Let's shine together! a comparative study between Learning Analytics and Educational Data Mining

Guanliang Chen, Vitor Rolim, Rafael Ferreira Mello, Dragan Gaševic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


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 widelyused 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.

Original languageEnglish
Title of host publicationLAK20 Conference Proceedings
EditorsMaren Scheffel, Vitomir Kovanović, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450377126
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th
https://lak20.solaresearch.org (Website)
https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3375462 (Website)


ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
Internet address


  • Educational Data Mining
  • Hierarchical Topic Detection
  • Language Modeling
  • Learning Analytics

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