Eye-tracking analysis of user behavior and performance in web search on large and small screens

2014 ◽  
Vol 66 (3) ◽  
pp. 526-544 ◽  
Author(s):  
Jaewon Kim ◽  
Paul Thomas ◽  
Ramesh Sankaranarayana ◽  
Tom Gedeon ◽  
Hwan-Jin Yoon
Keyword(s):  
Eye Tracking ◽  
Web Search ◽  
User Behavior ◽  
And Performance

scholarly journals User Behavior for Neural Network-based Web Search Results Filtering

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Essam F. Alnatsheh
Keyword(s):  
Neural Network ◽  
Web Search ◽  
User Behavior ◽  
Information Filtering ◽  
Web Browsers ◽  
Search Results ◽  
Mouse Pointer ◽  
And Performance ◽  
Relevance Measure ◽  
Relevance Rating

This paper describes methodology and performance of an experimental research on filtering of web search results. Filtering was performed on the basis of predicted relevance of search results derived from users’ implicit feedback. The feedback was obtained from users’ web browsers and consisted of a set of browsing behavioral metrics, including reading time, clicks on links, mouse pointer and wheel movement patterns, bookmarking, sharing, copying, and whether the search was continued after the page was closed. A multi-layer neural network used to infer from the behaviors how much the user was interested in each filtered document. Neural network, therefore, performed deep learning without human supervision. Predicted relevance measure was compared to the explicit feedback. Obtained results of 89% correct relevance rating prediction suggest that selected set of metrics was successful in terms of correctly predict how relevant the web page was for the user involved in the study. More research is recommended for further advances of information filtering methods. 

A Survey on Improving the Web Search Ranking by User Behavior Information

2010 ◽  
Author(s):  
Mohamed Husain ◽  
Amarjeet Singh ◽  
Manoj Kumar ◽  
Rakesh Ranjan
Keyword(s):  
Web Search ◽  
User Behavior ◽  
The Web ◽  
Web Search Ranking

Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

2021 ◽  
Vol 18 (2) ◽  
pp. 1-17
Author(s):  
Shannon P. Devlin ◽  
Jennifer K. Byham ◽  
Sara Lu Riggs
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Performance Measure ◽  
Task Demands ◽  
Unexpected Finding ◽  
Impact Performance ◽  
History Effects ◽  
And Performance ◽  
Workload History ◽  
Over Time

Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments.

Eye-Tracking as a Research Methodology in Educational Context

2019 ◽  
pp. 269-294
Author(s):  
Pedro Rodrigues ◽  
Pedro J. Rosa
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Behavioral Assessment ◽  
Large Body ◽  
Learning Processes ◽  
Self Report ◽  
Clinical Interviews ◽  
Cognitive Activities ◽  
And Performance ◽  
Assessment Procedures

A large body of educational research has been keen to the processes and outcomes of learning. Usually, clinical interviews, self-report measures or behavioral assessment procedures have been the most frequently used techniques to assess cognitive activities during learning. Equally, such approaches often suffer from validity issues. The eye-tracking methodology can be used to overcome some limitations in the study of cognitive processes linked to learning and performance. Therefore, this chapter aims to show how eye movement studies can be used to link ocular metrics to learning processes (e.g. language acquisition, reading, memory). The authors cover a topic that ranges from the paradigm shift in the theories of learning, through eye movement applications and measures, to the contribution of eye tracking methodology to investigate learning processes in educational settings.

Eye-Tracking as a Research Methodology in Educational Context

2017 ◽  
pp. 1-26 ◽  
Author(s):  
Pedro Rodrigues ◽  
Pedro J. Rosa
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Behavioral Assessment ◽  
Large Body ◽  
Learning Processes ◽  
Self Report ◽  
Clinical Interviews ◽  
Cognitive Activities ◽  
And Performance ◽  
Assessment Procedures

A large body of educational research has been keen to the processes and outcomes of learning. Usually, clinical interviews, self-report measures or behavioral assessment procedures have been the most frequently used techniques to assess cognitive activities during learning. Equally, such approaches often suffer from validity issues. The eye-tracking methodology can be used to overcome some limitations in the study of cognitive processes linked to learning and performance. Therefore, this chapter aims to show how eye movement studies can be used to link ocular metrics to learning processes (e.g. language acquisition, reading, memory). The authors cover a topic that ranges from the paradigm shift in the theories of learning, through eye movement applications and measures, to the contribution of eye tracking methodology to investigate learning processes in educational settings.

Impact of bookshelf locations using eye-tracking methodology

2020 ◽  
Vol 41 (8/9) ◽  
pp. 617-629
Author(s):  
Sho Sato ◽  
Yukari Eto ◽  
Kotomi Iwaki ◽  
Tadashi Oyanagi ◽  
Yu Yasuma
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Public Library ◽  
Gaze Behavior ◽  
Content Type ◽  
User Behaviors ◽  
Significant Relationships ◽  
Vertical Location ◽  
The Right ◽  
The Impact

PurposeThis study aimed to understand better the user gaze behavior on bookshelves using eye-tracking technology.Design/methodology/approachAn eye-tracking experiment in a public library with 11 participants was performed. The impact of vertical shelf location of books on the number of times the books are looked at, the impact of horizontal location and the relationship between user behavior and location impact were examined by the findings.FindingsThe results showed that the vertical location of books has a significant impact on the number of times the books are looked at. More than 80% of the time spent looking at bookshelves was spent on books on the top to fourth rows. It was also revealed that the horizontal location of books has a little impact. Books located on the left side of shelves will be looked at significantly more often than those on the right side. No significant relationships between type of user behaviors and location impact were observed.Originality/valueThe study explored the impact of the vertical location of books on time spent looking at bookshelves using eye-tracking methodology. Few published studies do such experiments to address user gaze behavior on bookshelves. The study explored that the vertical location of books has a great impact, and horizontal location has a little impact on user gaze behavior.

Web Search Engine Architectures and their Performance Analysis

2011 ◽  
pp. 491-509
Author(s):  
Xiannong Meng
Keyword(s):  
Performance Analysis ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
General Purpose ◽  
Performance Measurements ◽  
Web Documents ◽  
System Architectures ◽  
Web Search Engine ◽  
And Performance

This chapter surveys various technologies involved in a Web search engine with an emphasis on performance analysis issues. The aspects of a general-purpose search engine covered in this survey include system architectures, information retrieval theories as the basis of Web search, indexing and ranking of Web documents, relevance feedback and machine learning, personalization, and performance measurements. The objectives of the chapter are to review the theories and technologies pertaining to Web search, and help us understand how Web search engines work and how to use the search engines more effectively and efficiently.

scholarly journals 802.11 wireless simulation and anomaly detection using HMM and UBM

SIMULATION ◽  
2020 ◽  
Vol 96 (12) ◽  
pp. 939-956 ◽  
Author(s):  
Anisa Allahdadi ◽  
Ricardo Morla ◽  
Jaime S Cardoso
Keyword(s):  
Anomaly Detection ◽  
User Behavior ◽  
Local Area Networks ◽  
Principal Component ◽  
Access Point ◽  
Local Area ◽  
Wireless Local Area Networks ◽  
Network Simulator ◽  
Detection Analysis ◽  
And Performance

Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

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