Mpus IP address, along with the rest have been IP addresses from outdoors the campus. The access time was then converted into minutes to understand the time spent around the activities inside or outdoors the campus. For data extracted from eDify, all 4 Cefuroxime axetil medchemexpress attributes had been taken and no conversion was performed around the data. three.four. Final Dataset The final .csv dataset was the comprehensive dataset, with 21 out of 40 attributes that may be utilized for this study. This dataset could be employed with any datamining tool for classifying and predicting student academic overall performance making use of EDM. From SIS, 15 out from the 24 attributes were Cholesteryl sulfate (sodium) Formula selected for the final dataset: “ApplicantName”, “CGPA”, “AttemptCount”, “RemoteStudent”, “Probation”, “HighRisk”, “TermExceeded”, “AtRisk”, “AtRiskSSC”, “OtherModules”, “PlagiarismHistory”, “CW1”, “CW2”, “ESE” and “Result (Target Variable)”. From Moodle, two attributes have been selected based on the activities performed on Moodle from outside or within the campus: “Online C” and “Online O”. From eDify, four attributes were chosen: “Played”, “Paused”, “Likes” and “Segment”. The final dataset can help researchers to improved recognize the studying behaviors from the students inside the on-line studying atmosphere setting. four. Conclusions This article gives the dataset with numerous mastering environments, which will be valuable for researchers who choose to explore students’ academic performance in on the internet learning environments. This will enable them to model their educational datamining models. The dataset will probably be helpful for researchers who wish to conduct comparative research on student behaviors and patterns associated with online understanding environments. It can additional enable to kind an educational datamining model that can be applied to diverse classification algorithms to predict thriving students. In addition, function selection methods is often applied, which can deliver a far better accuracy price for predicting students’ academic functionality. For future studies, weekly video interaction records is usually regarded to supply superior insights into video finding out analytics and student functionality. Furthermore, the data might be used using the predictive churn model to act as an early warning method for the dropouts within the course.Information 2021, six,9 of5. Patents Hasan, Raza, Palaniappan, Sellappan, Mahmood, Salman, and Asif Hussain, Shaik. A novel strategy and system to improve teaching and studying plus the student evaluation method employing the “eDify” mobile application. AU Patent Innovation 2021103523, filed 22 June 2021.Supplementary Materials: The following are offered on the web at mdpi/article/10 .3390/data6110110/s1, Information S1: csv files. Author Contributions: Conceptualization and methodology, R.H.; supervision, S.P.; information curation and validation, S.M.; writing–original draft preparation and visualization, A.A.; investigation and writing–review and editing, K.U.S. All authors have study and agreed for the published version from the manuscript. Funding: This study received no external funding. Institutional Overview Board Statement: Not Applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved inside the study. Data Availability Statement: The authors confirm that the information supporting the findings of this study are available inside the article and/or its Supplementary Supplies. Acknowledgments: The authors of this data write-up are particularly thankful to all the faculty and students who participated within this study. Conflicts of Interest: The auth.