Building a Better Game: A Theory of Gameful Learning & the Construction of Student Personas with Agency

gameful learning;course design;Education;Social Sciences;Information

Holman, CaitlinMcKay, Timothy A ;

University of Michigan

关键词: gameful learning;    course design;    Education;    Social Sciences;    Information;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/144070/cholma_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Gameful course design creates learning environments that support student motivation, drawing inspiration from well-designed games. This dissertation establishes the theoretical framework on which gameful pedagogy is founded. One key piece of gameful course design is that the instructor creates opportunities for students to make decisions about how they will complete course work. Designing these opportunities requires instructors to reflect on how different types of students are likely to behave, and to decide what grade outcomes can be earned through different routes of action. The field of Human-Computer Interaction uses a design tool called personas to help software developers better understand target users and their respective goals as they build new technologies. This dissertation investigates what choices students made within a gameful course, with the intention of developing a method to systematically construct student personas, based on a combination of behavioral, performance, demographic, and psycho-social data. Such personas would ideally enable instructors to more finely tune gameful course structures to student needs.While this research succeeded in establishing a method to describe the pathways students took through the gameful course studied, it identified very little commonality in students’ choices at the assignment level: the 159 students studied took 158 unique pathways through the core assignment work. This finding speaks to the success of gameful course design in enabling students to have autonomy over their learning experience, but, in addition to a general lack of significant findings between basic student characteristics and assignment choice, did not allow for the creation of data-driven personas that felt cohesive and representative of the students they represented. Three goals for future research into data-driven personas are identified: First, to confirm in a larger and more diverse context that the characteristics examined in this study do not have strong relationships to assignment choice. Second, to re-evaluate whether characteristics like ethnicity and gender need to be included in learner personas at all if they do not offer a better understanding of how similar learners are likely to behave. And third, to investigate whether it is more valuable to iterative course design to focus on how different behavior patterns relate to each and impact each other rather than assuming that the patterns themselves will relate to any particular learner characteristic.