Using Learning Analytics for Preserving Academic Integrity

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 stud...

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Autores principales:	Alexander Amigud, Joan Arnedo-Moreno, Thanasis Daradoumis, Ana-Elena Guerrero-Roldan
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2017
Materias:	electronic assessment learning analytics academic integrity Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/426a49954056401f9c1435b4afc2e031
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id	oai:doaj.org-article:426a49954056401f9c1435b4afc2e031
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spelling	oai:doaj.org-article:426a49954056401f9c1435b4afc2e0312021-12-02T19:25:28ZUsing Learning Analytics for Preserving Academic Integrity10.19173/irrodl.v18i5.31031492-3831https://doaj.org/article/426a49954056401f9c1435b4afc2e0312017-08-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/3103https://doaj.org/toc/1492-3831This 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. Alexander AmigudJoan Arnedo-MorenoThanasis DaradoumisAna-Elena Guerrero-RoldanAthabasca University Pressarticleelectronic assessmentlearning analyticsacademic integritySpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 18, Iss 5 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	electronic assessment learning analytics academic integrity Special aspects of education LC8-6691
spellingShingle	electronic assessment learning analytics academic integrity Special aspects of education LC8-6691 Alexander Amigud Joan Arnedo-Moreno Thanasis Daradoumis Ana-Elena Guerrero-Roldan Using Learning Analytics for Preserving Academic Integrity
description	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.
format	article
author	Alexander Amigud Joan Arnedo-Moreno Thanasis Daradoumis Ana-Elena Guerrero-Roldan
author_facet	Alexander Amigud Joan Arnedo-Moreno Thanasis Daradoumis Ana-Elena Guerrero-Roldan
author_sort	Alexander Amigud
title	Using Learning Analytics for Preserving Academic Integrity
title_short	Using Learning Analytics for Preserving Academic Integrity
title_full	Using Learning Analytics for Preserving Academic Integrity
title_fullStr	Using Learning Analytics for Preserving Academic Integrity
title_full_unstemmed	Using Learning Analytics for Preserving Academic Integrity
title_sort	using learning analytics for preserving academic integrity
publisher	Athabasca University Press
publishDate	2017
url	https://doaj.org/article/426a49954056401f9c1435b4afc2e031
work_keys_str_mv	AT alexanderamigud usinglearninganalyticsforpreservingacademicintegrity AT joanarnedomoreno usinglearninganalyticsforpreservingacademicintegrity AT thanasisdaradoumis usinglearninganalyticsforpreservingacademicintegrity AT anaelenaguerreroroldan usinglearninganalyticsforpreservingacademicintegrity
_version_	1718376554881351680

Using Learning Analytics for Preserving Academic Integrity

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