Bibliographic Details
| Title: |
Plagiarism detection in students' programming assignments based on semantics: multimedia e-learning based smart assessment methodology. |
| Authors: |
Ullah, Farhan, Wang, Junfeng, Farhan, Muhammad, Jabbar, Sohail, Wu, Zhiming, Khalid, Shehzad |
| Source: |
Multimedia Tools & Applications; Apr2020, Vol. 79 Issue 13/14, p8581-8598, 18p |
| Subject Terms: |
PROGRAMMING languages, DATA structures, PLAGIARISM, SOURCE code, VIRTUAL classrooms, LEARNING management system |
| Abstract: |
The multimedia-based e-Learning methodology provides virtual classrooms to students. The teacher uploads learning materials, programming assignments and quizzes on university' Learning Management System (LMS). The students learn lessons from uploaded videos and then solve the given programming tasks and quizzes. The source code plagiarism is a serious threat to academia. However, identifying similar source code fragments between different programming languages is a challenging task. To solve the problem, this paper proposed a new plagiarism detection technique between C++ and Java source codes based on semantics in multimedia-based e-Learning and smart assessment methodology. First, it transforms source codes into tokens to calculate semantic similarity in token by token comparison. After that, it finds semantic similarity in scalar value for the complete source codes written in C++ and Java. To analyse the experiment, we have taken the dataset consists of four (4) case studies of Factorial, Bubble Sort, Binary Search and Stack data structure in both C++ and Java. The entire experiment is done in R Studio with R version 3.4.2. The experimental results show better semantic similarity results for plagiarism detection based on comparison. [ABSTRACT FROM AUTHOR] |
|
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |