Educational data mining and learning analytics: a review of educational management in e-learning
| Date | 17 July 2023 |
| Pages | 149-163 |
| DOI | https://doi.org/10.1108/IDD-10-2022-0099 |
| Published date | 17 July 2023 |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
| Author | Anaile Rabelo,Marcos W. Rodrigues,Cristiane Nobre,Seiji Isotani,Luis Zárate |
Educational data mining and learning
analytics: a review of educational
management in e-learning
Anaile Rabelo, Marcos W. Rodrigues and Cristiane Nobre
Department of Computer Science, PUC Minas, Belo Horizonte, Brazil
Seiji Isotani
Department of Computer Science, USP, São Paulo, Brazil, and
Luis Z
arate
Department of Computer Science, PUC Minas, Belo Horizonte, Brazil
Abstract
Purpose –The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment
from a managerial perspective.
Design/methodology/approach –This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the
e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and
management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to
2023, totaling 30 years of research in EDM.
Findings –From this review, it was observed that managers have been concerned about the effectiveness of the platform used by stud ents as it
contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on
these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring
systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of
different learning styles.
Originality/value –This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher
education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’
knowledge, this review is original because other contributions do not focus on the manager.
Keywords Educational data mining, Education, e-Learning, Administrator
Paper type Literature review
1. Introduction
Currently, there is a growing interest in the use of data and
information in education e-learning, especially in an attempt to
measure the teaching-learning (TL) process and to optimize
system performance at different levels, from the TL process
itself to the level of management of educational institutions
(Rodrigues et al.,2018). According to Gardner and Brooks
(2018), the endeavor for the improvement of educational
processes has shownthat generalizing the results of the analyses
proves to be ineffective. This occurs because different
educational environments have peculiarities and different
characteristics. For example, massive open online courses
(MOOCs) platforms are incomparable to other educational
modalities because they have different characteristics in terms
of structure, model and management. Even in the context of
online courses, the objectives, metrics and structuring of
MOOCs cannot be compared to the degrees of e-learning
modality (Gardnerand Brooks, 2018).
The application of data mining in the educational context,
known as educational data mining(EDM), is widely applied to
identify patterns extracted from collected educational data,
with the aim to improve the TL process. When applied, EDM
techniques and methodscan reveal important information, which
are used by administrators who seek to optimize educational
processes and increase their assertive decision-making capacity.
For example, Kerr (2016) applies EDM to understand how a
student learns. Kim et al. (2016) examine the behaviors of
educational actors within online learning environments. Burgos
et al. (2018) apply EDM techniques, considering activity grades
and looking for prognostics on the possibility of student dropout.
All of the aforementioned approaches are pertinent at the
managerial level of educational institutions.
The current issue and full text archiveof this journal is available on Emerald
Insight at: https://www.emerald.com/insight/2398-6247.htm
Information Discovery and Delivery
52/2 (2024) 149–163
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-10-2022-0099]
Received 11 October 2022
Revised 26 January 2023
27 April 2023
2 June 2023
Accepted 9 June 2023
149
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