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http://elar.urfu.ru/handle/10995/130942
Название: | Exploring Gamification Research Trends Using Topic Modeling |
Авторы: | Ayaz, A. Ozyurt, O. Al-Rahmi, W. M. Salloum, S. A. Shutaleva, A. Alblehai, F. Habes, M. |
Дата публикации: | 2023 |
Издатель: | Institute of Electrical and Electronics Engineers Inc. |
Библиографическое описание: | 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 |
Аннотация: | 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. |
Ключевые слова: | 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 |
Условия доступа: | info:eu-repo/semantics/openAccess cc-by-nc-nd |
Текст лицензии: | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Идентификатор SCOPUS: | 85176309086 |
Идентификатор WOS: | 001100934300001 |
Идентификатор PURE: | 48553750 |
ISSN: | 2169-3536 |
DOI: | 10.1109/ACCESS.2023.3326444 |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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2-s2.0-85176309086.pdf | 1,06 MB | Adobe PDF | Просмотреть/Открыть |
Лицензия на ресурс: Лицензия Creative Commons