脑机交互促进学习有效发生的路径及实验研究——基于在线学习系统中的注意力干预分析
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摘要
脑机交互是一种实现大脑与计算机之间通信的信息交换技术。目前,其在教育中的应用主要有:将脑机接口植入虚拟现实学习环境、人工智能支持下的自适应学习、教育大数据的搜集与处理和智能学习系统等方式。在这些方式里,计算机对学习者在学习过程中的脑电信号进行监测与识别,从而调控学习行为,可促进有效学习。基于这一思路,形成了在在线英语学习系统中植入脑机接口的研究设计,旨在针对大学英语听力课程,感知和调控学习者的注意力,进而实现自适应学习。通过对X大学100名大一学生进行持续1个月的实验研究,结果发现:脑机交互技术在提高学业成绩和注意力、改善学习态度和自我效能感、创设适合不同学习风格的学习路径方面,都取得了较好的效果。这为脑机交互进入教育实验提供了方法上的尝试,也为技术促进学习的有效发生,探索了新的路径。
Brain-computer interactive technology is a kind of information exchange technology to realize communication be-tween brain and computer. In education, brain-computer technology can be mainly implanted into the path of virtual reality learning environment, adaptive learning education supported by artificial intelligence, big data collection and processing, and intelligent learn-ing system. In these pathways, the computer monitors and recognizes the EEG signals of learners in the learning process to regulate learning behaviors, which promotes learners' deep learning. Based on this, the research aiming at college English listening courses implants brain-computer technology into the online English learning system, which mainly senses and regulates learners ' attention,and then realizes the adaptive learning path. Additionally, the experimental study of 100 first-year students in X university lasting for one month achieved good results in improving academic performance and attention, improving learners ' learning attitude and self-efficacy, and creating learning paths for learners with different learning styles. This study provides a methodological approach for the in-troduction of brain-computer interaction technology into educational experiments and a new approach for technology to promote deep learning.
Brain-computer interactive technology is a kind of information exchange technology to realize communication be-tween brain and computer. In education, brain-computer technology can be mainly implanted into the path of virtual reality learning environment, adaptive learning education supported by artificial intelligence, big data collection and processing, and intelligent learn-ing system. In these pathways, the computer monitors and recognizes the EEG signals of learners in the learning process to regulate learning behaviors, which promotes learners' deep learning. Based on this, the research aiming at college English listening courses implants brain-computer technology into the online English learning system, which mainly senses and regulates learners ' attention,and then realizes the adaptive learning path. Additionally, the experimental study of 100 first-year students in X university lasting for one month achieved good results in improving academic performance and attention, improving learners ' learning attitude and self-efficacy, and creating learning paths for learners with different learning styles. This study provides a methodological approach for the in-troduction of brain-computer interaction technology into educational experiments and a new approach for technology to promote deep learning.
引文
[1]姜雷,张海,张岚,等.脑机接口研究之演化及教育应用趋势的知识图谱分析[J].远程教育杂志,2018(4):27-38.
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[4]Wolpaw J R,Birbaumer N,Heetderks W J,McFarland D J,Peckham P H,Schalk G,Donchin E,Quatrano L A,Robinson C J,Vaughan T M.Brain-computer interface technology:A review of the first international meeting[J].IEEE Transactions on Rehabilitation Engineering,2000(2):164-173.
[5]Wilson K,Korn J H.Attention during lectures:Beyond ten minutes[J].Teaching of Psychology,2007(2):85-89.
[6]Lin F R,Kuo C M.Mental effort detection using EEG data in E-learning contexts[J].Computers&Education,2018.
[7]Rebolledo-Mendez G,Dunwell I,Erika A Martínez-Mirón,et al.Assessing NeuroSky’s usability to detect attention levels in an assessment exercise[C]//Human-Computer Interaction.New Trends,13th International Conference on HCI,2009.
[8]Crowley K,Sliney A,Pitt I,et al.Evaluating a brain-computer interface to categorise human emotional response[C]//IEEE International Conference on Advanced Learning Technologies.IEEE,2010.
[9]Hidi S E.A reexamination of the role of attention in learning from text[J].Educational Psychology Review,1995(4):323-350.
[10]Reynolds R E.Selective attention and prose learning:Theoretical and empirical research[J].Educational Psychology Review,1992(4):345-391.
[11]Risko E F,Buchanan D,Medimorec S,et al.Everyday attention:Mind wandering and computer use during lectures[J].Computers&Education,2013(5):275-283.
[12]Wilson K,Korn J H.Topical articles:Attention during lectures-beyond ten minutes[J].Teaching of Psychology,2007(2):85-89.
[13]Rush B R,Hafen M A,Biller D S,et al.The effect of differing audience response system question types on student attention in the veterinary medical classroom[J].Journal of Veterinary Medical Education,2010(2):145-153.
[14]Corno L.The best-laid plans:Modern conceptions of volition and educational research[J].Educational Researcher,1993(2):14-22.
[15]Stytsenko K,Jablonskis E,Prahm C.Evaluation of consumer EEGdevice Emotive EPOC[J].Stytsenko,2011.
[16]Schmidt R W.Attention,awareness,and individual differences in language learning[J].Perspectives on Individual Characteristics&Foreign Language Education,2010:721-737.
