Player Types and Game Element Preferences: Investigating the Relationship with the Gamification User Types HEXAD Scale

Krath, Jeanine; Korflesch, Harald F. O. von

doi:10.1007/978-3-030-77277-2_18

Cited by 26 publications

(32 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From this fact it is concluded that the personality of the engineering professors may present different characteristics from those of the player profiles specific to the design of the gamified digital learning environment that they consider didactically more effective. These results differ from those of other works carried out with students, in which a significant correlation is identified between the personality characteristics of students and the player profiles best considered for learning, both in engineering teachings [34, 38, 45] and in other areas [23, 29, 36].…”

Section: Discussioncontrasting

confidence: 99%

“…The literature does not show that there is an analogous difference among students. In this sense, it has been shown that matching the player profile of the gamified didactic situation to the student's own player profile benefits their willingness to learn, suggesting that no such difference would exist [12,23,29,36,37]. The results obtained here support the conclusions of previous works, which show that there may be a gap between the personality of the player or the one who designs the gamified situation and the player profile to be exercised [22].…”

Section: Discussionsupporting

confidence: 86%

“…The specialized literature usually works under the hypothesis that the user player profile is the most favorable for learning, when this user is a student, of engineering or any other area [12,23,29,36]. However, there are, as far as could be ascertained, no papers that precisely demonstrate this hypothesis.…”

Section: Player Profiles Of Engineering Students and Engineering Prof...mentioning

confidence: 99%

“…However, there are, as far as could be ascertained, no papers that precisely demonstrate this hypothesis. But it has been shown that matching the player profile to the learner player profile helps to increase motivation and predisposition to learning through gamified situations when the population consists of university students in general [23,29,36,37,54]. This increase is also realized, without significant differences, between engineering students [34,38,45] and other technical teachings, such as Architecture [76].…”

Section: Player Profiles Of Engineering Students and Engineering Prof...mentioning

confidence: 99%

See 3 more Smart Citations

Player profiles for game‐based applications in engineering education

Vergara

Antón‐Sancho

Fernández‐Arias

2022

Comp Applic In Engineering

View full text Add to dashboard Cite

The use of digital learning environments in higher education has led to design methodological strategies, like gamification, adapted to computer‐based learning. The effectiveness of gamification depends on the adaptation of the methodology to the player's profile. Four types of player profiles (Explorer, Killer, Socialized, and Achiever) are established according to the Bartle classification. In this article, quantitative research is carried out on the player profile and the one considered best for learning engineering professors. Sociological and academic aspects that influence player profile choices are also identified. A descriptive and inferential analysis of the answers given by a population of 532 engineering professors —who attended a training course on gamification in digital environments—to a survey designed by the authors was carried out. The results reveal that most engineering professors are Explorers and this is also the player profile considered best for learning, although there is a gap between the own player profile and the choice of the most effective one. The player profile shown here to be the most frequent among engineering professors differs from that attributed to students in the literature, which is Achiever. Gaps by gender, age, teaching experience, and university tenure are also identified. Universities are recommended to strengthen professor training to design personalized game‐based computing environments and to cater to differences due to several sociological and academic aspects.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Discussionsupporting

confidence: 86%

Section: Player Profiles Of Engineering Students and Engineering Prof...mentioning

confidence: 99%

Section: Player Profiles Of Engineering Students and Engineering Prof...mentioning

confidence: 99%

See 2 more Smart Citations

Player profiles for game‐based applications in engineering education

Vergara

Antón‐Sancho

Fernández‐Arias

2022

Comp Applic In Engineering

View full text Add to dashboard Cite

show abstract

“…It was found that learners of the Free Spirit and Player user types seemed more experienced in this application in the ODL environment. Several previous studies (Krath & von Korflesch, 2021;Lopez & Tucker, 2019;Marczewski, 2015;Orji, Nacke, & Di Marco, 2017;Tondello et al, 2016) have investigated which game mechanics are best suited to the motivation of each user type in gamification. The findings of the present study indicate that the gamification mechanics, such as points, leaderboards, and badges, used in the gamified SoruKüp application may appeal more to Players, while the opportunity to add to the questions provided to the learners by the application may appeal to the Free Spirits.…”

Section: Evaluation Of User Types Based On Gender Gamification Experience and Game Mode Among Odl Studentsmentioning

confidence: 99%

Examination of the Hexad User Types and their Relationships with Gender, Game Mode, and Gamification Experience in the Context of Open and Distance Learning

Şenocak

Büyük

Bozkurt

2021

OLJ

View full text Add to dashboard Cite

Gamification, which is defined as the use of game design elements in non-game contexts, is put forward as a solution to low motivation and is suggested for the creation of a sustainable learning ecology in open and distance learning (ODL). The overall purpose of the present study was to examine the distribution of the Hexad gamification user types and the correlations of gamification experience, game mode, and gender with the user types’ scores within the context of an ODL system. The researchers adapted quantitative cross-sectional survey design to seek answers in this study. The Hexad user types of distance learners were determined based on the online “Gamification User Types Hexad Scale.” Findings revealed that the most common user types in the ODL environment were Philanthropists, Achievers, and Free Spirits, followed by Socializers and Players with a lower mean, while the least common user type was Disruptors. Women tended to score higher than men on the Disruptor user type. Achievers, Socializers, Philanthropists, and Players preferred multiplayer game modes, while the game mode had little influence on Free Spirits and Disruptors. Regarding the gamification experience, Players and Free Spirits seemed to have more experience of gamified applications. This study provides insights to learning designers in developing gamified ODL systems to engage the different Hexad user types.

show abstract