基于传感数据的学习分析应用研究
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摘要
数据是学习分析研究的重要前提。传感技术的发展极大地提升了物理学习空间中的数据采集能力,拓展了学习分析的边界。为把握传感技术的应用现状与趋势,文章在分析传感技术的特征和功能基础上,使用文献研究法探讨了传感数据与学习分析结合的应用价值,构建了基于传感数据的学习分析框架,包括感知学习状态、预测学习表现、干预与反馈学习过程等。基于现有研究成果,将基于传感数据的学习分析应用归纳为学习认知、学习情感和动作技能等三个领域,分析了传感数据采集、模型优化、反馈机制等方面的挑战并提出未来可关注的研究方向。
Data is an important prerequisite for research on learning analytics. The development of sensing technology has greatly improved the ability of data acquisition in physics learning space and expanded the boundary of learning analytics. In order to grasp the application status and trend of sensing technology, based on the analysis of the characteristics and functions of sensing technology, this paper adopts literature research method to explore the application value of combining sensing data with learning analytics. A framework of learning analytics based on sensing data is constructed, including perceiving learning state, predicting learning performance, intervention and feedback learning process, etc. Based on the existing research results, the application of learning analytics based on sensing data is summarized into three fields: learning cognition, learning emotion and motor skill. Finally, this paper analyzes the challenges of sensor data acquisition, model optimization and feedback mechanism, and proposes the future research directions as well.
Data is an important prerequisite for research on learning analytics. The development of sensing technology has greatly improved the ability of data acquisition in physics learning space and expanded the boundary of learning analytics. In order to grasp the application status and trend of sensing technology, based on the analysis of the characteristics and functions of sensing technology, this paper adopts literature research method to explore the application value of combining sensing data with learning analytics. A framework of learning analytics based on sensing data is constructed, including perceiving learning state, predicting learning performance, intervention and feedback learning process, etc. Based on the existing research results, the application of learning analytics based on sensing data is summarized into three fields: learning cognition, learning emotion and motor skill. Finally, this paper analyzes the challenges of sensor data acquisition, model optimization and feedback mechanism, and proposes the future research directions as well.
引文
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[34]BOWMAN L C,THORPE S G,CANNON E N,et al.Action mechanisms for social cognition:behavioral and neural correlates of developing theory of mind[J].Developmental science,2017,20(5):1-16.
[35]SUN C Y,YEH P C.The effects of attention monitoring with EEG biofeedback on university students'attention and self-efficacy:the case of anti-phishing instructional materials[J].Computers&Education,2017,106:73-82.
[36]GHERGULESCU I,MUNTEAN C H.ToTCompute:a novel EEG-based timeontask threshold computation mechanism for engagement modelling and monitoring[J].International journal of artificial intelligence in education,2016,26(3):821-854.
[37]MOSTOW J,CHANG K,NELSON J.Toward exploiting EEG input in a reading tutor[J].International journal of artificial intelligence in education,2013,22(1-2):19-38.
[38]MULDNER K,BURLESON W.Utilizing sensor data to model students’creativity in a digital environment[J].Computers in human behavior,2015(42):127-137.
[39]MITRI D D,KLEMKE R,DRACHSLER H,et al.Towards a real-time feedback system based on analysis of multimodal data[C]//European Conference on Technology Enhanced Learning,2017.
[40]RACHURI K K,MASCOLO C,RENTFROW P J,et al.Mobile sensing at the service of mental well-being:a large-scale longitudinal study[C]//Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:103-112.
[41]CHEN C M,LEE T H.Emotion recognition and communication for reducing second‐language speaking anxiety in a web‐based one‐to‐one synchronous learning environment[J].British journal of educational technology,2011,42(3):417-440.
[42]PRIETO L P,SHARMA K,DILLENBOURG P.Teaching analytics:towards automatic extraction of orchestration graphs using wearable sensors[C]//Proceedings of the Sixth International Conference on Learning Analytics&Knowledge.New York:ACM,2016:148-157.
[43]PIRKL G,HEVESI P,LUKOWICZ P,et al.Any problems?A wearable sensor-based platform for representational learninganalytics[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:Adjunct.New York:ACM,2016:353-356.
[44]ANZULEWICZ A,SOBOTA K,DELAFIELDBUTT J T.Toward the autism motor signature:gesture patterns during smart tablet gameplay identify children with autism[J].Scientific reports,2016,6,31107:1-13.
