Personas in Heuristic Evaluation: An Exploratory Study

Introduction Usability evaluation is essential to make sure that software products newly released are easy to use, efficient, and effective to reach goals, and satisfactory to users. For example, when a software company wants to develop and sell a new product, the company needs to evaluate usability of the new product before launching it at a market to avoid the possibility that the new product may contain usability problems, which span from cosmetic problems to severe functional problems. Three widely used methods for usability evaluation are Think Aloud (TA), Heuristic Evaluation (HE) and Cognitive Walkthrough (CW). TA method is commonly employed with a lab-based user testing, while there are variants of TA methods, including thinking out aloud at user's workplace instead of at labs. What we discuss here is the TA method that is combined with a lab-based user testing, in which test users use products while simultaneously and continuously thinking out aloud, and experimenters record users' behaviors and verbal protocols in the laboratory. HE is a usability inspection method, in which a small number of evaluators find usability problems in a user interface design by examining an interface and judging its compliance with well-known usability principles, called heuristics. CW is a theory-based method, in which evaluators evaluate every step necessary to perform a scenario-based task, and look for usability problems that would interfere with learning by exploration. These three methods have their own advantages and disadvantages. For instance, TA method provides good qualitative data from a small number of test users, but laboratory environment may influence test user's behaviors. HE is a cheap, fast and easy-to-use method, while it often finds too specific and low-priority usability problems, including even not real problems. CW helps find mismatches between users' and designers' conceptualization of a task, but it needs extensive knowledge of cognitive psychology and technical details to apply. However, even though these advantages and disadvantages show overall characteristics of three major usability evaluation methods, we cannot compare them quantitatively and see their efficiency clearly. Because one of reasons why so-called discounted methods, such as HE and CW, were developed is to save costs of usability evaluation, cost-related criteria for comparing usability evaluation are meaningful to usability practitioners as well as usability researchers. One of the most disputable issues related to cost of usability evaluation is sample size. That is, how many users or evaluators are needed to achieve a targeted usability evaluation performance, for example, 80% of overall discovery rate? The sample size of usability evaluation is known to depend on an estimate of problem discovery rate across participants. The overall discovery rate is a common quantitative measure that is used to show the effectiveness of a specific usability evaluation method in most of usability evaluation studies. It is also called overall detection rate or thoroughness measure, which is the ratio of 'the sum of unique usability problems detected by all experiment participants' against 'the number of usability problems that exist in the evaluated systems', ranging between 0 and 1. The overall discovery rates were reported more than any other criterion measure in the usability evaluation experiments and also a key component for projecting required sample size for usability evaluation study. Thus, how many test users or evaluators participate in the usability evaluation is a critical issue, considering its cost-effectiveness.

A growing number of mobile applications for health education are being utilized to support different stakeholders, from health professionals to software developers to patients and more general users. There is a lack of a critical evaluation framework to ensure the usability and reliability of these mobile health education applications (MHEAs). Such a framework would facilitate the saving of time and effort for the different user groups. This paper describes a framework for evaluating mobile applications for health education, including a guidance tool to help different stakeholders select the one most suitable for them. The framework is intended to meet the needs and requirements of the different user categories, as well as improving the development of MHEAs through software engineering approaches. A description of the evaluation framework is provided, with its efficient hybrid of selected heuristic evaluation (HE) and usability evaluation (UE) factors. Lastly, an account of the quantitative and qualitative results for the framework applied to the Medscape and other mobile apps is given. This proposed framework – an Evaluation Framework for Mobile Health Education Apps – consists of a hybrid of five metrics selected from a larger set during heuristic and usability evaluation, the choice being based on interviews with patients, software developers and health professionals.

The usability of virtual reality (VR) training applications is crucial for their success, but examining the usability in the early development stages remains challenging. A realistic and plausible solution would be revisiting and reconciling Heuristics Evaluation (HE) methods among the most widely used usability inspection methods in the human-computer interaction (HCI) domain. While research on studying and using HE methods is growing within the VR domain, few studies have considered the novel VR environment challenges new requirements for fitting HE methods to the context and applying them effectively. To this end, we conducted a user study with 14 evaluators using the standard HE methods to complete two HE sessions for a VR training application. We identified five critical challenges that evaluators encountered in the HE process by observing and interviewing them. Based on our findings, we discuss the importance of considering an easy-to-use heuristic set, how we can facilitate the HE procedures in the VR context, and the opportunities for developing HE-supporting tools.

