Usability Comparison of Educational Webtoon between Touch Display and VR Device Using AttrakDiff

Kim, Young‐Jin; Yoo, Hoonsik

doi:10.14695/kjsos.2022.25.1.103

Cited by 1 publication

(1 citation statement)

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“…The Hedonic quality is measured, but it also takes into account the practicality and attraction of a product. Using the Kim and Yoo [13]' user experience paradigm, AttrakDiff has a solid theoretical foundation. In the model, hedonism and practicality are seen as the two major attributes of a product.…”

Section: Measurement Of User Experiencementioning

confidence: 99%

Machine Learning and AI Application Behaviour Prediction for User Experience Modelling and Optimization

Neilson

Grigore

2022

JCNS

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The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test the efficacy of the suggested method by implementing the improved design.

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Section: Measurement Of User Experiencementioning

confidence: 99%