[45]LU Y,ZHANG S,ZHANG Z,et al.A framework for learning analytics using commodity wearable devices[J].Sensors,2017,17(6),1382:1-25.
[2]ARNOLD K E,PISTILLI M D.Course signals at purdue:using learning analytics to increase student success[C]//Proceedings of the2nd international conference on learning analytics and knowledge.New York:ACM,2012:267-270.
[3]DYCKHOFF A L,ZIELKE D,BULTMANN M,et al.Design and implementation of a learning analytics toolkit for teachers[J].Educational technology&society,2012,15(3):58-76.
[4]刘三女牙,李卿,孙建文,刘智.量化学习:数字化学习发展前瞻[J].教育研究,2016(7):119-126.
[5]徐晓青,赵蔚,刘红霞.混合式学习环境下情绪分析应用与模型研究---基于元分析的视角[J].电化教育研究,2018,39(8):70-77.
[6]张琪,武法提.学习分析中的生物数据表征---眼动与多模态技术应用前瞻[J].电化教育研究,2016,37(9):76-81.
[7]李香勇,左明章,王志锋.学习分析的研究现状与未来展望---2016年学习分析和知识国际会议述评[J].开放教育研究,2017,23(1):46-55.
[8]吴永和,李若晨,王浩楠.学习分析研究的现状与未来发展---2017年学习分析与知识国际会议评析[J].开放教育研究,2017,23(5):42-56.
[9]SWAN M.Sensor mania!The internet of things,wearable computing,objective metrics,and the quantified self 2.0[J].Journal of sensor and actuator networks,2012,1(3):217-253.
[10]THUS H,CHATTI M A,YALCIN E,et al.Mobile learning in context[J].International journal of technology enhanced learning,2012,4(5/6):332-344.
[11]VANLEHN K,ZHANG L,BURLSON W,et al.Can an non-cognitive learning companion increase the effectiveness of a metacognitive learning strategy?[J].IEEE transactions on learning technologies,2017,10(3):277-289.
[12]CATRYSSE L,GIJBELS D,DONCHE V,et al.How are learning strategies reflected in the eyes?Combining results from self‐reports and eye‐tracking[J].British journal of educational psychology,2017,88(5):118-137.
[13]TAELMAN J,VANDEPUT S,SPAEPEN A,et al.Influence of mental stress on heart rate and heart rate variability[C]//4th European Conference of the International Federation for Medical and Biological Engineering.Berlin,Heidelberg:Springer,2009:1366-1369.
[14]AFTANAS L I,GOLOCHEIKINE S A.Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention:high-resolution EEG investigation of meditation[J].Neuroscience letters,2001,310(1):57-60.
[15]STEIL J,MULLER P,SUGANO Y,et al.Forecasting user attention during everyday mobile interactions using device-integrated and wearable sensors[C]//Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services.New York:ACM,2018.
[16]SAEED S,ZYNGIER D.How motivation influences student engagement:a qualitative case study[J].Journal of education&learning,2012,1(2):252-267.
[17]CSIKSZENTMIHALYI M,CSIKSZENTMIHALYI M.Flow:the psychology of optimal experience[J].Design issues,1991,8(1):75-77.
[18]MONKARESI H,BOSCH N,CALVO R,et al.Automated detection of engagement using video-based estimation of facial expressions and heart rate[J].IEEE transactions on affective computing,2017,8(1):15-28.
[19]CAIN B.A review of the mental workload literature[R].Defence Research And Development Toronto(Canada),2007.
[20]DIRICAN A C,GOKTURK M.Psychophysiological measures of human cognitive states applied in human computer interaction[J].Procedia computer science,2011,3(1):1361-1367.
[21]HAAPALAINEN E,KIM S J,FORLIZZI J F,et al.Psycho-physiological measures for assessing cognitive load[C]//Proceedings of the 12th ACM International Conference on Ubiquitous Computing.New York:ACM,2010:301-310.
[22]ROBINSON M,MARSH J,WILLETT R.Play,creativity and digital cultures[J].Routledge,2009.
[23]GOUGH H G.A creative personality scale for the adjective check list[J].Journal of personality&social psychology,1979,37(8):1398-1405.
