Recent advances in extended reality (XR) have enabled seamless access to immersive services. However, user authentication in these environments typically relies on conventional methods, such as PIN entry, which remains vulnerable to unauthorized use. This paper investigates gaze behavior as an implicit second authentication factor in XR. Using a Meta Quest Pro headset, participants performed legitimate and impostor login attempts while their gaze data were recorded. Temporal, spatial, and oculomotor metrics revealed distinctive and reproducible gaze dynamics between user roles. Tree-based machine learning models, particularly XGBoost, reliably distinguished legitimate from impostor sessions under user-independent validation (AUC =.84). Calibrating model thresholds further enabled adaptive balancing between security and usability. These findings demonstrate that gaze dynamics can unobtrusively enhance PIN authentication, introducing an adaptive layer that aligns authentication sensitivity with situational risk and user needs in immersive environments.

Web accessibility refers to the inclusive practice of developing websites so that people with disabilities can perceive, navigate, understand, and interact with the content effectively. Various sets of guidelines have been proposed to ensure website accessibility, most notably the Web Content Accessibility Guidelines (WCAG). Violating such guidelines can have major consequences for the user experience regardless of users' physical or cognitive abilities. However, there is a tendency for developers to ignore website accessibility, especially when it comes to developing sites of increased complexity. Lack of training, lack of managerial and client support, and confusing guidelines are some of the major obstacles to making websites more accessible. This paper presents an educational platform, the Accessibility Academy, which has been designed and developed to inform and educate developers on ensuring web accessibility following a learning by doing approach. The purpose of the platform is to help web developers understand the field of accessibility, how neglecting it can lead to user frustration, and to support them in learning the skills needed to develop accessible websites or audit existing ones. A study involving 79 computer science students found that the Accessibility Academy has a high level of perceived educational experience and usability. Qualitative insights on its strengths and future improvements are also presented.

Today, most higher education institutions use e-learning platforms. Information Architecture (IA), the way that content is organized and interconnected, in such e-learning environments often poses usability problems to learners. This paper presents a case study of designing the IA for the Moodle website of mathematics courses at the University of the South Pacific. The study employed the open card sorting method and involved current staff, postgraduate students, and third-year face-to-face students. A total of 112 participants were involved and 50 cards representing items from the Moodle site were used. Individual sessions along with researchers were arranged, and the participants sorted the cards into groups that were meaningful to them. Each participant provided a label for every category of cards they created. This paper also proposes a new clustering algorithm, the Actual Agreement K-means (AAK-means), for analysis of open card sort data of more than 30 participants. The algorithm combines the Actual Agreement Method (AAM) and K-means. The results were compared to the results of a previous study that used the state of the art method. It was found that the proposed clustering algorithm provides increased insights when analyzing open card sorting datasets compared to existing methods. From the findings, a new Moodle Site IA is proposed.

Open card sorting is a widely used method in HCI for the design of user-centered Information Architectures (IAs). This article proposes a new algorithm that combines the best merge method (BMM), category validity technique (CVT), and multidimensional scaling (MDS) to explore, analyze and visualize open card sort data. A study involving 20 participants and 41 cards explored the IA redesign of a university’s website. The collected data were analyzed using two popular methods employed in the quantitative analysis of open card sort data (i.e., hierarchical clustering, K-means) and the proposed algorithm. It was found that the latter provides increased IA insights compared to the existing methods. Specifically, the proposed algorithm can expose hidden patterns and relationships amongst cards and identify complexities. We also found that the proposed algorithm produces better initial clusters, which have a direct effect on the final clustering quality.

Effective teaching of concepts related to human computer interaction (HCI) requires introduction of the core paradigms as well as the design and evaluation methodologies to the learners. In this paper, we investigate the learning effectiveness of a tool-mediated learning activity to support instruction of established human performance models, namely the Keystroke Level Model (KLM) and Fitts’ law, and their application in web form design practice. The tool, named Keystroke Level Model Form Analyser (KLM-FA), supports learning through exploration by providing step-by-step tracing of the KLM modelling for any web form filling task. KLM-FA can be used in modelling exercises of either online or offline web forms according to different interaction strategies or users’ characteristics. Three pretest-posttest studies involving 52 students in total enrolled in computer science curricula with different education delivery methods (campus-based, blended learning, distance learning) and levels (undergraduate, postgraduate) provide evidence for the effectiveness of the proposed learning activity. In all three studies, it was found that the KLM-FA activity had a significant positive effect on students’ learning gain. In addition, students rated their perceived educational experience and KLM-FA usability as rather high. Content analysis of students’ comments also found that KLM-FA is an educationally valuable and usable tool.

