Ust.edu.my (S.M.) Division of Computing, Middle East College, Information Oasis Muscat, P.B. No. 79, Al

Ust.edu.my (S.M.) Division of Computing, Middle East College, Information Oasis Muscat, P.B. No. 79, Al Rusayl 124, Oman; [email protected] College of Informatics and Applied Mathematics, University Malaysia Terengganu, Kuala Terengganu 21030, Malaysia; ku_sarker@yahoo Correspondence: [email protected] have to.edu.my; Tel.: 968-9819-Citation: Hasan, R.; Palaniappan, S.; Mahmood, S.; Abbas, A.; Sarker, K.U. Dataset of o-Phenanthroline Epigenetic Reader Domain Students’ Overall performance Using Student Details System, Moodle and the Mobile Application “eDify”. Information 2021, 6, 110. https:// doi.org/10.3390/data6110110 Academic Editors: Leonardo Grilli, Carla Rampichini, Maria Cecilia Verri and Donatella Merlini Received: 10 August 2021 Accepted: 19 October 2021 Published: 22 OctoberAbstract: The data presented within this write-up comprise an educational dataset collected from the student details technique (SIS), the finding out management program (LMS) named Moodle, and video interactions from the mobile application referred to as “eDify.” The dataset, in the larger educational institution (HEI) in Sultanate of Oman, comprises 5 modules of data from Spring 2017 to Spring 2021. The dataset consists of 326 student records with 40 attributes in total, like the students’ academic facts from SIS (which has 24 functions), the students’ activities performed on Moodle inside and outdoors the campus (comprising 10 options), as well as the students’ video interactions collected from eDify (consisting of six capabilities). The dataset is beneficial for researchers who desire to discover students’ academic performance in on-line understanding environments, and will assist them to model their educational datamining models. Furthermore, it may serve as an input for predicting students’ academic efficiency inside the module for educational datamining and understanding analytics. Furthermore, researchers are extremely recommended to refer for the original papers for far more specifics. Dataset: https://zenodo.org/record/5591907 (accessed on 18 October 2021). Dataset License: CC-BY four.0. Keywords and phrases: educational datamining; finding out management technique; prediction; student academic functionality; student details system1. SummaryPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Copyright: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This short article is an open access post distributed beneath the terms and situations of the Inventive Commons Attribution (CC BY) license (licenses/by/ 4.0/).Larger educational institutions (HEIs) employ a variety of understanding approaches primarily based on details and communications technologies (ICT). These approaches involve diverse mastering environments to facilitate the teaching and finding out process with ease and dissemination of expertise to their learners. Additionally, these environments keep track of your users and their interactions inside these environments for auditing and recovery purposes. The logs can assist stakeholders with important learning data, and when analyzed properly, will help to provide a better finding out practical experience to learners. Reports generating distinct users/courses may be applied to evaluate the efficacy with the courses along with the progress with the learners. α-Thujone Technical Information Insights can assist cater diverse finding out types, which helps to establish the complexity of courses, identifying distinct components with the content material that lead to problems in understanding the ideas and gaining insights into the future performance of learners. Lots of HEIs use machine finding out (.