PMID- 34803236
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230523
IS  - 0941-0643 (Print)
IS  - 1433-3058 (Electronic)
IS  - 0941-0643 (Linking)
VI  - 35
IP  - 16
DP  - 2023
TI  - E-learningDJUST: E-learning dataset from Jordan university of science and 
      technology toward investigating the impact of COVID-19 pandemic on education.
PG  - 11481-11495
LID - 10.1007/s00521-021-06712-1 [doi]
AB  - 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.
CI  - (c) The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of 
      Springer Nature 2021.
FAU - Abdullah, Malak
AU  - Abdullah M
AUID- ORCID: 0000-0002-5200-1959
AD  - Irbid, 22110 Jordan Computer Science, Jordan University of Science and 
      Technology. GRID: grid.37553.37. ISNI: 0000 0001 0097 5797
FAU - Al-Ayyoub, Mahmoud
AU  - Al-Ayyoub M
AD  - Irbid, 22110 Jordan Computer Science, Jordan University of Science and 
      Technology. GRID: grid.37553.37. ISNI: 0000 0001 0097 5797
FAU - AlRawashdeh, Saif
AU  - AlRawashdeh S
AD  - Irbid, 22110 Jordan Computer Science, Jordan University of Science and 
      Technology. GRID: grid.37553.37. ISNI: 0000 0001 0097 5797
FAU - Shatnawi, Farah
AU  - Shatnawi F
AD  - Irbid, 22110 Jordan Computer Science, Jordan University of Science and 
      Technology. GRID: grid.37553.37. ISNI: 0000 0001 0097 5797
LA  - eng
PT  - Journal Article
DEP - 20211113
PL  - England
TA  - Neural Comput Appl
JT  - Neural computing & applications
JID - 9313239
PMC - PMC8590139
OTO - NOTNLM
OT  - COVID-19
OT  - Correlation
OT  - E-learning
OT  - Machine learning
COIS- Conflict of interestThe authors declare that they have no conflict of interest.
EDAT- 2021/11/23 06:00
MHDA- 2021/11/23 06:01
PMCR- 2021/11/13
CRDT- 2021/11/22 06:33
PHST- 2021/09/03 00:00 [received]
PHST- 2021/10/27 00:00 [accepted]
PHST- 2021/11/23 06:01 [medline]
PHST- 2021/11/23 06:00 [pubmed]
PHST- 2021/11/22 06:33 [entrez]
PHST- 2021/11/13 00:00 [pmc-release]
AID - 6712 [pii]
AID - 10.1007/s00521-021-06712-1 [doi]
PST - ppublish
SO  - Neural Comput Appl. 2023;35(16):11481-11495. doi: 10.1007/s00521-021-06712-1. 
      Epub 2021 Nov 13.