“Thinking About Thinking Aloud”: An Investigation of Think-Aloud Methods in Usability Testing

Alhadreti, Obead; Mayhew, Pam J.

doi:10.14236/ewic/hci2016.101

Cited by 19 publications

(26 citation statements)

References 3 publications

(4 reference statements)

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“…Time per problem can be calculated by dividing the time the evaluator spent on a method by the number of problems identified by that method [2]. The CTA method required 29 minutes per usability problem, whereas the RTA method required 68 minutes per usability problem and the HB method required 45 minutes per usability problem.…”

Section: Table 14 Ta Methods and Time Expendedmentioning

confidence: 99%

“…We considered a number of measures during the process of identifying the usability problem in this study in order to reduce the evaluator effect and to increase the reliability and validity of data [19]. This process is explained in detail in [2]. This subsection presents the results relating to the quantity and quality of usability problem data at the level of individual problems (i.e., problems detected per participant in each condition) and final problems (i.e., the aggregate problems detected in each condition).…”

Section: Usability Problemsmentioning

confidence: 99%

“…The severity levels of individual problems were categorised into one of four types according to their impact on participants' performance: 1) critical, 2) major, 3) minor, and 4) enhancement [9,2,38], as outlined in Table 7.…”

Section: Individual Usability Problems and Severity Levelsmentioning

confidence: 99%

“…The current study investigates the utility and validity of three TA methods, namely concurrent TA, retrospective TA, and a hybrid method, within the context of usability testing. It is part of a larger research project that focuses on the merits and restrictions of different variations of TA protocols for usability testing [1]. The findings of this study will help usability practitioners to make more informed decisions about which TA variant to use in particular contexts.…”

Section: Introductionmentioning

confidence: 99%

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Rethinking Thinking Aloud

Alhadreti

Mayhew

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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This paper presents the results of a study that compared three think-aloud methods: concurrent think-aloud, retrospective think-aloud, and a hybrid method. The three methods were compared through an evaluation of a library website, which involved four points of comparison: task performance, participants' experiences, usability problems discovered, and the cost of employing the methods. The results revealed that the concurrent method outperformed both the retrospective and the hybrid methods in facilitating successful usability testing. It detected higher numbers of usability problems than the retrospective method, and produced output comparable to that of the hybrid method. The method received average to positive ratings from its users, and no reactivity was observed. Lastly, this method required much less time on the evaluator's part than did the other two methods, which involved double the testing and analysis time.

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Section: Table 14 Ta Methods and Time Expendedmentioning

confidence: 99%

Section: Usability Problemsmentioning

confidence: 99%

Section: Individual Usability Problems and Severity Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rethinking Thinking Aloud

Alhadreti

Mayhew

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…They are in charge of welcoming the participant and executing the questionnaires explained in Section 5. The expert practises active listening in a semistructured interview [24,25], and think-aloud methods used in usability testing [26]. • Observer (placed in rear-left seat): In charge of noting every relevant phrase (called a verbatim) pronounced by the driver with their observations and reactions of the driver as a human-factor specialist [27].…”

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confidence: 99%

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Musabini

Nguyen

Rouyer³

et al. 2020

MTI

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The electrification of vehicles is without a doubt one of the milestones of today’s automotive technology. Even though industry actors perceive it as a future standard, acceptance, and adoption of this kind of vehicles by the end user remain a huge challenge. One of the main issues is the range anxiety related to the electric vehicle’s remaining battery level. In the scope of the H2020 ADAS&ME project, we designed and developed an intelligent Human Machine Interface (HMI) to ease acceptance of Electric Vehicle (EV) technology. This HMI is mounted on a fake autonomous vehicle piloted by a hidden joystick (called Wizard of Oz (WoZ) driving). We examined 22 inexperienced EV drivers during a one-hour driving task tailored to generate range anxiety. According to our protocol, once the remaining battery level started to become critical after manual driving, the HMI proposed accurate coping techniques to inform the drivers how to reduce the power consumption of the vehicle. In the following steps of the protocol, the vehicle was totally out of battery, and the drivers had to experience an emergency stop. The first result of this paper was that an intelligent HMI could reduce the range anxiety of the driver by proposing adapted coping strategies (i.e., transmitting how to save energy when the vehicle approaches a traffic light). The second result was that such an HMI and automated driving to a safe spot could reduce the stress of the driver when an emergency stop is necessary.

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An approach to predict the task efficiency of web pages

Saha

Senapati

Maity

2023

Multimed Tools Appl

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Usability is generally considered as a metric to judge the efficacy of any interface. This is also true for the web pages of a website. There are different factors - efficiency, memorability, learnability, errors, and aesthetics play significant roles in order to determine usability. In this work, we proposed a computational model to predict the efficiency with which users can do a particular task on a website. We considered seventeen features of web pages that may affect the efficiency of a task. The statistical significance of these features was tested based on the empirical data collected using twenty websites. For each website, a representative task was identified. Twenty participants completed these tasks using a controlled environment within a group. Task completion times were recorded for feature identification. The one Dimensional ANOVA study reveals sixteen out of the seventeen are statistically significant for efficiency measurement. Using these features, a computational model was developed based on the Support Vector Regression. Experimental results show that our model can predict the efficiency of web pages’ tasks with an accuracy of 90.64%.

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“Thinking About Thinking Aloud”: An Investigation of Think-Aloud Methods in Usability Testing

Cited by 19 publications

References 3 publications

Rethinking Thinking Aloud

Rethinking Thinking Aloud

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

An approach to predict the task efficiency of web pages

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