The topic of this paper is a collection of user interface (UI) design patterns for mobile applications. In the paper we present the structure of the patterns collection - the patterns are suggested solutions to problems that are grouped into a set of problem areas that are further grouped into three main problem areas - a structure which is valuable both as an index to identifying patterns to use, and it gives a fairly comprehensive overview of issues when designing user interfaces for mobile applications. To show the breadth of the patterns collection we present six individual problems with connected design patterns in some detail - each coming from different problem areas. They represent important and relevant problems, and are on different levels of abstraction, thus showing how patterns may be used to present problems and solutions on different levels of detail. To show the relevance and usefulness of the patterns collection for usability professionals with a mixed background, we present some relevant findings from a validation of the patterns collection. In addition to verifying the relevance and usefulness of the patterns collection, it also shows both expected and surprising correlations between background and perceived relevance and usefulness. One important finding from the validation is an indication that the patterns collection is best suited for experienced UI developers wanting to start developing mobile UIs. Using a patterns collection for documenting design knowledge and experience has been a mixed experience, so we discuss pros and cons of this. Finally, we present related work and future research.

Design Patterns for User Interface for Mobile Applications.

Research on construction and application of gene network model for form design based on consumer’s preference

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.

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

This paper aims to optimize a pulsed electrophoretic deposition (EPD) process for TiO2 films. This is accomplished by determining the optimal configuration of the coating parameters from a robust optimization perspective. The experimental study uses a composite central design (CCD) with four control factors, i.e., the initial concentration (x1 in g/L), the deposition time (x2 in s), the duty cycle (x3 in %), and the voltage (x4 in V). The process responses that should all be maximized are the photocatalytic efficiency of the thin film (De) and three critical charges, which characterize the adhesion failure, i.e., LC1: the load at which the first cracks occurred; LC2: the load at which the film starts to delaminate at the edge level of the scratch track; and LC3: the load when the damage of the film exceeds 50%. This paper compares the robust optimization design of the EPD process using two methods: the robust design of processes and products using the stochastic frontier (RDPP-SF) and the surface response and desirability function methods. The findings show that the RDPP-SF method is superior to the response surface–desirability method for the process responses De and LC2 because of non-natural sources of variation; however, both methods perform comparably well while analyzing the LC1 and LC3 responses, which are subjected to pure random variability. The parameters setting for the process robust optimization are met in run 25 (x1 = 14 g/L, x2 = 150 s, x3 = 50%, and x4 40 V). 

Static Robust Design Optimization Using the Stochastic Frontier Method: A Case Study of Pulsed EPD Process on TiO2 Films

Identifying users’ diversified needs and designing products that match those needs is important in the era of mass personalization. Smart TV navigation interfaces lack good affective design, and difficulties remain in responding to diversified affective needs. Therefore, an affective design approach that considers the diversity of user needs is presented to optimize the design of smart TV navigation interfaces. First, the laddering interview is conducted to capture the diversified affective needs and multi-layer interface design elements perceived by users. Kansei distance is introduced to characterize diversified needs. Then, single-user relationship models between user perceptions and interface attributes are constructed. Finally, the two-stage interface optimization is performed to obtain common and individual optimization attributes, which are further embodied as parameter-layer optimization solutions through user experiments. This method is capable of capturing the diversified affective needs for smart TV navigation interfaces and achieving differentiated product design. 

Affective Design of Smart TV Navigation Interface Considering the Diversity of User Needs

User experience is a key quality of mobile application design. It involves all aspects of human–computer interactions, and thus the optimisation of user experience is a multi-objective optimisation...

Multi-objective optimisation of user experience in mobile application design via a grey-fuzzy-based Taguchi approach:

Affective responses reflect consumers’ affective needs and have attracted considerable attention in industrial product form design. When designing a product for consumers, designers should take int...

Product form design model based on the robust posterior preference articulation approach

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yongfeng Li

Author Tools

Chat about Author

Papers

Multi-objective optimisation of user experience in mobile application design via a grey-fuzzy-based Taguchi approach:

Product form design model based on the robust posterior preference articulation approach