HCI researchers and practitioners are increasingly using physiological data to measure User eXperience (UX) parameters. The dynamic nature of physiological data offers a continuous window for an in-depth understanding of users’ interaction experience. However, in order to be truly informative, physiological signals need to be linked to users’ interaction experience aspects, such as their emotional states, in a systematic and efficient way. Studies have shown that skin conductance is a physiological signal highly associated with stress. The main purpose of this paper is to present the validation study of our proposed stress detection mechanism which is integrated into a software named PhysiOBS. PhysiOBS is an observation analysis tool that can be used in the post-study analysis phase. PhysiOBS uses nonspecific skin conductance responses (NS-SCRs) in order to auto-report time periods that are probably associated with a problematic interaction. PhysiOBS can also combine multiple data sources. Hence, UX evaluators are able to further investigate a recorded session in order to reveal additional interaction flaws. The integrated stress assessment mechanism, which uses four trained classifiers, can be applied in the reported periods (auto/expert-reported) in order to classify them as stress or non-stress. For the purpose of the validation study, 24 users were recruited in order to participate in a lab experiment. Results showed that our stress assessment mechanism supports UX evaluators by accurately identifying stressful regions within an interaction scenario.

Abstract Open card sorting is a well-established method for discovering how people understand and categorize information. This paper addresses the problem of quantitatively analyzing open card sorting data using the K-means algorithm. Although the K-means algorithm is effective, its results are too sensitive to initial category centers. Therefore, many approaches in the literature have focused on determining suitable initial centers. However, this is not always possible, especially when the number of categories is increased. This paper proposes an approach to improve the quality of the solution produced by the K-means for open card sort data analysis. Results show that the proposed initialization approach for K-means outperforms existing initialization methods, such as MaxMin, random initialization and K-means++. The proposed algorithm is applied to a real-world open card sorting dataset, and, unlike existing solutions in the literature, it can be used with any number of participants and cards.

Massive Open Online Courses (MOOCs) are becoming widely spread and recent years have seen a substantial research interest on this topic. MOOCs are mediated by software platforms that need to meet specific non-functional requirements, such as usability. The goal of this paper is to investigate the usability of three popular MOOC platforms: edX, Coursera and Udacity. To this end, a study combining experimental and inquiry usability evaluation methods was conducted. Thirty-one participants were asked to perform five typical tasks in the evaluated MOOC platforms. Study results showed that Coursera was perceived to be significantly more usable compared to both edX and Udacity. In addition, interaction efficiency was significantly higher while using Coursera compared to Udacity. No significant effect of MOOC platform on participants' interaction effectiveness and perceived task difficulty was observed.

This paper presents a taxonomy of design guidelines for robot teleoperation developed from a focused literature review of robot teleoperation. A list of user interface design guidelines was assembled, open card sorting and a focus group were used to classify them, and closed card sorting was employed to validate and further refine the proposed taxonomy. The initially obtained set of 70 guidelines is grouped into eight categories: platform architecture and scalability, error prevention and recovery, visual design, information presentation, robot state awareness, interaction effectiveness and efficiency, robot environment/surroundings awareness, and cognitive factors. Agricultural robots were used as an application case study for implementation and field evaluation. The proposed guideline taxonomy was used heuristically to evaluate the usability of an existing user interface of a teleoperated agricultural robot.

Transitioning from bespoke single-purpose displays to multipurpose public interactive displays entails a number of challenges. One challenge is the development of usable mechanisms that allow users to explore the functionality and services on such displays. This article presents a field trial that employs AutoCardSorter, a tool that uses semantic similarity and clustering algorithms, to automatically group the available applications of a public interactive display into categories based on the developer-provided descriptions of each application. The results demonstrate that the grouping generated by AutoCardSorter improved both performance and self-reported usability measures compared to practitioners' existing grouping. In addition, the study investigated the interplay between grouping and interaction modality (i.e., public display vs. desktop). Results tend to support that grouping affects more the user experience with a multipurpose interactive display, but findings were insignificant. This work provides a way for public displays to dynamically update their offered services without sacrificing usability.

In this paper, we describe an innovative tool that supports the design and evaluation of the information architecture of a Web site. The tool uses Latent Semantic Analysis and hierarchical clustering algorithms to provide optimal information navigation schemes in an automated manner. The proposed, tool-based, approach addresses the problem of reasonable content structuring, which established techniques such as card sorting also address. A real world case study depicted substantial effectiveness gain, without expense in the quality of results. We argue that such an approach could facilitate information-rich applications design, like most Web sites, by reducing time and resources required.

Structuring of the content is an important step in web site design, affecting greatly navigability and the overall user experience. Automated support of this task is the object of this paper. AutoCardSorter, a computational tool that supports clustering of the web pages of a site, is introduced. The proposed tool-based methodology uses semantic similarity measures, such as latent semantic analysis, and hierarchical clustering algorithms, in order to suggest suitable information navigation schemes. In the paper, after introducing AutoCardSorter design and functionality, three independent studies are discussed. The studies, that were conducted in order to validate the proposal, compared the proposed method with the established card-sorting approach, in different domains. It was found that substantial gain in effectiveness was achieved without expense in the quality of results, therefore, reducing the required time and human resources.