E-learningDJUST:约旦科技大学的电子学习数据集,用于调查 COVID-19 大流行病对教育的影响。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-11-13 DOI:10.1007/s00521-021-06712-1
Malak Abdullah, Mahmoud Al-Ayyoub, Saif AlRawashdeh, Farah Shatnawi
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引用次数: 0

摘要

最近,COVID-19 大流行引发了不同的教育行为,特别是在封锁期间,以遏制病毒在全球的爆发。因此,世界各地的教育机构目前都在使用在线学习平台来维持其教育存在。本研究论文介绍并研究了一个名为 "E-LearningDJUST "的数据集,该数据集代表了约旦科技大学(JUST)大流行期间学生学习进度的样本。该数据集描述了该大学的学生样本,包括来自 11 个学院的 9246 名学生,他们分别在 2020 年春季、2020 年夏季和 2021 年秋季学期选修了四门课程。据我们所知,这是首个利用电子学习系统记录反映约旦高校学生学习进度的数据集。这项工作的主要发现之一是观察到电子学习事件与期末成绩(满分 100 分)之间的高度相关性。因此,E-LearningDJUST 数据集采用了两种稳健的机器学习模型(随机森林和 XGBoost)和一种简单的深度学习模型(前馈神经网络)来预测学生的成绩。以 RMSE 作为主要评估标准,RMSE 值介于 7 到 17 之间。在其他主要发现中,随机森林的特征选择应用为所有课程带来了更好的预测结果,因为 RMSE 差值在(0-0.20)之间。最后,一项对比研究考察了冠状病毒大流行前后学生的成绩,以了解大流行对学生成绩的影响。与大流行之前相比,大流行期间的成功率较高,这在意料之中,因为考试是在线进行的。不过,获得高分的学生比例仍与大流行前的课程相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.

Recently, the COVID-19 pandemic has triggered different behaviors in education, especially during the lockdown, to contain the virus outbreak in the world. As a result, educational institutions worldwide are currently using online learning platforms to maintain their education presence. This research paper introduces and examines a dataset, E-LearningDJUST, that represents a sample of the student's study progress during the pandemic at Jordan University of Science and Technology (JUST). The dataset depicts a sample of the university's students as it includes 9,246 students from 11 faculties taking four courses in spring 2020, summer 2020, and fall 2021 semesters. To the best of our knowledge, it is the first collected dataset that reflects the students' study progress within a Jordanian institute using e-learning system records. One of this work's key findings is observing a high correlation between e-learning events and the final grades out of 100. Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. However, the proportion of students with high marks remained similar to that of pre-pandemic courses.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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