[17]Liu G Z,Liu Z H,Hwang G J.Developing multi-dimensional evaluation criteria for English learning websites with university students and professors[J].Computers and Education,2011(1):65-79.
[18]NeuroSky.The MindSet communications protocol[EB/OL].[2019-04-26].http://wearcam.org/ece516/mindset_communications_protocol,2010.
[19]Akcaoglu M.Learning problem-solving through making games[J].E-ducational Technology Research&Development,2014(5):583-600.
[20]Hsu Ting-Chia.Learning English with augmented reality:Do learning styles matter?[J].Computers&Education,2017,106:137-149.
[21]Yang,Kai-Hsiang.Learning behavior and achievement analysis of a digital game-based learning approach integrating mastery learning theory and different feedback models[J].Interactive Learning Environments,2017:1-14.
[22]Felder R M,Silverman L K.Learning and teaching styles in engineering education[J].Engineering Education,1988(7):674-681.
[23]Hwang G J,Yang L H,Wang S Y.A concept map-embedded educational computer game for improving students’learning performance in natural science courses[J].Computers&Education,2013,69:121-130.
[24]Berk R H,Bakeman R,Gottman J M.Observing interaction:An introduction to sequential analysis[J].Technometrics,1992(1):112.
(1)这也是选择大学英语听力课程的原因所在,学习者在学习过程中,可以按照常规状态正常进行听力学习,而不会受到实验研究对其的干扰与心理影响。
[2]胡航,董玉琦.技术促进深度学习:“个性化-合作”学习的理论构建与实证研究[J].远程教育杂志,2017(3):48-61.
[3]胡航.技术促进小学数学深度学习的实证研究[D].长春:东北师范大学,2017.
[4]Wolpaw J R,Birbaumer N,Heetderks W J,McFarland D J,Peckham P H,Schalk G,Donchin E,Quatrano L A,Robinson C J,Vaughan T M.Brain-computer interface technology:A review of the first international meeting[J].IEEE Transactions on Rehabilitation Engineering,2000(2):164-173.
[5]Wilson K,Korn J H.Attention during lectures:Beyond ten minutes[J].Teaching of Psychology,2007(2):85-89.
[6]Lin F R,Kuo C M.Mental effort detection using EEG data in E-learning contexts[J].Computers&Education,2018.
[7]Rebolledo-Mendez G,Dunwell I,Erika A Martínez-Mirón,et al.Assessing NeuroSky’s usability to detect attention levels in an assessment exercise[C]//Human-Computer Interaction.New Trends,13th International Conference on HCI,2009.
[8]Crowley K,Sliney A,Pitt I,et al.Evaluating a brain-computer interface to categorise human emotional response[C]//IEEE International Conference on Advanced Learning Technologies.IEEE,2010.
[9]Hidi S E.A reexamination of the role of attention in learning from text[J].Educational Psychology Review,1995(4):323-350.
[10]Reynolds R E.Selective attention and prose learning:Theoretical and empirical research[J].Educational Psychology Review,1992(4):345-391.
[11]Risko E F,Buchanan D,Medimorec S,et al.Everyday attention:Mind wandering and computer use during lectures[J].Computers&Education,2013(5):275-283.
[12]Wilson K,Korn J H.Topical articles:Attention during lectures-beyond ten minutes[J].Teaching of Psychology,2007(2):85-89.
[13]Rush B R,Hafen M A,Biller D S,et al.The effect of differing audience response system question types on student attention in the veterinary medical classroom[J].Journal of Veterinary Medical Education,2010(2):145-153.
[14]Corno L.The best-laid plans:Modern conceptions of volition and educational research[J].Educational Researcher,1993(2):14-22.
[15]Stytsenko K,Jablonskis E,Prahm C.Evaluation of consumer EEGdevice Emotive EPOC[J].Stytsenko,2011.
[16]Schmidt R W.Attention,awareness,and individual differences in language learning[J].Perspectives on Individual Characteristics&Foreign Language Education,2010:721-737.
[17]Liu G Z,Liu Z H,Hwang G J.Developing multi-dimensional evaluation criteria for English learning websites with university students and professors[J].Computers and Education,2011(1):65-79.
[18]NeuroSky.The MindSet communications protocol[EB/OL].[2019-04-26].http://wearcam.org/ece516/mindset_communications_protocol,2010.
[19]Akcaoglu M.Learning problem-solving through making games[J].E-ducational Technology Research&Development,2014(5):583-600.
[20]Hsu Ting-Chia.Learning English with augmented reality:Do learning styles matter?[J].Computers&Education,2017,106:137-149.
[21]Yang,Kai-Hsiang.Learning behavior and achievement analysis of a digital game-based learning approach integrating mastery learning theory and different feedback models[J].Interactive Learning Environments,2017:1-14.
[22]Felder R M,Silverman L K.Learning and teaching styles in engineering education[J].Engineering Education,1988(7):674-681.
[23]Hwang G J,Yang L H,Wang S Y.A concept map-embedded educational computer game for improving students’learning performance in natural science courses[J].Computers&Education,2013,69:121-130.
[24]Berk R H,Bakeman R,Gottman J M.Observing interaction:An introduction to sequential analysis[J].Technometrics,1992(1):112.
(1)这也是选择大学英语听力课程的原因所在,学习者在学习过程中,可以按照常规状态正常进行听力学习,而不会受到实验研究对其的干扰与心理影响。
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