Dataset of Students’ Performance Using Student Information System, Moodle and the Mobile Application “eDify”

Raza Hasan, Sellappan Palaniappan, Salman Mahmood, Ali Abbas and Kamal Uddin Sarker
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Raza Hasan: Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, Petaling Jaya 47810, Malaysia
Sellappan Palaniappan: Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, Petaling Jaya 47810, Malaysia
Salman Mahmood: Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, Petaling Jaya 47810, Malaysia
Ali Abbas: Department of Computing, Middle East College, Knowledge Oasis Muscat, P.B. No. 79, Al Rusayl 124, Oman
Kamal Uddin Sarker: School of Informatics and Applied Mathematics, University Malaysia Terengganu, Kuala Terengganu 21030, Malaysia

Data, 2021, vol. 6, issue 11, 1-10

Abstract: The data presented in this article comprise an educational dataset collected from the student information system (SIS), the learning management system (LMS) called Moodle, and video interactions from the mobile application called “eDify.” The dataset, from the higher educational institution (HEI) in Sultanate of Oman, comprises five modules of data from Spring 2017 to Spring 2021. The dataset consists of 326 student records with 40 features in total, including the students’ academic information from SIS (which has 24 features), the students’ activities performed on Moodle within and outside the campus (comprising 10 features), and the students’ video interactions collected from eDify (consisting of six features). The dataset is useful for researchers who want to explore students’ academic performance in online learning environments, and will help them to model their educational datamining models. Moreover, it can serve as an input for predicting students’ academic performance within the module for educational datamining and learning analytics. Furthermore, researchers are highly recommended to refer to the original papers for more details.

Keywords: educational datamining; learning management system; prediction; student academic performance; student information system (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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