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An E-Assessment Methodology Based on Artificial Intelligence Techniques to Determine Students’ Language Quality and Programming Assignments’ Plagiarism

Farhan Ullah1,4,*, Abdullah Bajahzar2, Hamza Aldabbas3, Muhammad Farhan4, Hamad Naeem1, S. Sabahat H. Bukhari4,5, Kaleem Razzaq Malik6

1 College of Computer Science, Sichuan University, Chengdu 610065, China
2 Department of Computer Science and Information, College of Science at Zulfi, Majmaah University, Zulfi 11932, Saudi Arabia
3 Prince Abdullah bin Ghazi Faculty of Information and Technology, Al-Balqa Applied University, Al-Salt- Jordan
4 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
5 College of Computer Science, Chongqing University, Chongqing 400044, China
6 Department of Computer Science & Engineering, Air University, Multan Campus, Multan 60000, Pakistan

* Corresponding Author: Farhan Ullah, email,

Intelligent Automation & Soft Computing 2020, 26(1), 169-180.


This research aims to an electronic assessment (e-assessment) of students’ replies in response to the standard answer of teacher’s question to automate the assessment by WordNet semantic similarity. For this purpose, a new methodology for Semantic Similarity through WordNet Semantic Similarity Techniques (SS-WSST) has been proposed to calculate semantic similarity among teacher’ query and student’s reply. In the pilot study-1 42 words’ pairs extracted from 8 students’ replies, which marked by semantic similarity measures and compared with manually assigned teacher’s marks. The teacher is provided with 4 bins of the mark while our designed methodology provided an exact measure of marks. Secondly, the source codes plagiarism in students' assignments provide smart e-assessment. The WordNet semantic similarity techniques are used to investigate source code plagiarism in binary search and stack data structures programmed in C++, Java, C# respectively.


Cite This Article

F. Ullah, A. Bajahzar, H. Aldabbas, M. Farhan, H. Naeem et al., "An e-assessment methodology based on artificial intelligence techniques to determine students’ language quality and programming assignments’ plagiarism," Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 169–180, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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