research-article

Let's shine together!: a comparative study between learning analytics and educational data mining

Authors:

Guanliang Chen,

Rafael Ferreira Mello,

Dragan GaševićAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 544 - 553

https://doi.org/10.1145/3375462.3375500

Published: 23 March 2020 Publication History

Abstract

Learning Analytics and Knowledge (LAK) and Educational Data Mining (EDM) are two of the most popular venues for researchers and practitioners to report and disseminate discoveries in data-intensive research on technology-enhanced education. After the development of about a decade, it is time to scrutinize and compare these two venues. By doing this, we expected to inform relevant stakeholders of a better understanding of the past development of LAK and EDM and provide suggestions for their future development. Specifically, we conducted an extensive comparison analysis between LAK and EDM from four perspectives, including (i) the topics investigated; (ii) community development; (iii) community diversity; and (iv) research impact. Furthermore, we applied one of the most widely-used language modeling techniques (Word2Vec) to capture words used frequently by researchers to describe future works that can be pursued by building upon suggestions made in the published papers to shed light on potential directions for future research.

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Cited By

Wongvorachan TSrisuttiyakorn SSriklaub K(2024)Optimizing Learning: Predicting Research Competency via Statistical ProficiencyTrends in Higher Education10.3390/higheredu30300323:3(540-559)Online publication date: 8-Jul-2024
https://doi.org/10.3390/higheredu3030032
Risley KRöpke R(2024)A Growing Community of Practice on Games and Learning: A Literature Review with Bibliometric and Thematic AnalysesGames and Learning Alliance10.1007/978-3-031-78269-5_1(3-13)Online publication date: 18-Dec-2024
https://doi.org/10.1007/978-3-031-78269-5_1
Perrotta C(2024)Towards a Genealogical Critical Theory of Learning AnalyticsTheory Informing and Arising from Learning Analytics10.1007/978-3-031-60571-0_13(203-217)Online publication date: 29-Dec-2024
https://doi.org/10.1007/978-3-031-60571-0_13
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Index Terms

Let's shine together!: a comparative study between learning analytics and educational data mining
1. Applied computing
  1. Education
2. Information systems
  1. World Wide Web
    1. Web mining

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Published In

cover image ACM Other conferences

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2020

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LAK '20

LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Wongvorachan TSrisuttiyakorn SSriklaub K(2024)Optimizing Learning: Predicting Research Competency via Statistical ProficiencyTrends in Higher Education10.3390/higheredu30300323:3(540-559)Online publication date: 8-Jul-2024
https://doi.org/10.3390/higheredu3030032
Risley KRöpke R(2024)A Growing Community of Practice on Games and Learning: A Literature Review with Bibliometric and Thematic AnalysesGames and Learning Alliance10.1007/978-3-031-78269-5_1(3-13)Online publication date: 18-Dec-2024
https://doi.org/10.1007/978-3-031-78269-5_1
Perrotta C(2024)Towards a Genealogical Critical Theory of Learning AnalyticsTheory Informing and Arising from Learning Analytics10.1007/978-3-031-60571-0_13(203-217)Online publication date: 29-Dec-2024
https://doi.org/10.1007/978-3-031-60571-0_13
Wongvorachan THe SBulut O(2023)A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data MiningInformation10.3390/info1401005414:1(54)Online publication date: 16-Jan-2023
https://doi.org/10.3390/info14010054
Liu FRahwan TAlShebli B(2023)Non-White scientists appear on fewer editorial boards, spend more time under review, and receive fewer citationsProceedings of the National Academy of Sciences10.1073/pnas.2215324120120:13Online publication date: 20-Mar-2023
https://doi.org/10.1073/pnas.2215324120
Waheed HHassan SNawaz RAljohani NChen GGasevic D(2023)Early prediction of learners at risk in self-paced educationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118868213:PAOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118868
Alam AMohanty A(2023)Predicting Students’ Performance Employing Educational Data Mining Techniques, Machine Learning, and Learning AnalyticsCommunication, Networks and Computing10.1007/978-3-031-43140-1_15(166-177)Online publication date: 27-Sep-2023
https://doi.org/10.1007/978-3-031-43140-1_15
Wongvorachan TBulut OTsai YLindner M(2022)Improving Student Feedback Literacy in e-Assessments: A Framework for the Higher Education ContextTrends in Higher Education10.3390/higheredu10100021:1(16-29)Online publication date: 6-Dec-2022
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Baker RNasiar NGong WPorter C(2022)The impacts of learning analytics and A/B testing research: a case study in differential scientometricsInternational Journal of STEM Education10.1186/s40594-022-00330-69:1Online publication date: 14-Feb-2022
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Saqr MJovanovic JViberg OGašević D(2022)Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analyticsStudies in Higher Education10.1080/03075079.2022.206145047:12(2370-2391)Online publication date: 11-Apr-2022
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