Integrating quantitative and qualitative analysis to evaluate digital applications in museums

Analyzing the effectiveness of a digital museum application is generally a complex process. This is due to the interaction between a digital tool capable of a limited number of narration paths and a human being whose multifaceted reactions are uniquely affected by many cultural, psychological, and emotional elements. The classical approaches in the literature can be divided into two main areas: qualitative methods, usually based on interviews at the end of the experience for assessing the cognitive effect of the digital interaction, and quantitative approaches, generally more oriented with the assessment of the subjects’ emotional reactions. Among the quantitative methods, we can mention those dealing with measurements of physiological parameters during the experience, such as ECG, EEG, temperature, etc. This paper proposes a novel approach based on integrating a qualitative process using a two-stage interview, with the quantitative analysis of the user's keystrokes during his digital interaction with the application. The results show how the quantitative component can decouple the interview answers from the interviewer's possible interferences, simultaneously delivering: i) a more transparent assessment of the user's learning experience and ii) mapping of the application's least effective sections. This provides crucial input for improving the design of digital applications for museums.