Tree testing is a technique used in the early stages of Information Architecture to design the structure and navigation of a web application. It can also be used in later stages to evaluate navigational trees or the navigation map of existing web applications. Although Tree Testing is used frequently, its use and adoption are still largely handmade, using handcraft or spreadsheets to carry it out. This makes it challenging to perform benchmarking and comparative evaluations to assess the quality of navigational content and compare different website designs. Although some tools to assist evaluators exist, they are mainly based on standard metrics, lacking advanced representation and the possibility to compare and benchmark different evaluation projects. This paper presents a systematic solution to improve Tree Testing analysis through advanced visual, statistical, and machine-learning techniques that facilitate decision-making. Our solution includes a tool created to support evaluators and participants, featuring advanced techniques to extract and represent knowledge not included in other existing tools. This facilitates decision-making in the early (analysis) and advanced (evaluation) stages of web-content development. The tool has been evaluated with target users, obtaining successful results related to usefulness, satisfaction, ease of learning, and ease of use.

The utilization of Empathy Maps in User Experience and Design Thinking is becoming commonplace. They can be seen as a valuable tool to help developers better understand the user’s requirements. During the interaction design, it is essential to have a thorough knowledge of what the user wants, which might be difficult to gather or portray to others. In this sense, Empathy Maps visually represent the user’s information, attitude, and behavior. Nevertheless, creating Empathy Maps can become tedious, as this usually requires the involvement of a group of people with expert knowledge, deeply analyzing the information elicited from the user. This is the reason why most Empathy Maps are created manually. A first literature analysis reports that there are still no specific tools to generate Empathy Maps automatically, although there are technologies to take on this challenge. In this paper, we present a tool for automatically generating an initial version of an Empathy Map that can help speed up the capture of explicit knowledge, and then it can be further worked on by the development team. The tool creates the Empathy Map from an audio interview, featuring audio transcription technology and natural language processing. More specifically, this paper includes the architecture, a proof of concept, and user testing, reporting numerical evidence and promising results to corroborate the proposal’s feasibility, reaching an overall accuracy of 86% at classification.

Cognitive impairments in text reading and understanding not only affect individuals with intellectual disabilities but also deaf and deaf-blind ones, the illiterate, and immigrants whose native language is different, among others. Technology to support accessibility to texts is required to help people to understand information. This article addresses an evaluation of the EASIER tool conducted with intellectual disabilities and older people for the identification of complex and unusual words in Spanish texts and proposes them simpler synonyms for the complex word detected. Fifty participants were recruited in collaboration with an organization that works with people with intellectual disabilities. The participants were divided into two groups: Group 1 represented 25 older people, and Group 2 represented 25 people with intellectual disabilities. The evaluation showed that, for complex word identification, the selected texts were more complex in content for people with intellectual disabilities than for older people, which consequently suggests that the proposal had a greater benefit for the intellectually disabled group. Related to the proposal of substitutes for complex words, there is a high number of substitutions accepted in participants with a high school level of education for Group 1 and primary level of education for Group 2. Additional multivariate analysis shows that the system is adequate for people of advanced age.

Monitoring performance indicators has become a main concern for most organizations today. While much attention has been paid to developing economic, bioinformatics, health, and social media dashboards, little or no attention has been devoted to monitoring and forecasting usability indicators. This can be of interest for analyzing the degree of perceived satisfaction and usability of interactive software products designed or just developed, being also useful as general strategic indicators in human-centered organizations. This paper presents an approach including main measures, Key Performance Indicators, trends, and forecasts to deal with usability information over time and produce new knowledge based on historical data. Also, an instance has been implemented, including data obtained from real software evaluations. Target users have evaluated the approach to validate its suitability, obtaining successful usability results that denote the adequacy of the approach presented.

QR codes provide potential opportunities to improve interaction in different areas of application. Even so, this technology has been utilised in punctual and specific standalone applications, being barely applied to broader contexts, such as the educational one, where different scenarios can be considered to take advantage of the same QR-code approach. In this paper, key issues for the development of applications that utilise QR codes to improve sharing and availability of resources are presented, specifically focusing on improving interaction and acceptance. In this way, a holistic approach has been developed where the system is aimed at improving the interaction between educators and students. On the one hand, educators use a web interface to manage educational resources and announcements and automatically generate QR codes. On the other hand, students scan those QR codes using a mobile device, obtaining updated information related to their lectures. This scenario has been evaluated by educators and students, obtaining good results in terms of acceptance, cognitive burden, and overall usability. The approach provides development clues and the evaluation method that can be applied to different contexts, thus representing a generalisable solution.

