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Title: Using learning analytics for preserving academic integrity
Author: Amigud, Alexander
Arnedo Moreno, Joan  
Daradoumis Haralabus, Atanasi  
Guerrero Roldán, Ana Elena  
Keywords: electronic assessment
learning analytics
academic integrity
Issue Date: Aug-2017
Publisher: International Review of Research in Open and Distributed Learning
Citation: Amigud, A., Arnedo Moreno, J., Daradoumis Haralabus, A., Guerrero Roldán, A. (2017). "Using learning analytics for preserving academic integrity". International Review of Research in Open and Distributed Learning, 18, 5. ISSN 1492-3831
Abstract: This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students¿ patterns of language use from data, providing an accessible and non-invasive validation of student identities and student-produced content. To assess the performance of the proposed approach, we conducted a series of experiments using written assignments of graduate students. The proposed method yielded a mean accuracy of 93%, exceeding the baseline of human performance that yielded a mean accuracy rate of 12%. The results suggest a promising potential for developing automated tools that promote accountability and simplify the provision of academic integrity in the e-learning environment.
Language: English
ISSN: 1492-3831MIAR
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