What does physiological synchrony reveal about metacognitive experiences and group performance?
暂无分享,去创建一个
Muhterem Dindar | Sanna Järvelä | Eetu Haataja | S. Järvelä | M. Dindar | Eetu Haataja | Sanna Järvelä
[1] A. Roepstorff,et al. Building trust: Heart rate synchrony and arousal during joint action increased by public goods game , 2015, Physiology & Behavior.
[2] Goodman,et al. The Interactive Effects of Task and External Feedback on Practice Performance and Learning. , 1998, Organizational behavior and human decision processes.
[3] Piet Desmet,et al. Combining physiological data and subjective measurements to investigate cognitive load during complex learning , 2019, Frontline Learning Research.
[4] Dragan Gasevic,et al. Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning , 2020, Br. J. Educ. Technol..
[5] P. Yetton,et al. The relationships among group size, member ability, social decision schemes, and performance , 1983 .
[6] Peggy P. Chen,et al. Self-regulation, motivation, and math achievement in middle school: variations across grade level and math context. , 2009, Journal of school psychology.
[7] Mary L. Still,et al. The role of autonomic arousal in feelings of familiarity , 2008, Consciousness and Cognition.
[8] Travis J. Wiltshire,et al. Multiscale movement coordination dynamics in collaborative team problem solving. , 2019, Applied ergonomics.
[9] Michail N. Giannakos,et al. Multimodal data as a means to understand the learning experience , 2019, Int. J. Inf. Manag..
[10] P. Kirschner,et al. Social and Cognitive Factors Driving Teamwork in Collaborative Learning Environments , 2006 .
[11] A. Bandura,et al. The anatomy of stages of change. , 1997, American journal of health promotion : AJHP.
[12] Michail N. Giannakos,et al. Fitbit for learning: Towards capturing the learning experience using wearable sensing , 2020, Int. J. Hum. Comput. Stud..
[13] Lynn S. Fuchs,et al. Effects of Workgroup Structure and Size on Student Productivity during Collaborative Work on Complex Tasks , 2000, The Elementary School Journal.
[14] Guoying Zhao,et al. Leaders and Followers Identified by Emotional Mimicry During Collaborative Learning: A Facial Expression Recognition Study on Emotional Valence , 2020, IEEE Transactions on Affective Computing.
[15] Kai Puolamäki,et al. Biosignals reflect pair-dynamics in collaborative work: EDA and ECG study of pair-programming in a classroom environment , 2018, Scientific Reports.
[16] R. Simons,et al. To err is autonomic: error-related brain potentials, ANS activity, and post-error compensatory behavior. , 2003, Psychophysiology.
[17] D. DeSteno,et al. The rhythm of joint action: Synchrony promotes cooperative ability , 2010 .
[18] Bertrand Schneider,et al. Using Physiological Synchrony as an Indicator of Collaboration Quality, Task Performance and Learning , 2018, AIED.
[19] Rosemary Luckin,et al. The NISPI framework: Analysing collaborative problem-solving from students' physical interactions , 2018, Comput. Educ..
[20] Sebastian Wallot,et al. Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action , 2016, Front. Psychol..
[21] A. Efklides. Metacognition and Affect: What Can Metacognitive Experiences Tell Us about the Learning Process?. , 2006 .
[22] Angela Stewart,et al. Towards a generalized competency model of collaborative problem solving , 2020, Comput. Educ..
[23] G. Ben-Shakhar,et al. Standardization within individuals: a simple method to neutralize individual differences in skin conductance. , 1985, Psychophysiology.
[24] John Sweller,et al. From Cognitive Load Theory to Collaborative Cognitive Load Theory , 2018, International Journal of Computer-Supported Collaborative Learning.
[25] Bertrand Schneider,et al. Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups , 2018, Int. J. Comput. Support. Collab. Learn..
[26] R GrahamCharles,et al. Measuring student engagement in technology-mediated learning , 2015 .
[27] Sylvia D. Kreibig,et al. Goal relevance and goal conduciveness appraisals lead to differential autonomic reactivity in emotional responding to performance feedback , 2012, Biological Psychology.