User Experience (UX) research is intended to find insights and elicit applicable requirements to guide usable designs. Card Sorting is one of the most utilized methods. It is used to uncover the user's mental model and increase the usability of existing products. However, although Card Sorting has been widely utilized, most applications are based on spreadsheets. Furthermore, existing tools are principally intended to obtain qualitative information or customized quantitative outcomes to improve the information architecture. In this paper, a supporting tool based on the Card Sorting method is presented and detailed, including a comprehensive use case showing the main features. The tool implements predictive analysis of results through advanced statistics and machine learning techniques, providing comprehensive reports that enable evaluators and UX researchers to obtain high-level knowledge and important quantitative clues to enhance decision-making. The tool has been evaluated with participants and evaluators, obtaining relevant usability results and feedback.

Abstract The World Wide Web has become a common platform for interactive software development. Most web applications feature custom user interfaces used by millions of people every day. Information architecture addresses the structural design of information to build quality web applications with improved usability of content, navigation, and findability. One of the most frequently utilized information architecture methods is card sorting—an affordable, user-centered approach for eliciting and evaluating categories and navigable items. Card sorting facilitates decision-making during the development process based on users’ mental models of a given application domain. However, although the qualitative analysis of card sorts has become common practice in information architecture, the quantitative analysis of card sorting is less widely applied. The reason for this gap is that quantitative analysis often requires the use of customized techniques to extract meaningful information for decision-making. To facilitate this process and support the structuring of information, we propose a methodology for the quantitative analysis of card-sorting results in this paper. The suggested approach can be systematically applied to provide clues and support for decisions. These might significantly impact the design and, thus, the final quality of the web application. Therefore, the approach includes proper goodness values that enable comparisons among the results of the methods and techniques used and ensure the suitability of the analyses performed. Two publicly available datasets were used to demonstrate the key issues related to the interpretation of card sorting results and the overall suitability and validity of the proposed methodology.

Context In order to ensure usability, it is necessary to schedule activities and methods to be applied throughout different stages of the development process . There exists a substantial number of usability methods to be applied in user-centered software development. However, the application of each usability method largely depends on specific constraints that should be closely considered. Even so, these constraints are not always known beforehand, remaining unidentified or under uncertainty at early stages of the project. Objective This paper presents an approach to automatically recommend 43 usability methods depending on the project's stage and constraints. Our approach deals with uncertainty to recommend usability methods regardless of the completeness of the information available, which makes it suitable for enhancing initial scheduling. Besides, a supporting tool intended to schedule and guide on usability methods is presented in order to systematize the recommendation mechanism. Method To validate our approach, we present two application scenarios demonstrating the suitability of the mechanism, including also an expert analysis to observe the recommendation appropriateness in terms of recommendation gap. Also, a user testing was accomplished to evaluate the usability of the approach with key users. Results A low recommendation gap was observed (<2.5%) and, according to the results obtained in the user testing, high percentage values for usefulness (82.38%) and satisfaction (87.89%) were obtained. The user evaluation also reported high values concerning other dimensions such as ease of use (89.00%) and ease of learning (92.38%). Conclusions Results obtained helped answer main research questions, demonstrating that it is possible to create a mechanism to recommend usability methods according to a software project's constraints, even under uncertainty, and also affirm that it is possible to systemize the recommendations with a scheduling tool being satisfactory for key stakeholders, denoting acceptable levels of recommendation appropriateness, usefulness, and overall usability.

The use of resource description framework-based ontologies as knowledge repositories has become increasingly popular over the past few years. The semantic web has rapidly spread, appearing as a new challenge for knowledge sharing and automatic processing. However, the reality is that the power of the semantic web is still barely used. This is mostly because of the fact that the semantic web is a powerful but complex technology that most end users cannot afford to use for their common problem-solving activities. This has probably made the semantic web to stay in the background of interactive technologies, unlike other new end-user-oriented paradigms (e.g., the so-called Web 2.0 and later approaches) that have very much increased along these years. Nevertheless, the semantic web can be considered as a highly valuable paradigm that has not been, conveniently, exploited yet. In this paper, we propose a semantic environment to exploit semantic interaction by end users in order to help them access semantic information easily. We follow a programming by demonstration approach, where the user navigates and modifies HTML presentation of data and the system, automatically, infers changes to the underlying semantic models. Furthermore, we provide an evaluation of the interaction, including the most important results obtained for the proposed approach.

