Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning

Authors:
raw_author_name: Huiying Chen
raw_affiliation_string: Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
raw_author_name: Ruonan Huang
raw_affiliation_string: Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
raw_author_name: Lihong Zhu
raw_affiliation_string: National Institute of Metrology, Beijing 100029, China
raw_author_name: Shengling Ma
raw_affiliation_string: Section of Hematology-Oncology, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
raw_author_name: Larry Leifer
raw_affiliation_string: Department of Mechanical Engineering, Stanford University, Stanford, CA 94306, USA
raw_author_name: Wei Liu
raw_affiliation_string: Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China

Abstract: This study introduces a novel tool for classifying user needs in user experience (UX) design, specifically tailored for beginners, with potential applications in education. The tool employs the Kano model, text analysis, and deep learning to classify user needs efficiently into four categories. The data for the study were collected through interviews and web crawling, yielding 19 user needs from Generation Z users (born between 1995 and 2009) of LEGO toys (Billund, Denmark). These needs were then categorized into must-be, one-dimensional, attractive, and indifferent needs through a Kano-based questionnaire survey. A dataset of over 3000 online comments was created through preprocessing and annotating, which was used to train and evaluate seven deep learning models. The most effective model, the Recurrent Convolutional Neural Network (RCNN), was employed to develop a graphical text classification tool that accurately outputs the corresponding category and probability of user input text according to the Kano model. A usability test compared the tool’s performance to the traditional affinity diagram method. The tool outperformed the affinity diagram method in six dimensions and outperformed three qualities of the User Experience Questionnaire (UEQ), indicating a superior UX. The tool also demonstrated a lower perceived workload, as measured using the NASA Task Load Index (NASA-TLX), and received a positive Net Promoter Score (NPS) of 23 from the participants. These findings underscore the potential of this tool as a valuable educational resource in UX design courses. It offers students a more efficient and engaging and less burdensome learning experience while seamlessly integrating artificial intelligence into UX design education. This study provides UX design beginners with a practical and intuitive tool, facilitating a deeper understanding of user needs and innovative design strategies.