Browsing by Author "Huang, Xun-Yi"

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EEG-based Measures of Auditory Saliency in a Complex Context
(2019-01-01)
Huang, Xun-Yi
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Cherng, Fu-Yin
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King, Jung-Tai
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Lin, Wen-Chieh
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資訊工程學系
Auditory saliency is an important mechanism that helps humans extract relevant information from environments. Audio notifications of mobile devices with high saliency can increase users' receptivity, yet overly high saliency could cause annoyance. Accurately measuring auditory saliency of a notification is critical for evaluating its usability. Previous studies adopted behavioral methods. However, their results may not accurately reflect auditory saliency as humans' perception of auditory saliency often involves complicated cognitive processes. Thus, we propose an electroencephalography (EEG)-based approach that can complement behavioral studies to provide a more nuanced analysis of auditory saliency. We evaluated our method by conducting an EEG experiment that measured the mismatch negativity and P3a of the sounds in realistic scenarios. We also conducted a behavioral experiment to link the EEG-based method with the behavioral method. The results suggested that EEG can provide detailed information about how human perceive auditory saliency and complement the behavioral measures.
Exploring Online Learners' Interactive Dynamics by Visually Analyzing Their Time-anchored Comments
(2017-10-01)
Sung, Ching-Ying
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Huang, Xun-Yi
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Shen, Yicong
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Cherng, Fu-Yin
;
Lin, Wen-Chieh
MOOCs (Massive Open Online Courses) are increasingly prevalent as an online educational resource open to everyone and have attracted hundreds of thousands learners enrolling these online courses. At such scale, there is potentially rich information of learners' behaviors embedded in the interactions between learners and videos that may help instructors and content producers adjust the instructions and refine the online courses. However, the lack of tools to visualize information from interactive data, including messages left to the videos at particular timestamps as well as the temporal variations of learners' online participation and perceived experience, has prevented people from gaining more insights from video-watching logs. In this paper, we focus on extracting and visualizing useful information from time-anchored comments that learners left to specific time points of the videos when watching them. Timestamps as a kind of metadata of messages can be useful to recover the interactive dynamics of learners occurring around the videos. Therefore, we present a visualization system to analyze and categorize time-anchored comments based on topics and content types. Our system integrates visualization methods of temporal text data, namely ToPIN and ThemeRiver, which can help people understand the quality and quantity of online learners' feedback and their states of learning. To evaluate the proposed system, we visualized time-anchored commenting data from two online course videos, and conducted two user studies participated by course instructors and third-party educational evaluators. The results validate the usefulness of the approach and show how the quantitative and qualitative visualizations can be used to gain interesting insights around learners' online learning behaviors.