Measuring network user psychological experience quality

2015 ◽  
pp. 441-445
Author(s):  
X Wu ◽  
P Wang
Keyword(s):  
Psychological Experience ◽  
Measuring Network ◽  
User Psychological

scholarly journals Modeling User Psychological Experience and Case Study in Online E-learning

2015 ◽  
Vol 10 (6) ◽  
pp. 53 ◽  
Author(s):  
Xiyuan Wu ◽  
Min Liu ◽  
Qinghua Zheng ◽  
Yunqiang Zhang ◽  
Haifei Li
Keyword(s):  
User Experience ◽  
Subjective Experience ◽  
Analytic Hierarchy ◽  
Psychological Experience ◽  
E Learning ◽  
User Psychological ◽  
Hierarchy Process ◽  
Interview Questionnaire

In the post WWW era, the research of e-learning focuses on facilitating intelligent and proactive services for learners. The quality of user experience determines whether e-learning services would be accepted by learners. However, many researchers traditionally focus on the effectiveness of computer systems or the accuracy of algorithms themselves rather than on user-centric psychological experience. How to model and evaluate user experience taking into account user psychological and cognitive properties are challenging research topics. There are some traditional methods typically proposed to evaluate users’ psychological experience, such as interview, questionnaire etc. They are qualitative and easy to conduct but need more time and resource. And they are liable to subjective views. Based on user web log data, the current paper presents a quantitative approach of modeling user psychological experience in the context of intelligent e-learning. The properties and elements, which affect user experience, are analyzed and quantified. The holistic user experience is quantified through the fusion of analytic hierarchy process (AHP) and Delphi methods. A case study, at a university in China, is conducted for diagnosing whether the result of the proposed approach can be uniform with user subjective experience, and indicates that the proposed approach is effective and complements existing user experience research in intelligent e-learning.

The Use of fNIRS for Unique Contributions to Social and Affective Neuroscience

2019 ◽  
Author(s):  
Shannon Burns ◽  
Matthew D. Lieberman
Keyword(s):  
Infrared Spectroscopy ◽  
Social Interaction ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Affective Neuroscience ◽  
Functional Near Infrared Spectroscopy ◽  
Psychological Experience ◽  
The World ◽  
And Behavior ◽  
Meaningful Interaction

Social and affective neuroscience studies the neurophysiological underpinnings of psychological experience and behavior as it relates to the world around us. Yet, most neuroimaging methods require the removal of participants from their rich environment and the restriction of meaningful interaction with stimuli. In this Tools of the Trade article, we explain functional near infrared spectroscopy (fNIRS) as a neuroimaging method that can address these concerns. First, we provide an overview of how fNIRS works and how it compares to other neuroimaging methods common in social and affective neuroscience. Next, we describe fNIRS research that highlights its usefulness to the field – when rich stimuli engagement or environment embedding is needed, studies of social interaction, and examples of how it can help the field become more diverse and generalizable across participant populations. Lastly, this article describes how to use fNIRS for neuroimaging research with points of advice that are particularly relevant to social and affective neuroscience studies.

scholarly journals Measuring network rewiring over time

PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0220295 ◽  
Author(s):  
Yicheol Han ◽  
Stephan J. Goetz
Keyword(s):  
Measuring Network ◽  
Network Rewiring ◽  
Over Time

Research on the strategy of intelligent analysis to improve sports person psychological experience in the era of artificial intelligence

2021 ◽  
pp. 1-11
Author(s):  
Lei Wu ◽  
Juan Wang ◽  
Long Jin ◽  
P. Hemalatha ◽  
R Premalatha
Keyword(s):  
Artificial Intelligence ◽  
Vital Role ◽  
Performance Ratio ◽  
Psychological Experience ◽  
The Public ◽  
Usable Knowledge ◽  
Intelligent Analysis ◽  
Accuracy Ratio ◽  
Depression Level ◽  
Predictive Research

Artificial intelligence (AI) is an excellent potential technology that is evolving day-to-day and a critical avenue for exploration in the world of computer science & engineering. Owing to the vast volume of data and the eventual need to turn this data into usable knowledge and realistic solutions, artificial intelligence approaches and methods have gained substantial prominence in the knowledge economy and community world in general. AI revolutionizes and raises athletics to an entirely different level. Although it is clear that analytics and predictive research have long played a vital role in sports, AI has a massive effect on how games are played, structured, and engaged by the public. Apart from these, AI helps to analyze the mental stability of the athletes. This research proposes the Artificial Intelligence assisted Effective Monitoring System (AIEMS) for the specific intelligent analysis of sports people’s psychological experience. The comparative analysis suggests the best AI strategies for analyzing mental stability using different criteria and resource factors. It is observed that the growth in the present incarnation indicates a promising future concerning AI use in elite athletes. The study ends with the predictive efficiency of particular AI approaches and procedures for further predictive analysis focused on retrospective methods. The experimental results show that the proposed AIEMS model enhances the athlete performance ratio of 98.8%, emotion state prediction of 95.7%, accuracy ratio of 97.3%, perception level of 98.1%, and reduces the anxiety and depression level of 15.4% compared to other existing models.

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