|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 187 - Issue 66 |
| Published: December 2025 |
| Authors: Neda Lotfi, Suhair Amer, Ankita Maharjan |
10.5120/ijca2025926107
|
Neda Lotfi, Suhair Amer, Ankita Maharjan . Integrating Plagiarism Detection into a Class Management System for Academic Integrity. International Journal of Computer Applications. 187, 66 (December 2025), 1-8. DOI=10.5120/ijca2025926107
@article{ 10.5120/ijca2025926107,
author = { Neda Lotfi,Suhair Amer,Ankita Maharjan },
title = { Integrating Plagiarism Detection into a Class Management System for Academic Integrity },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 66 },
pages = { 1-8 },
doi = { 10.5120/ijca2025926107 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Neda Lotfi
%A Suhair Amer
%A Ankita Maharjan
%T Integrating Plagiarism Detection into a Class Management System for Academic Integrity%T
%J International Journal of Computer Applications
%V 187
%N 66
%P 1-8
%R 10.5120/ijca2025926107
%I Foundation of Computer Science (FCS), NY, USA
This system is a comprehensive database designed to assist faculty members in efficiently managing academic information and monitoring student performance. It allows tracking and maintenance of key data, including class details, student records, activities, grades, and submitted documents. Faculty members can store and update information such as student names and addresses, class names, numbers, locations, times, and enrolled students. The system also supports management of classroom activities by recording activity names, numbers, maximum points, due dates, and instructions. A built-in grade book enables faculty to maintain and analyze student scores for each activity, while an integrated plagiarism detection feature compares newly uploaded files against previously submitted ones to ensure academic integrity. Additionally, the system provides tools for retrieving information, identifying missing assignments, and generating performance summaries such as highest, lowest, and average scores, thereby enhancing the overall efficiency of academic record management.