kafrelsheikh, Egypt.
King Abdulaziz University, Jeddah, Saudi Arabia.
PeerJ Comput Sci. 2023 May 19;9:e1348. doi: 10.7717/peerj-cs.1348. eCollection 2023.
Modern approaches in education technology, which make use of advanced resources such as electronic books, infographics, and mobile applications, are progressing to improve education quality and learning levels, especially during the spread of the coronavirus, which resulted in the closure of schools, universities, and all educational facilities. To adapt to new developments, students' performance must be tracked in order to closely monitor all unfavorable barriers that may affect their academic progress. Educational data mining (EDM) is one of the most popular methods for predicting a student's performance. It helps monitoring and improving students' results. Therefore, in the current study, a model has been developed so that students can be informed about the results of the computer networks course in the middle of the second semester and 11 machine algorithms (out of five classes). A questionnaire was used to determine the effectiveness of using infographics for teaching a computer networks course, as the results proved the effectiveness of infographics as a technique for teaching computer networks. The Moodle (Modular Object-Oriented Dynamic Learning Environment) educational platform was used to present the course because of its distinctive characteristics that allow interaction between the student and the teacher, especially during the COVID-19 pandemic. In addition, the different methods of classification in data mining were used to determine the best practices used to predict students' performance using the weka program, where the results proved the effectiveness of the true positive direction of functions, multilayer perceptron, random forest trees, random tree and supplied test set, f-measure algorithms are the best ways to categories.
现代教育技术方法利用电子书、信息图表和移动应用程序等先进资源，正在不断发展以提高教育质量和学习水平，尤其是在冠状病毒传播期间，这导致学校、大学和所有教育设施关闭。为了适应新的发展情况，必须跟踪学生的表现，以便密切监测所有可能影响他们学业进展的不利障碍。教育数据挖掘（EDM）是预测学生表现最常用的方法之一。它有助于监测和提高学生的成绩。因此，在当前的研究中，开发了一个模型，以便在第二学期中期让学生了解计算机网络课程的成绩以及11种机器学习算法（共五类）。使用问卷调查来确定使用信息图表教授计算机网络课程的有效性，结果证明信息图表作为一种教授计算机网络的技术是有效的。由于Moodle（模块化面向对象动态学习环境）教育平台具有独特的特点，能够让学生和教师之间进行互动，特别是在新冠疫情期间，因此使用该平台来呈现课程。此外，使用数据挖掘中的不同分类方法，通过weka程序来确定用于预测学生表现的最佳实践，结果证明函数的真阳性方向、多层感知器、随机森林树、随机树和提供测试集、F值算法是分类的最佳方法。