Heuristic Evaluation of a Web-Based Application to Deliver a Home-Based Personalized Physical Exercise Program for Older Adults

Heuristic evaluation (HE) is an inspection-based usability evaluation method in which a number of evaluators, typically 3–5, assess the usability of a system based on a set of usability guidelines. HE was first introduced by Nielsen and Molich and then revised by Nielsen. Since its introduction, HE has gained wide popularity among human-computer interaction (HCI) and user experience (UX) practitioners and is now one of the most common usability evaluation methods. A few years after the introduction of HE, some researchers realized that novice evaluators perform poorly on complex systems. This issue, known as the expertise effect, is one of the major challenges of HE; i.e., the more experienced the evaluator is, the better the results of the evaluation. Consequently, some researchers argue that the results of HE are the product of the evaluator’s experience rather than the method itself. To address this issue, we interviewed 15 usability experts, all of whom had at least four years of experience and who came from both academia and industry. We analyzed their responses and developed a step-by-step protocol.

Despite the increased focus on usability and on the processes and methods used to increase usability, a substantial amount of software is unusable and poorly designed. Much of this is attributable to the lack of cost-effective usability evaluation tools that provide an interaction-based framework for identifying problems. We developed the user action framework and a corresponding evaluation tool, the usability problem inspector (UPI), to help organize usability concepts and issues into a knowledge base. We conducted a comprehensive comparison study to determine if our theory-based framework and tool could be effectively used to find important usability problems in an interface design, relative to two other established inspection methods (heuristic evaluation and cognitive walkthrough). Results showed that the UPI scored higher than heuristic evaluation in terms of thoroughness, validity, and effectiveness and was consistent with cognitive walkthrough for these same measures. We also discuss other potential advantages of the UPI over heuristic evaluation and cognitive walkthrough when applied in practice. Potential applications of this work include a cost-effective alternative or supplement to lab-based formative usability evaluation during any stage of development.

Comparison of two heuristic evaluation methods for evaluating the usability of health information systems

Heuristics evaluation is proved to be an efficient and effective method for inspecting usability of application software and game. However, it is reported in literature that negative usability results may cause failure to the product. In large projects usability evaluation in time consuming and due to time constraints project may fail. There is a need of improving method of conducting heuristic evaluation. Therefore, a motivation is raised to improve the method of conducting heuristic evaluation by automation the process. An automated heuristics evaluation tool empower users to focus on inspecting more usability problems within time constraints and reduce manual efforts. In this study, a playability heuristic evaluation system (PHES) is designed and developed to automate the process of conducting heuristics evaluation for games. To ensure the functionality of this systems, several tests were conducted. The results of this study advocates that with the help of playability heuristic evaluation system (PHES) more playability problems can be identified with in short time.

Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design

Ubiquitous systems are made up of sensors and smart devices that communicate with each other to achieve certain goals. They have specific characteristics, such as transparency, attention, calmness, mobility, and context-awareness. Given that the Human-Computer Interaction (HCI) evaluation methods have been proposed for traditional systems, these characteristics present challenges that may require their adaptation. In this paper, we conducted an exploratory study aimed to compare the suitability of Heuristic Evaluation and the MALTU Model to evaluate the Usability and User eXperience (UX) of ubiquitous systems. For this, we apply both methods for evaluating a healthcare system and perform a comparative analysis. From our findings, MALTU is indicated for UX evaluation, being a low-cost approach when compared to Heuristic Evaluation, which is indicated for usability evaluation. Both methods were able to identify the problems that emerge from ubiquity and can provide better results when applied in aggregate. Furthermore, we provide recommendations to apply both methods for the Usability and UX evaluation of ubiquitous systems.