Current web application development requires highly qualified staff, dealing with an extensive number of architectures and technologies. When these applications incorporate semantic data, the list of skill requirements becomes even larger, leading to a high adoption barrier for the development of semantically enabled Web applications. This paper describes VPOET, a tool focused mainly on two types of users: web designers and web application developers. By using this tool, web designers do not need specific skills in semantic web technologies to create web templates to handle semantic data. Web application developers incorporate those templates into their web applications, by means of a simple mechanism based in HTTP messages. End-users can use these templates through a Google Gadget. As web designers play a key role in the system, an experimental evaluation has been conducted, showing that VPOET provides good usability features for a representative group of web designers in a wide range of competencies in client-side technologies, ranging from amateur HTML developers to professional web designers.

Customization of Web-based applications is often considered a designer skill rather than an end-user need. However, there is an ongoing shift to end-user-centred technology, and even users with poor or no skill in Web-based languages may feel the need to customize Web applications according to their preferences. Although Web authoring environments have an increasing number of features, the challenge of providing end-users with the ability to easily customize entire Web applications still remains unsolved. In this paper, we propose an intelligent approach to customizing Web-based applications. Customizations rules are automatically inferred by the system from changes that users supply as examples. They remain as long-term knowledge that can be applied to support future interactions, thus minimizing the amount of authoring that end-users need to do for this purpose. In order to better understand the implications of the user’s modifications, they are analysed using the logical descriptions of the corresponding Web pages.

Generally speaking, emerging web-based technologies are mostly intended for professional developers.They pay poor attention to users who have no programming abilities but need to customize software applications.At some point, such needs force end-users to act as designers in various aspects of software authoring and development.Every day, more new computing-related professionals attempt to create and modify existing applications in order to customize web-based artifacts that will help them carry out their daily tasks.In general they are domain experts rather than skilled software designers, and new authoring mechanisms are needed in order that they can accomplish their tasks properly.The work we present is an effort to supply end-users with easy mechanisms for authoring web-based applications.To complement this effort, we present a user study showing that it is possible to carry out a trade-off between expressiveness and ease of use in order to provide end-users with authoring facilities.

Authoring dynamic web pages is an inherently difficult task. We present DESK, an interactive authoring tool that allows the customization of dynamic page generation procedures with no a-priori tool-specific skill requirements from authors. Our approach consists of combining Programming By Example (PBE) techniques with an ontology-based representation of knowledge displayed in web pages. DESK acts as a client-side complement of a dynamic web page generation system, PEGASUS, which generates HTML pages from a formally structured domain model and an abstract presentation model. Authorized users can modify the internal presentation model by editing the generated HTML pages with DESK in a WYSIWYG environment. DESK keeps track of all users actions and exploits the explicitly represented domain semantics to enhance the power of PBE techniques

In this paper we present DESK, an authoring tool for the automatic customisation of the front-end of web applications as a result of changes that users perform in dynamically generated HTML pages. Our authoring tool uses domain knowledge and presentation knowledge stored in PEGASUS, an automatic web page generation system for rendering ontology-driven knowledge. DESK automatically detects the differences between original and modified web pages and uses heuristics to infer additional knowledge for modelling the context of each change. DESK uses an explicit user model to identify which user can perform each kind of change to the web presentation.

This paper addresses the genetic design of functional link networks (FLN). FLN are high-order perceptrons (HOP) without hidden units. Despite their linear nature, FLN can capture nonlinear input-output relationships, provided that they are fed with an adequate set of polynomial inputs, which are constructed out of the original input attributes. Given this set, it turns out to be very simple to train the network, as compared with a multilayer perceptron (MLP). However finding the optimal subset of units is a difficult problem because of its nongradient nature and the large number of available units, especially for high degrees. Some constructive growing methods have been proposed to address this issue, Here, we rely on the global search capabilities of a genetic algorithm to scan the space of subsets of polynomial units, which is plagued by a host of local minima. By contrast, the quadratic error function of each individual FLN has only one minimum, which makes fitness evaluation practically noiseless. We find that surprisingly simple FLN compare favorably with other more complex architectures derived by means of constructive and evolutionary algorithms on some UCI benchmark data sets. Moreover, our models are especially amenable to interpretation, due to an incremental approach that penalizes complex architectures and starts with a pool of single-attribute FLN.

Functional link networks (FLNs) are linear neural networks without hidden units whose ability to learn non-linear mappings depends on their being fed with suitable polynomial features. The discrete nature and huge dimension of the search space (subsets of polynomial features) clearly calls for an evolutionary approach. Our evolved FLN architectures (EFLNs) are derived by means of a genetic algorithm (GA) that imposes pressure on both classification performance and architectural simplicity. This gives rise to surprisingly simple and efficient networks such as those found for the Wisconsin breast cancer dataset. Further, it is shown that taking the majority vote of a reduced set of low degree EFLNs improves generalization significantly.