research-article

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Tweetology of Learning Analytics: What does Twitter tell us about the trends and development of the field?

Authors:

Mohammad Khalil,

Jacqueline Wong,

Martin Heitmann,

Gleb BelokrysAuthors Info & Claims

LAK22: LAK22: 12th International Learning Analytics and Knowledge Conference

Pages 347 - 357

https://doi.org/10.1145/3506860.3506914

Published: 21 March 2022 Publication History

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Abstract

Twitter is a very popular microblogging platform that has been actively used by scientific communities to exchange scientific information and to promote scholarly discussions. The present study aimed to leverage the tweet data to provide valuable insights into the development of the learning analytics field since its initial days. Descriptive analysis, geocoding analysis, and topic modeling were performed on over 1.6 million tweets related to learning analytics posted between 2010-2021. The descriptive analysis reveals an increasing popularity of the field on the Twittersphere in terms of number of users, twitter posts, and hashtags emergence. The topic modeling analysis uncovers new insights of the major topics in the field of learning analytics. Emergent themes in the field were identified, and the increasing (e.g., Artificial Intelligence) and decreasing (e.g., Education) trends were shared. Finally, the geocoding analysis indicates an increasing participation in the field from more diverse countries all around the world. Further findings are discussed in the paper.

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Cited By

Fernandez EAnderies Winata MFasya FGunawan A(2022)Improving IndoBERT for Sentiment Analysis on Indonesian Stock Trader Slang Language2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)10.1109/IoTaIS56727.2022.9975975(240-244)Online publication date: 24-Nov-2022
https://doi.org/10.1109/IoTaIS56727.2022.9975975

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cover image ACM Other conferences

LAK22: LAK22: 12th International Learning Analytics and Knowledge Conference

March 2022

582 pages

ISBN:9781450395731

DOI:10.1145/3506860

Program Chairs:
Alyssa Friend Wise
New York University, USA
,
Roberto Martinez-Maldonado
Monash University, Australia
,
Isabel Hilliger
Pontificia Universidad Católica de Chile, Chile

Copyright © 2022 ACM.

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Publication History

Published: 21 March 2022

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LAK22: 12th International Learning Analytics and Knowledge Conference

March 21 - 25, 2022

Online, USA

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Cited By

Fernandez EAnderies Winata MFasya FGunawan A(2022)Improving IndoBERT for Sentiment Analysis on Indonesian Stock Trader Slang Language2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)10.1109/IoTaIS56727.2022.9975975(240-244)Online publication date: 24-Nov-2022
https://doi.org/10.1109/IoTaIS56727.2022.9975975

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