@Article{2019.100000138,
AUTHOR = {Farhan Ullah, Abdullah Bajahzar, Hamza Aldabbas, Muhammad Farhan, Hamad Naeem, S. Sabahat H. Bukhari, Kaleem Razzaq Malik},
TITLE = {An E-Assessment Methodology Based on Artificial Intelligence Techniques  to Determine Students’ Language Quality and Programming Assignments’  Plagiarism},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {1},
PAGES = {169--180},
URL = {http://www.techscience.com/iasc/v26n1/39852},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.31209/2019.100000138}
}