Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/3923
Title: Gauging user tolerance to interface modifications using Gestalt Principles
Authors: Barrette, Rachelle
Issue Date: 7-Jul-2021
Abstract: Interface updates are more than all too common in the twenty-first century. Constantly, users are bombarded by new updates incurring changes, and new functionality to not one but many of their favourite applications and devices. How changes affect the users can be studied in various settings for numerous reasons. This paper examines a situation where the health and safety of individuals may be at risk; specifically, the patients in the care of nurses and when the input of the medical equipment that the nurses’ use is changed. It is theorized to have unforeseen consequences on the nurse, leading to critical, even fatal errors to the patients in their care. This research aims to outline what interface changes can be made and how these changes affect the user in terms of performance and cognitive load. To accomplish this, an experiment is conducted where participants’ play a simple memory game, and the inputs are changed during gameplay. Simultaneously, performance scores, time, number of errors, and cognitive load are tracked to infer to what degree users are affected by the change. It is hypothesized that the changes tested are not significant enough to elicit a large reaction from the user, and this was found to be true in the performance and cognitive load scores collected, other than a few exceptions. Given this, we can state that user interface modifications based on the Gestalt Principles of Figure-Ground, Proximity, and Closure did not, in this case, elicit a significant effect from the user. Therefore, changes like those tested could be implemented on an interface in a similar scenario and given to users with little concern for negative repercussions. Although, to generalize to a larger context, the experiment would need to be reconducted with more participants to elicit a significant effect from the data.
URI: https://zone.biblio.laurentian.ca/handle/10219/3923
Appears in Collections:Computational Sciences - Master's theses

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