English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90029/119959 (75%)
Visitors : 24034433      Online Users : 136
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/80531


    Title: Assessing the Attention Levels of Students by using a Novel Attention Aware System based on Brainwave Signals
    Authors: Chen, Chih-Ming;Wang, Jung-Ying;Yu, Chih-Ming
    陳志銘
    Chen, Chih-Ming
    Contributors: 圖檔所
    Date: 2017-03
    Issue Date: 2016-01-13 11:14:01 (UTC+8)
    Abstract: Rapid progress in information and communication technologies (ICTs) has fueled the popularity of e-learning. However, an e-learning environment is limited in that online instructors cannot monitor immediately whether students remain focus during online autonomous learning. Therefore, this study tries to develop a novel attention aware system (AAS) capable of recognizing students' attention levels accurately based on electroencephalography (EEG) signals, thus having high potential to be applied in providing timely alert for conveying low-attention level feedback to online instructors in an e-learning environment. To construct AAS, attention responses of students and their corresponding EEG signals are gathered based on a continuous performance test (CPT), ie, an attention assessment test. Next, the AAS is constructed by using training and testing data by the NeuroSky brainwave detector and the support vector machine (SVM), a well-known machine learning model. Additionally, based on the discrete wavelet transform (DWT), the collected EEG signals are decomposed into five primary bands (ie, alpha, beta, gamma, theta, and delta) as well as each primary band contains five statistical parameters (including approximate entropy, total variation, energy, skewness, and standard deviation), thus generating 25 potential brainwave features associated with students' attention level for constructing the AAS. An attempt based on genetic algorithm (GA) is also made to enhance the prediction performance of the proposed AAS in terms of identifying students' attention levels. According to GA, the seven most influential features are selected from 25 considered features; parameters of the proposed AAS are optimized as well. Analytical results indicate that the proposed AAS can accurately recognize individual student's attention state as either a high or low level, and the average accuracy rate reaches as high as 89.52%. Moreover, the proposed AAS is integrated with a video lecture tagging system to examine whether the proposed AAS can accurately detect students' low-attention periods while learning about electrical safety in the workplace via a video lecture. Four experiments are designed to assess the prediction performance of the proposed AAS in terms of identifying the periods of video lecture with high- or low-attention levels during learning processes. Analytical results indicate that the proposed AAS can accurately identify the low-attention periods of video lecture generated by students when engaging in a learning activity with video lecture. Meanwhile, the proposed AAS can also accurately identify the low-attention periods of video lecture generated by students to some degree even when students engage in a learning activity by a video lecture with random disturbances. Furthermore, strong negative correlations are found between the students' learning performance (ie, posttest score and progressive score) and the low-attention periods of video lecture identified by the proposed AAS. Results of this study demonstrate that the proposed AAS is effective, capable of assisting online instructors in evaluating students' attention levels to enhance their online learning performance.
    Relation: British Journal of Educational Technology, Volume48, Issue2, Pages 348-369
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1111/bjet.12359
    DOI: 10.1111/bjet.12359
    Appears in Collections:[圖書資訊與檔案學研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    bjet12359.pdf804KbAdobe PDF865View/Open
    Assessing the Attention Levels of Students by using a Novel Attention Aware System based on Brainwave Signals_revised2__post print_.pdfpost-print version517KbAdobe PDF44View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback