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10.14489/vkit.2022.05.pp.018-025

DOI: 10.14489/vkit.2022.05.pp.018-025

Воронина А. А., Шабалина О. А., Садовникова Н. П.
ВОВЛЕЧЕННОСТЬ ПОЛЬЗОВАТЕЛЯ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ: СПОСОБЫ ОЦЕНКИ И ВОЗМОЖНОСТИ ПРИМЕНЕНИЯ
(с. 18-25)

Аннотация. Исследован феномен вовлеченности как характеристики профессиональной деятельности человека. Проведена классификация методов оценки вовлеченности, различаемых по типам собираемых данных, способам их сбора, обработки и интерпретации в контексте вовлеченности. Рассмотрено понятие вовлеченности пользователя программного обеспечения (ПО) как реакции пользователя на взаимодействие с этим программным обеспечением. Показаны возможности применения результатов оценки вовлеченности пользователя ПО для оценки деятельности людей, непосредственно связанной с использованием ПО, и качества самого ПО, с которым взаимодействует пользователь.

Ключевые слова:  программное обеспечение; характеристика программного обеспечения; вовлеченность; оценка вовлеченности; метод оценки вовлеченности; вовлеченность пользователя программного обеспечения; автоматизированная оценка вовлеченности; программное средство оценки вовлеченности.

 

Voronina A. A., Shabalina O. A., Sadovnikova N. P.
SOFTWARE USER ENGAGEMENT: METHODS OF EVALUATION AND APPLICATIONS
(pp. 18-25)

Abstract. The article explores phenomenon of engagement as a socio-psychological category that determines physical, emotional and intellectual state of a person involved in any activity. Further, engagement is considered as one of the factors that significantly affects the effectiveness and success of a person's professional activity. Methods for assessing engagement, distinguished by the types of data collected and approaches for their collection, processing and interpretation in the context of engagement, are analyzed. Existing software tools for assessing engagement based on self-assessment and systems for automatic assessment of engagement based on computer vision and machine learning methods are described. The concept of software user engagement as a user's reaction to interaction with this software is considered. The possibilities of using the results of user engagement assessment to assess the activities of people directly related to the use of the software are shown. Various factors that determine the interaction of the user with the software and affect his engagement are analyzed. The possibilities of using user engagement to assess competitiveness, usability, popularity and the overall quality of the software itself with which the user interacts are considered. Approaches to using the results of the user engagement assessment for adapting the learning process in learning systems are considered. The method of combined online assessment of engagement is described, in which engagement is considered as an indirect assessment of the success of the user's learning process and the results of the analysis of engagement are used to adapt the learning process to this user. The results of the study are represented as a mental map with the categorization of the concept of engagement. Directions for further research are described related to the development of an intelligent support system for assessing user engagement, based on the analysis of observed heterogeneous data on the current state of the user and behavioral metrics collected in the process of user interaction with the software and a combination of methods for their processing.

Keywords: Software; Software characteristics; Engagement; Engagement assessment; Engagement assessment method; Software user engagement; Automated assessment of engagement; Software tool for assessment of engagement.

Рус

А. А. Воронина, О. А. Шабалина, Н. П. Садовникова (Волгоградский государственный технический университет, Волгоград, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

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A. A. Voronina, O. A. Shabalina, N. P. Sadovnikova (Volgograd State Technical University, Volgograd, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

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7. Gallup. Available at: https://www.gallup.com (Accessed: 25.02.2022).
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10. Gray J., Diloreto M. (2016). The Effects of Student Engagement, Student Satisfaction, and Perceived Learning in Online Learning Environments. International Journal of Educational Leadership Preparation, Vol. 11, pp. 89 ‒ 119.
11. Lalmas M., O’Brien H., Yom-Tov E. (2015). Measuring User Engagement. Synthesis Lectures on Information Concepts, Retrieval, and Services, pp. 11 ‒ 58.
12. Voronina A. A., Shabalina O. A., Kataev A. V. (2019). Methods for assessing the involvement of users of interactive applications. Informatsionnye tekhnologii v nauke, obrazovanii i upravlenii, 14(4), pp. 70 ‒ 74. [in Russian language]
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14. O’Brien H. L, Cairns P., Hall M. (2018). A Practical Approach to Measuring User Engagement with the Refined User Engagement Scale (UES) and New UES Short Form. International Journal of Human-Computer Studies, Vol. 112, pp. 28 ‒ 39.
15. EFS (Enterprise Feedback Suite) Survey. Available at: https://community.questback.com/s/questback-efs-survey (Accessed: 25.11.2021).
16. Hung Ya-Hsin, Parsons P. (2017). Assessing User Engagement in Information Visualization.
17. Whitehill J., Serpell Z., Lin Y. et al. (2014). The Faces of Engagement: Automatic Recognition of Student Engagement from Facial Expressions. IEEE Transactions on Affective Computing, Vol. 5, (1), pp. 86 ‒ 98. DOI 10.1109/TAFFC.2014.2316163
18. Nezami O. M., Dras M., Hamey L. et al. (2020). Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression.
19. Monkaresi H., Bosch N., Calvo R. A., D'Mello S. K. (2017). Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate. IEEE Transactions on Affective Computing, Vol. 8, (1), pp. 15 ‒ 28. DOI https://dl.acm.org/doi/10.1109/TAFFC.2016.2515084
20. Holmes G., Donkin A., Witten I. H. (1994). WEKA: a Machine Learning Workbench. Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357 – 361.
21. Ng S. C., Sweeney J. C., Plewa C. (2020). Customer Engagement: A Systematic Review and Future Research Priorities. Australasian Marketing Journal, Vol. 28, pp. 235‒252. Available at: https://doi.org/10.1016/j.ausmj. 2020.05.004
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26. Jones S. P., Patel V., Saxena S. et al. (2014). How Google’s 'ten Things we Know to be True' Could Guide the Development of Mental Health Mobile Apps. Health Aff (Millwood), Vol. 33, (9), pp. 1603 ‒ 1611.
27. Taki S., Lymer S., Russell C. G. et al. (2017). Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy app Engagement Index. JMIR Mhealth Uhealth, Vol. 5, (6):e89.
28. Zhao Z., Balagué C. (2015). Designing Branded Mobile Apps: Fundamentals and Recommendations. Business Horizons, Vol. 58, (3), pp. 305 ‒ 315.
29. Kim Y. H, Kim D. J, Wachter K. (2013). A Study of Mobile User Engagement (MoEN): Engagement Motivations, Perceived Value, Satisfaction, and Continued Engagement Intention. Decision Support Systems, Vol. 56, pp. 361 ‒ 370.
30. Tarute A., Nikou S., Gatautis R. (2017). Mobile Application Driven Consumer Engagement. Telematics and Informatics, Vol. 34, (4), pp. 145 ‒ 156.
31. Lehmann J., Lalmas M., Yom-Tov E., Dupret G. (2012). Model of User Engagement.
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