Utilizing a Digital Escape Room to Improve Students' Grasp and Understanding of Qualitative Research Methods

This theoretical work-in-progress paper proposes a Digital Escape Room (DER) to help undergraduate and postgraduate students grasp qualitative research methods, a subject that many students find difficult and often underestimate in terms of the time it demands. The authors, based at Inland Norway University of Applied Sciences and Roehampton University, argue that the puzzle-by-puzzle structure of escape rooms may mirror the stepwise progression typical of qualitative methods courses, and they ask whether this format can foster a deeper understanding of what qualitative research involves, motivated partly by the challenge of serving a large online student cohort.

The paper grounds its design in literature on gamification, escape rooms, and learning theory. It draws on social cognitive theory, flow theory, the zone of proximal development, and scaffolding, while distinguishing between "game-to-teach" and "game-to-learn" paradigms. Reviewed studies suggest that escape rooms can enhance motivation and 21st-century skills, but also that learners may lose direction without adequate feedback or a game master. The authors draw on prior work recommending a design-thinking process (empathize, define, ideate, prototype, test) and on findings that intrinsic motivation and group efficacy are linked to gameful experiences, while cautioning that game goals should be separated from educational goals so that enjoyment does not displace the intended learning outcomes.

Methodologically, the study is at an early stage. Its planned approach is semi-structured interviews with students about their attitudes and needs, followed by student involvement in prototype development and testing. The only data reported so far come from a single interview conducted in person and via video call, transcribed and manually coded. The paper therefore offers design considerations, such as providing feedback on every answer, possibly supported by AI-driven hints, rather than empirical results, and it leaves its research questions about fit with the step-by-step methodology and improved understanding unanswered pending further evaluation.