[24]MAHER M L,FISHER D H.Using AI to evaluate creative designs[C]//DS 73-1 Proceedings of the 2nd International Conference on Design Creativity,2012.
[25]LOPATA J A,NOWICKI E A,JOANISSE M F.Creativity as a distinct trainable mental state:an EEG study of musical improvisation[J].Neuropsychologia,2017(99):246.
[26]YANG D,WEN M,HOWLEY I,et al.Exploring the effect of confusion in discussion forums of massive open online courses[C]//Proceedings of the Second(2015)ACM Conference on Learning@Scale.ACM,2015:121-130.
[27]PEKRUN R,GOETZ T,DANIELS L M,et al.Boredom in achievement settings:exploring control-value antecedents and performance outcomes of a neglected emotion[J].Journal of educational psychology,2010,102(3):531-549.
[28]RUSSELL J A.A circumplex model of affect[J].Journal of personality&social psychology,1980,39(6):1161-1178.
[29]SHEN L,WANG M,SHEN R.Affective e-Learning:using“emotional”data to improve learning in pervasive learning environment[J].Educational technology&society,2009,12(2):176-189.
[30]ISEN A M,DAUBMAN K A,NOWICKI G P.Positive affect facilitates creative problem solving[J].Journal of personality&social psychology,1987,53(6):1122.
[31]CHEN C,JAFARI R,KEHTARNAVAZ N.Improving human action recognition using fusion of depth camera and inertial sensors[J].IEEE transactions on human-machine systems,2015,45(1):51-61.
[32]CHEN C,JAFARI R,KEHTARNAVAZ N.A real-time human action recognition system using depth and inertial sensor fusion[J].IEEE sensors journal,2016,16(3):773-781.
[33]CHEN C,JAFARI R,KEHTARNAVAZ N.A survey of depth and inertial sensor fusion for human action recognition[J].Multimedia tools&applications,2017,76(3):4405-4425.
[34]BOWMAN L C,THORPE S G,CANNON E N,et al.Action mechanisms for social cognition:behavioral and neural correlates of developing theory of mind[J].Developmental science,2017,20(5):1-16.
[35]SUN C Y,YEH P C.The effects of attention monitoring with EEG biofeedback on university students'attention and self-efficacy:the case of anti-phishing instructional materials[J].Computers&Education,2017,106:73-82.
[36]GHERGULESCU I,MUNTEAN C H.ToTCompute:a novel EEG-based timeontask threshold computation mechanism for engagement modelling and monitoring[J].International journal of artificial intelligence in education,2016,26(3):821-854.
[37]MOSTOW J,CHANG K,NELSON J.Toward exploiting EEG input in a reading tutor[J].International journal of artificial intelligence in education,2013,22(1-2):19-38.
[38]MULDNER K,BURLESON W.Utilizing sensor data to model students’creativity in a digital environment[J].Computers in human behavior,2015(42):127-137.
[39]MITRI D D,KLEMKE R,DRACHSLER H,et al.Towards a real-time feedback system based on analysis of multimodal data[C]//European Conference on Technology Enhanced Learning,2017.
[40]RACHURI K K,MASCOLO C,RENTFROW P J,et al.Mobile sensing at the service of mental well-being:a large-scale longitudinal study[C]//Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:103-112.
[41]CHEN C M,LEE T H.Emotion recognition and communication for reducing second‐language speaking anxiety in a web‐based one‐to‐one synchronous learning environment[J].British journal of educational technology,2011,42(3):417-440.
[42]PRIETO L P,SHARMA K,DILLENBOURG P.Teaching analytics:towards automatic extraction of orchestration graphs using wearable sensors[C]//Proceedings of the Sixth International Conference on Learning Analytics&Knowledge.New York:ACM,2016:148-157.
[43]PIRKL G,HEVESI P,LUKOWICZ P,et al.Any problems?A wearable sensor-based platform for representational learninganalytics[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:Adjunct.New York:ACM,2016:353-356.
[44]ANZULEWICZ A,SOBOTA K,DELAFIELDBUTT J T.Toward the autism motor signature:gesture patterns during smart tablet gameplay identify children with autism[J].Scientific reports,2016,6,31107:1-13.
[45]LU Y,ZHANG S,ZHANG Z,et al.A framework for learning analytics using commodity wearable devices[J].Sensors,2017,17(6),1382:1-25.