Using Learning Analytics for Preserving Academic Integrity ARTICLE
Alexander Amigud, Joan Arnedo-Moreno, Department of Computer Science, Multimedia and Telecommunications Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain ; Thanasis Daradoumis, Greece ; Ana-Elena Guerrero-Roldan, Department of Computer Science, Multimedia and Telecommunications Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain
IRRODL Volume 18, Number 5, ISSN 1492-3831 Publisher: Athabasca University Press
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\u2019 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.
Amigud, A., Arnedo-Moreno, J., Daradoumis, T. & Guerrero-Roldan, A.E. (2017). Using Learning Analytics for Preserving Academic Integrity. The International Review of Research in Open and Distributed Learning, 18(5),. Athabasca University Press. Retrieved March 22, 2018 from https://www.learntechlib.org/p/180432/.
© 2017 Athabasca University Press