BackgroundMobile device–based tools to help adolescents practice skills outside of cognitive behavioral therapy (CBT) sessions for treating an anxiety disorder may lead to greater treatment gains.ObjectiveThis study aimed to develop, design, and test the acceptability, learnability, heuristics, and usability of MindClimb, a smartphone-based app for adolescents with anxiety to use between CBT sessions to plan and complete exposure activities using skills (cognitive, relaxation, exposure practice, and reward) learned in treatment.MethodsThis 3-phase study took place from August 2015 to December 2018. In phase 1, the app was designed and developed in consultation with young people and CBT therapists to identify desired functions and content. Feedback was subjected to thematic analysis using a general inductive approach. In phase 2, we conducted 2 high-fidelity testing sessions using the think-aloud approach (acceptability, learnability, usability) and 10-item System Usability Scale with 10 adolescents receiving CBT. The high-fidelity MindClimb app was evaluated by 5 app developers based on Nielsen’s usability heuristics and 5-point severity ranking scale. In phase 3, a total of 8 adolescents and 3 therapists assessed the usability of MindClimb during CBT sessions by recording the frequency of skills practice, use of MindClimb features, satisfaction with the app, and barriers and facilitators to app use during treatment.ResultsFeedback from phase 1 consultations indicated that the app should (1) be responsive to user needs and preferences, (2) be easy to use and navigate, (3) have relevant content to the practice of CBT for anxiety, and (4) be aesthetically appealing. Using this feedback as a guide, a fully functional app prototype for usability testing and heuristic evaluation was developed. In phase 2, think-aloud and usability data resulted in minor revisions to the app, including refinement of exposure activities. The average system usability score was 77 in both testing cycles, indicating acceptable usability. The heuristic evaluation by app developers identified only minor errors (eg, loading speed of app content, with a score of 1 on the severity ranking scale). In phase 3, adolescents considered app features for completing exposure (6.2/10) and relaxation (6.4/10) modestly helpful. Both adolescents (average score 11.3/15, SD 1.6) and therapists (average score 10.0/12, 2.6 SD) reported being satisfied with the app.ConclusionsThe user-centered approach to developing and testing MindClimb resulted in a mobile health app that can be used by adolescents during CBT for anxiety. Evaluation of the use of this app in a clinical practice setting demonstrated that adolescents and therapists generally felt it was helpful for CBT practice outside of therapy sessions. Implementation studies with larger youth samples are necessary to evaluate how to optimize the use of technology in clinical care and examine the impact of the app plus CBT on clinical care processes and patient outcomes.

The lack of standard assessment criteria for reliably comparing usability evaluation methods (UEMs) is an important gap in HCI knowledge. Recently, metrics for assessing thoroughness, validity, and effectiveness of UEMs, based on user data, have been proposed to bridge this gap. This paper reports our findings of applying these proposed metrics in a study that compared heuristic evaluation (HE) to HE-Plus (an extended version of HE). Our experiment showed better overlap among the HE-Plus evaluators than the HE evaluators, demonstrating greater reliability of the method. When evaluation data, from testing the usability of the same website, was used in calculating the UEM performance metrics, HE-Plus was found to be a superior method to HE in all assessment criteria with a 17%, 39%, and 67% improvement in the aspects of thoroughness, validity, and effectiveness, respectively. The paper concludes with a discussion concerning the limitations of the effectiveness of the UEM from which the real users' data was obtained.

The complexity of an electronic medication administration record (eMAR) has been underestimated by most designers in the past. Usability issues, such as poorly designed user application flow in eMAR, are therefore of vital importance, since they can have a negative impact on nursing activities and result in poor outcomes. The purpose of this study was to evaluate the usability of an eMAR application during its development. A usability evaluation was conducted during the development of the eMAR application. Two usability methods were used: a heuristic evaluation complemented by usability testing. Each eMAR application version provided by the vendor was evaluated by 2 hospital pharmacists, who applied the heuristic method. They reviewed the eMAR tasks, detected usability problems and their heuristic violations, and rated the severity of the usability problems. Usability testing was used to assess the final application version by observing how 3 nurses interacted with the application. Thirty-four versions were assessed before the eMAR application was considered usable. During the heuristic evaluation, the usability problems decreased from 46 unique usability problems in version 1 (V1) to 9 in version 34 (V34). In V1, usability problems were categorized into 154 heuristic violations, which decreased to 27 in V34. The average severity rating also decreased from major usability problem (2.96) to no problem (0.23). During usability testing, the 3 nurses did not encounter new usability problems. A thorough heuristic evaluation is a good method for obtaining a usable eMAR application. This evaluation points key areas for improvement and decreases usability problems and their severity.

