Alberto Mora
Visiting Ph.D. Student, Gamification Elements
Alberto is a Ph.D. student at the Open University Of Catalonia, Spain, under the supervision of Dr. Joan Arnedo and Dr. Carina González, and a visiting researcher at the HCI Games Group. He holds a M.Sc in education and teacher training from the University of La Rioja and he is a computer engineer from the University of La Laguna. His main interests include gamification design in the field of learning environments, healthcare, and wellness. His thesis is titled "A framework for agile design of gamification services". Before starting his academic career, he has worked for several years as a software developer and IT consultant/manager, as well as a high school and vocational degree instructor.
Projects
Publications
Year 2019
Gustavo F. Tondello,
Alberto Mora, Andrzej Marczewski, and
Lennart E. Nacke. 2019. Empirical Validation of the Gamification User Types Hexad Scale in English and Spanish. In
International Journal of Human-Computer Studies 127: 95-111. Elsevier.
doi:10.1016/j.ijhcs.2018.10.002 @article{Tondello2018e,
abstract = {Gamification, the use of game elements in non-game systems, is now established as a relevant research field in human-computer interaction (HCI). Several empirical studies have shown that gameful interventions can increase engagement and generate desired behavioural outcomes in HCI applications. However, some inconclusive results indicate that we need a fuller understanding of the mechanisms and effects of gamification. The Gamification User Types Hexad scale allows us to parse different user motivations in participants‘ interactions with gameful applications, which are measured using a self-report questionnaire. Each user type represents a style of interaction with gameful applications, for example, if the interactions are more focused on achievements, socialization, or rewards. Thus, by scoring an individual in each one of the user types of the Hexad model, we can establish a profile of user preferences for gameful interactions. However, we still lack a substantial empirical validation of this scale. Therefore, we set out to validate the factor structure of the scale, in both English and Spanish, by conducting three studies, which also investigated the distribution of the Hexad‘s user types in the sample. Our findings support the structural validity of the scale, as well as suggesting opportunities for improvement. Furthermore, we demonstrate that some user types are more common than others and that gender and age correlate with a person‘s user types. Our work contributes to HCI research by further validating the utility of the Gamification User Types Hexad scale, potentially affording researchers a deeper understanding of the mechanisms and effects of gameful interventions.},
author = {Tondello, Gustavo F. and Mora, Alberto and Marczewski, Andrzej and Nacke, Lennart E.},
doi = {10.1016/j.ijhcs.2018.10.002},
issn = {10715819},
journal = {International Journal of Human-Computer Studies},
keywords = {Gameful Design,Gamification,Hexad,User Types},
publisher = {Elsevier},
title = {{Empirical Validation of the Gamification User Types Hexad Scale in English and Spanish}},
url = {https://doi.org/10.1016/j.ijhcs.2018.10.002},
year = {2019},
volume = {127},
pages = {95--111},
}Gamification, the use of game elements in non-game systems, is now established as a relevant research field in human-computer interaction (HCI). Several empirical studies have shown that gameful interventions can increase engagement and generate desired behavioural outcomes in HCI applications. However, some inconclusive results indicate that we need a fuller understanding of the mechanisms and effects of gamification. The Gamification User Types Hexad scale allows us to parse different user motivations in participants’ interactions with gameful applications, which are measured using a self-report questionnaire. Each user type represents a style of interaction with gameful applications, for example, if the interactions are more focused on achievements, socialization, or rewards. Thus, by scoring an individual in each one of the user types of the Hexad model, we can establish a profile of user preferences for gameful interactions. However, we still lack a substantial empirical validation of this scale. Therefore, we set out to validate the factor structure of the scale, in both English and Spanish, by conducting three studies, which also investigated the distribution of the Hexad's user types in the sample. Our findings support the structural validity of the scale, as well as suggesting opportunities for improvement. Furthermore, we demonstrate that some user types are more common than others and that gender and age correlate with a person's user types. Our work contributes to HCI research by further validating the utility of the Gamification User Types Hexad scale, potentially affording researchers a deeper understanding of the mechanisms and effects of gameful interventions.
Year 2018
Alberto Mora,
Gustavo F. Tondello,
Lennart E. Nacke, and Joan Arnedo-Moreno. 2018. Effect of personalized gameful design on student engagement. In
Proceedings of the IEEE Global Engineering Education Conference - EDUCON 2018. Tenerife, Spain. IEEE.
