Student Player Types in Higher Education—Trial and Clustering Analyses
Bibliographic Data
| ID | 22049009 |
|---|---|
| Authors | Lea C Brandl (0000-0001-6655-6763, University of Lübeck), Andreas Schrader (0000-0001-7926-0611, University of Lübeck) |
| Year | 2024 |
| Volume | 14 |
| Issue | 4 |
| Pages | 352 |
| Publication date | 2024-03-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci14040352 |
| OpenAlex | W4393217982 |
| Language | EN |
| Citations received | 1 |
| References cited | 22 |
In the context of the ongoing transformation in education, new learning methods, as well as new technologies, and therefore new forms of interactions are challenging higher education. This challenge can be addressed through ambient learning management systems that adapt to the student in the presentation and preparation of course materials. For educational games offered in such systems, this means that the game mechanics should be adapted to the student. To narrow down the sum of mechanics to the amount that is relevant for students, player types can be identified. This paper investigates the player types among students at the University of Lübeck. The characteristics of all player types of Marczewski’s Gamification User Types Hexad Framework are considered using a clustering method for the analysis. The result is three profiles with different characteristics of player types. For each of the profiles, mechanics are suggested which can be used for the respective profile. Thus, educational games can be more easily and automatically adapted to player type
Cluster analysis · Higher education · Mathematics education · Political science · Computer Science · Online Learning and Analytics · Psychology · Artificial Intelligence
Intrinsic Motivation, Learning Goals, Engagement, and Achievement in a Diverse High School
The core components of education 4.0 in higher education
The global k-means clustering algorithm
Intrinsic motivation and the process of learning
Serious Games in Higher Education in the Transforming Process to Education 4.0—Systematized Review
Tailored gamification
Who Belongs in the Family
Determining Sample Size
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |