Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/130942
Title: Exploring Gamification Research Trends Using Topic Modeling
Authors: Ayaz, A.
Ozyurt, O.
Al-Rahmi, W. M.
Salloum, S. A.
Shutaleva, A.
Alblehai, F.
Habes, M.
Issue Date: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Ayaz, A, Ozyurt, O, Al-Rahmi, WM, Salloum, SA, Shutaleva, A, Alblehai, F & Habes, M 2023, 'Exploring Gamification Research Trends Using Topic Modeling', IEEE Access, Том. 11, стр. 119676-119692. https://doi.org/10.1109/ACCESS.2023.3326444
Ayaz, A., Ozyurt, O., Al-Rahmi, W. M., Salloum, S. A., Shutaleva, A., Alblehai, F., & Habes, M. (2023). Exploring Gamification Research Trends Using Topic Modeling. IEEE Access, 11, 119676-119692. https://doi.org/10.1109/ACCESS.2023.3326444
Abstract: Gamification holds significant importance as an efficacious means to motivate individuals, stimulate their engagement, and foster desired behaviors. There is an increasing interest among researchers in exploring the domain of gamification. Consequently, it becomes crucial to identify specific research trends within this field. This study employs a comprehensive analysis of 4743 articles sourced from the Scopus database, utilizing the topic modeling approach, with the objective of discerning research patterns and trends within the gamification domain. The findings revealed the existence of thirteen distinct topics within the field. Notably, 'Health training,' 'Enhancing learning with technology,' and 'Game design framework' emerged as the most prominent topics, based on their frequency of research publications and popularity. This study serves as a valuable resource for researchers and practitioners seeking to stay abreast of the latest advancements in gamification. The identified issues through topic modeling can be employed to identify gaps in current research and potential directions for future research endeavors. © 2013 IEEE.
Keywords: GAMIFICATION
MACHINE LEARNING
TEXT MINING
TOPIC MODELING
TREND ANALYSIS
DATA MINING
LEARNING SYSTEMS
BIBLIOMETRIC
GAME
GAMIFICATION
MACHINE-LEARNING
MARKET RESEARCHES
RESEARCH TRENDS
SYSTEMATIC
TEXT-MINING
TOPIC MODELING
TREND ANALYSIS
MARKET RESEARCH
URI: http://elar.urfu.ru/handle/10995/130942
Access: info:eu-repo/semantics/openAccess
cc-by-nc-nd
License text: https://creativecommons.org/licenses/by-nc-nd/4.0/
SCOPUS ID: 85176309086
WOS ID: 001100934300001
PURE ID: 48553750
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2023.3326444
Appears in Collections:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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