TL;DR: 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 is discussed.
Abstract: IntroductionUsability 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.
TL;DR: The proposed machine learning-based evaluation method could provide an invaluable guidance to the usability experts as to what measures should be improved in order to maximize the system usability for a targeted group of end-users of an eLearning system.
Abstract: The research presented in this paper proposes a new machine learning-based evaluation method for assessing the usability of eLearning systems. Three machine learning methods (support vector machines, neural networks and decision trees) along with multiple linear regression are used to develop prediction models in order to discover the underlying relationship between the overall eLearning system usability and its predictor factors. A subsequent sensitivity analysis is conducted to determine the rank-order importance of the predictors. Using both sensitivity values along with the usability scores, a metric (called severity index) is devised. By applying a Pareto-like analysis, the severity index values are ranked and the most important usability characteristics are identified. The case study results show that the proposed methodology enhances the determination of eLearning system problems by identifying the most pertinent usability factors. The proposed method could provide an invaluable guidance to the usability experts as to what measures should be improved in order to maximize the system usability for a targeted group of end-users of an eLearning system. Usability assessment of eLearning systems is a necessary and challenging problem.Machine learning techniques are effective tools for usability assessments.Pareto-like analysis can help devise severity index values.Sensitivity analysis can help rank the most important usability factors.
TL;DR: In this forum, four experienced usability professionals will address different aspects of formative evaluations: which methods are most effective, how to maximize the chances of effecting change and implementing the usability recommendations, the importance of the usability professional's relationship with the product developer, and the necessity of developing a science of user interface design.
Abstract: Formative evaluation is a collection of "find-and-fix" usability engineering methods, focused on identifying usability problems before a product is completed. In this forum, four experienced usability professionals will address different aspects of formative evaluations:which methods are most effective,how to maximize the chances of effecting change and implementing the usability recommendations,the importance of the usability professional's relationship with the product developer, andthe importance of developing a science of user interface design, to minimize the need for iterative evaluations.
TL;DR: This work describes usability smells of user interaction, i.e., hints of usability problems on running web applications, and the process in which they can be identified by analyzing user interaction events, and describes USF, the tool that implements this process in a fully automated way with minimum setup effort.
Abstract: Usability assessment of web applications continues to be an expensive and often neglected practice. While large companies are able to spare resources for studying and improving usability in their products, smaller businesses often divert theirs in other aspects. To help these cases, researches have devised automatic approaches for user interaction analysis, and there are commercial services that offer automated usability statistics at relatively low fees. However, most existing approaches still fall short in specifying the usability problems concretely enough to identify and suggest solutions. In this work we describe usability smells of user interaction, i.e., hints of usability problems on running web applications, and the process in which they can be identified by analyzing user interaction events. We also describe USF, the tool that implements the process in a fully automated way with minimum setup effort. USF analyses user interaction events on-the-fly, discovers usability smells and reports them together with a concrete solution in terms of a usability refactoring, providing usability advice for deployed web applications.
TL;DR: In this paper, the authors investigate current agroforestry practices, farmers' preferences, tree management and perspectives for agro-forestry technologies in the Central Plateau (moderate altitude) and the Buberuka (high altitude) zones in Rwanda.
Abstract: Uptake and management of agroforestry technologies differs among farms in Rwanda and needs to be documented as a basis for shaping future research and development programs. The objective of this study was to investigate current agroforestry practices, farmers’ preferences, tree management and perspectives for agroforestry technologies. The study consisted of a combination of a formal survey, a participatory tree testing, farmer evaluation and focus group discussions in the Central Plateau (moderate altitude) and the Buberuka (high altitude) agro-ecological zones. A survey and a tree testing exercise with a range of species: (timber species—Eucalyptus urophyla, Grevillea robusta; legume shrubs - Calliandra calothyrsus, Tephrosia vogelii; and fruit species—Persea americana and Citrus sinensis) were carried out in Simbi (Central Plateau) and Kageyo (Buberuka) with farmers from different wealth status who received tree seedlings for planting, managing, and evaluating. Simbi had more tree species farm−1 (4.5) than Kageyo (2.9). Fruit trees occurred most frequently in Simbi. Grevillea robusta, Calliandra calothyrsus and Tephrosia vogelii were mostly established along contours, fruit trees in homefields and Eucalyptus urophyla trees in woodlots. Survival was better on contours for Grevillea robusta (58–100 %) and Calliandra calothyrsus (50–72 %). Tree growth was strongly correlated with the total tree lop biomass in Eucalyptus urophyla (R
2 = 0.69). Grevillea robusta was most preferred in Simbi and Eucalyptus urophyla and Calliandra calothyrsus in Kageyo. The study provided information useful for revising the national agroforestry research and extension agenda and has important implications for other countries in the highlands of Africa.