The current variety of alternative approaches to usability evaluation methods (UEMs) designed to assess and improve usability in software systems is offset by a general lack of understanding of the capabilities and limitations of each. Practitioners need to know which methods are more effective and in what ways and for what purposes. However, UEMs cannot be evaluated and compared reliably because of the lack of standard criteria for comparison. In this article, we present a practical discussion of factors, comparison criteria, and UEM performance measures useful in studies comparing UEMs. In demonstrating the importance of developing appropriate UEM evaluation criteria, we offer operational definitions and possible measures of UEM performance. We highlight specific challenges that researchers and practitioners face in comparing UEMs and provide a point of departure for further discussion and refinement of the principles and techniques used to approach UEM evaluation and comparison.

Criteria For Evaluating Usability Evaluation Methods

Lost in hyperspace: metrics and mental models

This paper presents a new framework for measuring the benefits of IT in construction. The framework is based on the principle that benefits realisation must be managed by: planning for strategic alignment and business-driven exploitation, managing the process of predicting benefits, and by measuring resulting benefits after a system or innovation is implemented. Three distinct types of benefits are identified within the new framework associated with business efficiency, business effectiveness and business performance. 

A key barrier to the more effective exploitation and application of IT in the construction sector has been the lack of investment on a scale comparable with other sectors. A primary reason cited for the low level of investment is the low level of perceived benefits from IT investments amongst construction business managers. 

Many benefits evaluation methods exist and are widely applied in other sectors. Benefits evaluation methods in construction are under-utilised. One reason for this is the lack of fit between these methods, and their associated language, with the peculiarities of the construction sector. The new framework presented in this paper has been derived for specific application to the construction sector. 

The framework has been subjected to testing and application within UK construction organisations. The results of this testing suggest a number of improvements in the benefits realisation process.

/pdf/a-framework-for-measuring-it-innovation-benefits-4dra9l07n8.pdf

A Framework for Measuring IT Innovation Benefits

Publisher Summary
The cognitive walkthrough (CW) is a method for evaluating user interfaces by analyzing the mental processes required of users. To perform a CW an analyst chooses a specific task from the suite of tasks that the interface is intended to support and determines one or more correct sequences of actions for that task. The cognitive walkthrough is used to identify problems with a user interface and to suggest reasons for these problems. The chapter begins by presenting a brief overview of the cognitive walkthrough and locates it in the space of interface evaluation methods. Following this, the chapter discusses cognitive walkthrough method in more detail, with practical instructions for its use. The chapter then describes related methods that have been developed from the cognitive walkthrough idea. It also reviews published accounts of experience with these methods.

Chapter 30 – Cognitive Walkthroughs

Improving evaluation theory through the empirical study of evaluation practice

Evaluating evaluation methods

Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.

https://www.mdpi.com/1424-8220/22/22/8703/pdf?version=1668148850

Identifying Biomarkers for Accurate Detection of Stress

Stress is an established risk factor in the development of addiction and in reinstating drug seeking. Substance use disorder (SUD) is a dangerous epidemic that affects the brain and behavior. Despite this growing epidemic and its subsequent consequences, there are limited management and treatment options, pharmacotherapies and psychosocial treatments available. To this end, there is a need for new and improved personalized devices and treatments for the detection and management of SUD. Based on documented negative effects of stress in SUD, in this paper, our objective was to select a few significant physiological features from a set of 8 features collected by a chest-worn RespiBAN Professional in 15 individuals. We used three machine learning classifiers on these optimal physiological features to detect stress. Our results indicate that best accuracies were achieved when electrodermal activity (EDA), body temperature and chest-worn accelerometer were considered as features for the classification. Challenges, implications and applications were discussed. In the near future, the proposed methods will be replicated in individuals with SUD.

Selection of Optimal Physiological Features for Accurate Detection of Stress

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Evaluating evaluation methods

Identifying Biomarkers for Accurate Detection of Stress

Selection of Optimal Physiological Features for Accurate Detection of Stress