Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology

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...

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Veröffentlicht in:Multimedia tools and applications Jg. 79; H. 13-14; S. 8581 - 8598
Hauptverfasser: Ullah, Farhan, Wang, Junfeng, Farhan, Muhammad, Jabbar, Sohail, Wu, Zhiming, Khalid, Shehzad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2020
Springer Nature B.V
Schlagworte:
C plus plus
C++ (programming language)
Codes
Colleges & universities
Computer Communication Networks
Computer Science
Data structures
Data Structures and Information Theory
Distance learning
Java
Methodology
Multimedia
Multimedia Information Systems
Online instruction
Plagiarism
Programming languages
Security management
Semantics
Similarity
Source code
Special Purpose and Application-Based Systems
Students
Multimedia
Latent Semantic Analysis
e-Learning
Smart Assessment
Semantic Similarity
Machine Learning
ISSN:1380-7501, 1573-7721
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-5827-6