On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems

Fehaid Alqahtani, Stamos Katsigiannis, N. Ramzan
{"title":"On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems","authors":"Fehaid Alqahtani, Stamos Katsigiannis, N. Ramzan","doi":"10.1109/BIBE.2019.00077","DOIUrl":null,"url":null,"abstract":"A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the test's questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the self-perceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1-score of 75.49% for the personalised single-participant models and a classification F1-score of 64.10% for the global models.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"49 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2019.00077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the test's questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the self-perceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1-score of 75.49% for the personalised single-participant models and a classification F1-score of 64.10% for the global models.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
智能辅导系统中利用心电和肌电信号预测试题难度的研究
传统智能辅导系统(ITS)的一个根本缺点是,与人类导师不同,它们无法理解用户的情绪状态,并相应地调整学习过程。这项工作探索了情感计算技术的潜在用途,为ITS提供了一种情感检测机制。研究人员记录了45个接受计算机英语语言测试的人的心电图(ECG)和肌电图(EMG)信号,并对测试问题的难度提供了反馈。然后,从ECG和EMG信号中提取的特征用于训练机器学习模型,以预测问题的自我感知难度水平。所进行的监督分类实验为该方法在利用学习者情感状态相关信息增强ITS方面的适用性提供了有希望的结果,个性化单参与者模型的平均分类f1得分为75.49%,全局模型的分类f1得分为64.10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Stability Investigation Using Hydrogen Bonds for Different Mutations and Drug Resistance in Non-Small Cell Lung Cancer Patients A Temporal Convolution Network Solution for EEG Motor Imagery Classification Evaluation of a Serious Game Promoting Nutrition and Food Literacy: Experiment Design and Preliminary Results Towards a Robust and Accurate Screening Tool for Dyslexia with Data Augmentation using GANs Exploring Fibrotic Disease Networks to Identify Common Molecular Mechanisms with IPF
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1