Notice to Authors

Due to the overwhelming number of submissions to IRRODL, the journal has already met its publication quota for 2019. As a result, for a period that will not exceed six months, IRRODL will no longer be accepting submissions after May 1, 2019. In order to improve our service to the academic community, and to ensure a six month review to publication cycle, IRRODL will be moving to a regularized publication schedule in 2020. More information will be provided later this year.

We thank our authors, reviewers, and readers for their unwavering and exceptional support in making our journal one of the world’s most successful, open access journals in the field of open and distributed learning.

Using Learning Analytics for Preserving Academic Integrity

  • Alexander Amigud Department of Computer Science, Multimedia and Telecommunications Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain
  • Joan Arnedo-Moreno Department of Computer Science, Multimedia and Telecommunications Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain
  • Thanasis Daradoumis 1. Department of Computer Science, Multimedia and Telecommunications Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain 2. Department of Cultural Technology and Communication University of the Aegean, University Hill , Mytilene 81100, Greece
  • Ana-Elena Guerrero-Roldan Department of Computer Science, Multimedia and Telecommunications Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, 08018 Barcelona, Spain
Keywords: electronic assessment, learning analytics, academic integrity

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.

Published
2017-08-15
How to Cite
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). https://doi.org/10.19173/irrodl.v18i5.3103
Section
Research Articles