@inproceedings{Mora2018,
abstract = {Many recent studies of gamification applied to higher education have demonstrated a wide range of positive results. However, most of them fail to consider any personalization factor for the student experience, despite recent studies having shown that gameful systems may be more engaging when they are personalized to each user. Therefore, the goal of this work is to investigate if gameful learning experiences can better motivate and engage students if they are personalized. In this way, we present the design and analysis of a personalized gameful learning experience within a Computer Network Design course. The general purpose of this study is to determine whether a personalized gameful learning experience affects both the students' behavioral and emotional engagement. The results of a descriptive analysis reveal that personalization works better than generic approaches in all items regarding the behavioral and emotional engagement of the students, being a promising standpoint to further investigate in subsequent studies.},
author = {Mora, Alberto and Tondello, Gustavo F and Nacke, Lennart E and Arnedo-Moreno, Joan},
booktitle = {Proceedings of the IEEE Global Engineering Education Conference - EDUCON 2018},
keywords = {Hexad,gameful design,gamification,learning,personalization,preferences},
mendeley-groups = {HCI Games Group Publications},
publisher = {IEEE},
title = {{Effect of personalized gameful design on student engagement}},
year = {2018}
}Many recent studies of gamification applied to higher education have demonstrated a wide range of positive results. However, most of them fail to consider any personalization factor for the student experience, despite recent studies having shown that gameful systems may be more engaging when they are personalized to each user. Therefore, the goal of this work is to investigate if gameful learning experiences can better motivate and engage students if they are personalized. In this way, we present the design and analysis of a personalized gameful learning experience within a Computer Network Design course. The general purpose of this study is to determine whether a personalized gameful learning experience affects both the students’ behavioral and emotional engagement. The results of a descriptive analysis reveal that personalization works better than generic approaches in all items regarding the behavioral and emotional engagement of the students, being a promising standpoint to further investigate in subsequent studies.
Year 2017
Gustavo F. Tondello,
Alberto Mora, and
Lennart E. Nacke. 2017. Elements of Gameful Design Emerging from User Preferences. In
Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY '17. Amsterdam, Netherlands. ACM, 129-142.
doi:10.1145/3116595.3116627 @inproceedings{Tondello2017c,
abstract = {Several studies have developed models to explain player preferences. These models have been developed for digital games; however, they have been frequently applied in gameful design (i.e., designing non-game applications with game elements) without empirical validation of their fit to this different context. It is not clear if users experience game elements embedded in applications similarly to how players experience them in games. Consequently, we still lack a conceptual framework of design elements built specifically for a gamification context. To fill this gap, we propose a classification of eight groups of gameful design elements produced from an exploratory factor analysis based on participants' self-reported preferences. We describe the characteristics of the users who are more likely to enjoy each group of design elements in terms of their gender, age, gamification user type, and personality traits. Our main contribution is providing an overview of which design elements work best for what demographic clusters and how we can apply this knowledge to design effective gameful systems.},
address = {Amsterdam, Netherlands},
author = {Tondello, Gustavo F. and Mora, Alberto and Nacke, Lennart E.},
booktitle = {Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY '17},
doi = {10.1145/3116595.3116627},
isbn = {9781450348980},
keywords = {Game Design Elements,Gameful Design,Games User Research,Gamification,Types,User},
publisher = {ACM},
title = {{Elements of Gameful Design Emerging from User Preferences}},
year = {2017}
}Several studies have developed models to explain player preferences. These models have been developed for digital games; however, they have been frequently applied in gameful design (i.e., designing non-game applications with game elements) without empirical validation of their fit to this different context. It is not clear if users experience game elements embedded in applications similarly to how players experience them in games. Consequently, we still lack a conceptual framework of design elements built specifically for a gamification context. To fill this gap, we propose a classification of eight groups of gameful design elements produced from an exploratory factor analysis based on participants’ self-reported preferences. We describe the characteristics of the users who are more likely to enjoy each group of design elements in terms of their gender, age, gamification user type, and personality traits. Our main contribution is providing an overview of which design elements work best for what demographic clusters and how we can apply this knowledge to design effective gameful systems.
