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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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New York
Springer US
01.04.2020
Springer Nature B.V |
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| ISSN: | 1380-7501, 1573-7721 |
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| 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
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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. |
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| AbstractList | 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. 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. |
| Author | Jabbar, Sohail Khalid, Shehzad Wu, Zhiming Farhan, Muhammad Ullah, Farhan Wang, Junfeng |
| Author_xml | – sequence: 1 givenname: Farhan surname: Ullah fullname: Ullah, Farhan organization: College of Computer Science, Sichuan University, Department of Computer Science, COMSATS Institute for Information Technology – sequence: 2 givenname: Junfeng surname: Wang fullname: Wang, Junfeng email: wangjf@scu.edu.cn organization: School of Aeronautics and Astronautics and College of Computer Science, Sichuan University – sequence: 3 givenname: Muhammad surname: Farhan fullname: Farhan, Muhammad organization: Department of Computer Science, COMSATS Institute for Information Technology – sequence: 4 givenname: Sohail surname: Jabbar fullname: Jabbar, Sohail organization: Department of Computer Science, National Textile University – sequence: 5 givenname: Zhiming surname: Wu fullname: Wu, Zhiming organization: School of Aeronautics and Astronautics, Sichuan University – sequence: 6 givenname: Shehzad surname: Khalid fullname: Khalid, Shehzad organization: Department of Computer Science, Bahria University |
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| Cites_doi | 10.7763/IJCTE.2012.V4.555 10.1109/TSE.2015.2418777 10.1145/986213.986216 10.1007/s11554-016-0662-3 10.1007/s11042-015-3228-7 10.1007/s11042-015-2918-5 10.1016/j.engappai.2013.06.007 10.1016/j.iheduc.2014.04.001 10.1145/2597716.2597723 10.1007/s11280-013-0206-8 10.1109/TIT.2004.830793 10.1016/j.future.2017.09.037 10.1007/s11042-016-4212-6 10.1016/j.knosys.2008.03.045 10.1207/s15389286ajde1903_3 10.1016/j.compedu.2013.08.005 10.1016/j.diin.2015.01.011 10.1109/TC.2011.223 10.3280/CAD2014-001006 10.1109/SACI.2014.6840038 10.1145/2970276.2970326 10.31219/osf.io/zpsd7 10.1002/9781118900772.etrds0053 10.1145/2884781.2884877 10.1109/ICSME.2016.18 10.1109/SCAM.2014.17 10.1109/TSE.2017.2655046 10.1145/330560.331015 10.1109/IIAI-AAI.2015.174 10.1145/2908812.2908870 10.1145/302405.302918 10.1137/1.9780898718164 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018. |
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