Using Learning Analytics for Preserving Academic Integrity
Open accessAbstract
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.
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Amigud et al. (2017) used machine learning algorithms to check academic integrity, by assessing the likelihood of student work being similar to their other work.
“Amigud, Arnedo-Moreno, Daradoumis, and Guerrero-Roldan (2017) used machine learning algorithms to check academic integrity, by assessing the likelihood of student work being similar to their other work.”
From Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019) · cites this paper as Amigud, Arnedo-Moreno, Daradoumis, and Guerrero-Roldan (2017)
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- Authors
- Amigud, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldan, A.-E.
- Affiliations
- Universitat Oberta de Catalunya (ES)
- Venue
- The International Review of Research in Open and Distributed Learning
- Publisher
- Athabasca University Press
- Published
- 2017-08-15
- Volume
- 18
- Issue
- 5
- ISSN
- 1492-3831 (ISSN-L)
- OpenAlex
- W2747279489
- Cited by
- 61
- References
- 44
- Citations by year
- 2025: 7 · 2024: 7 · 2023: 5 · 2022: 11 · 2021: 12 · 2020: 5 · 2019: 9 · 2018: 5
- FWCI
- 11.93
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- The International Review of Research in Open and Distributed Learning (publishedVersion, cc-by)
- Érudit (Université de Montréal) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The Oberta in Open Access (Universitat Oberta de Catalunya) (submittedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- learning analytics, academic integrity, online learning, student identity verification, student-generated content, language use patterns, machine learning, writing assignments, graduate students, academic accountability
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