The rapid proliferation of novel interaction paradigms—including Extended Reality (XR), tangible and natural user interfaces, and haptic systems—together with evolving user experience attributes such as emotional, social, and collaborative interactions, has fundamentally transformed human–computer interaction. These interaction modalities, increasingly integrated with artificial intelligence, expose the limitations of traditional usability and UX evaluation methods developed for conventional graphical user interfaces. Human–AI interaction introduces distinct design challenges related to transparency, explainability, user trust, and shared control, requiring evaluation approaches that shift from technology-centered assessment toward genuinely human-centered analysis.This research proposes a metric-based, AI-powered evaluation platform designed to systematically test and benchmark contemporary user interfaces and interaction modalities. The platform employs enhanced event logging and behavioral coding mechanisms to capture comprehensive multimodal interaction data, including task performance metrics, behavioral patterns, temporal action sequences, gaze trajectories, gesture dynamics, and physiological responses. Machine learning models analyze this multidimensional data to predict standard usability attributes—efficiency, effectiveness, and satisfaction as defined by ISO 9241-11—alongside emerging UX dimensions such as cognitive load, emotional engagement, and social presence.The platform architecture integrates high-performance XR-capable computing hardware with professional-grade graphics processing, multi-monitor visualization, eye-tracking sensors, haptic feedback devices, motion-capture systems, and biometric sensing infrastructure. Data collection is supported by curated training datasets derived from prior usability studies, expert heuristic evaluations, standardized task baselines, and cross-cultural UX assessment data. A custom-engineered, shock-resistant, transportable case with integrated power management and modular connectivity enables rapid deployment across laboratory, field, and organizational contexts, addressing ecological validity limitations inherent in traditional lab-based usability testing.Automated usability evaluation is driven by a hybrid AI framework combining three complementary models. Random Forest ensemble classifiers provide robust prediction across heterogeneous interaction data while offering interpretable feature importance measures to identify key usability determinants. Long Short-Term Memory (LSTM) networks model temporally ordered interaction sequences, enabling detection of behavioral signatures associated with confusion, flow states, hesitation, and error recovery. Support Vector Machines with radial basis function kernels capture complex non-linear relationships in high-dimensional usability data and perform effectively under limited expert-labeled training conditions.The central hypothesis posits that AI-driven automated usability evaluation can achieve accuracy and reliability comparable to expert-conducted heuristic evaluations and cognitive walkthroughs, validated through controlled A/B experiments comparing automated predictions with expert assessments across diverse interface types and user populations.By democratizing access to rigorous UX evaluation through automation, multimodal sensing, and portable deployment, the proposed platform aims to accelerate iterative design cycles for emerging interactive and human–AI systems while maintaining methodological rigor comparable to expert evaluation.

This paper describes the implementation and impact of specific focus on user-centred design (UCD) methods and practices to a mobile application concept development course. The course has been held for two consecutive years at a University of Applied Sciences. This project-based course educates students in teamwork, user study techniques, data interpretation, and scenario-based design. Moreover, by applying usability assessment methods such as heuristic evaluations and usability tests, students learn the basics of usability engineering methods and principles. In addition to outcomes on students’ learning, the course has resulted in several innovative mobile application concepts some of which are in the process of being commercialized. This paper presents student feedback about the applied methods months after the completion of the course. The questionnaire-originating results indicate that students gain knowledge and skills that are very valuable for their future careers in software and service development. Knowledge on UCD is relevant in contemporary mobile application development and an important asset for students to become competitive in job markets. Almost all students have applied the educated methods in their work, and they are willing to promote the UCD methods and framework to their professional peers. The results are further used to discuss and emphasize the importance of UCD education.