[28] Mary E. Webb,et al. Social Regulation of Learning During Collaborative Inquiry Learning in Science: How does it emerge and what are its functions? , 2015 .
[29] Mary Ainley,et al. Students, tasks and emotions: Identifying the contribution of emotions to students' reading of popular culture and popular science texts , 2005 .
[30] Päivi Häkkinen,et al. Effect of Ca2+, cyclic GMP, and cyclic AMP added to artificial solution perfusing lingual artery on frog gustatory nerve responses , 1982, The Journal of general physiology.
[31] Adam W. Hoover,et al. Physiological compliance and team performance. , 2009, Applied ergonomics.
[32] Judy Kay,et al. Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data , 2019, Hum. Comput. Interact..
[33] Aimee A. Callender,et al. Improving metacognition in the classroom through instruction, training, and feedback , 2016 .
[34] Laura Salerno,et al. The Italian Version of the Inventory of Interpersonal Problems (IIP-32): Psychometric Properties and Factor Structure in Clinical and Non-clinical Groups , 2018, Front. Psychol..
[35] Päivi Häkkinen,et al. Preparing teacher-students for twenty-first-century learning practices (PREP 21): a framework for enhancing collaborative problem-solving and strategic learning skills , 2017 .
[36] Kshitij Sharma,et al. Modelling Learners' Behaviour: A Novel Approach Using GARCH with Multimodal Data , 2019, EC-TEL.
[37] Maria Bannert,et al. e-Research and learning theory: What do sequence and process mining methods contribute? , 2014, Br. J. Educ. Technol..
[38] James D. Hess,et al. Diagnosing harmful collinearity in moderated regressions: A roadmap , 2016 .
[39] Philip H. Winne,et al. Paradigmatic Dimensions of Instrumentation and Analytic Methods in Research on Self-Regulated Learning , 2019, Comput. Hum. Behav..
[40] Sebastian Wallot,et al. Beyond Synchrony: Joint Action in a Complex Production Task Reveals Beneficial Effects of Decreased Interpersonal Synchrony , 2016, PloS one.
[41] K. Shockley,et al. Mutual interpersonal postural constraints are involved in cooperative conversation. , 2003, Journal of experimental psychology. Human perception and performance.
[42] Allyson F. Hadwin,et al. Calibration in goal setting: Examining the nature of judgments of confidence , 2013 .
[43] C. Marci,et al. Physiologic Correlates of Perceived Therapist Empathy and Social-Emotional Process During Psychotherapy , 2007, The Journal of nervous and mental disease.
[44] A. Efklides. Interactions of Metacognition With Motivation and Affect in Self-Regulated Learning: The MASRL Model , 2011 .
[45] Sanna Järvelä,et al. Examining shared monitoring in collaborative learning: A case of a recurrence quantification analysis approach , 2019, Comput. Hum. Behav..
[46] Korbinian Strimmer,et al. Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks , 2008, J. Mach. Learn. Res..
[47] Christian Mühl,et al. Connecting Brains and Bodies: Applying Physiological Computing to Support Social Interaction , 2015, Interact. Comput..
[48] Deanne N. Den Hartog,et al. Consequences of Positive and Negative Feedback: The Impact on Emotions and Extra-Role Behaviors , 2009 .
[49] S. Russell,et al. Physio-behavioral coupling in a cooperative team task: contributors and relations. , 2014, Journal of experimental psychology. Human perception and performance.
[50] Adrian Burns,et al. SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.
[51] Christian Burgers,et al. How feedback boosts motivation and play in a brain-training game , 2015, Comput. Hum. Behav..
[52] Rick Dale,et al. Behavior Matching in Multimodal Communication Is Synchronized , 2012, Cogn. Sci..
[53] Muhterem Dindar,et al. Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning , 2019, Education and Information Technologies.
[54] M. Croon,et al. Predicting group-level outcome variables from variables measured at the individual level: a latent variable multilevel model. , 2007, Psychological methods.
[55] Charles R. Graham,et al. Measuring student engagement in technology-mediated learning: A review , 2015, Comput. Educ..
[56] F. Paas. Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. , 1992 .
[57] Markku Niemivirta,et al. Predictors and outcomes of situational interest during a science learning task , 2013 .
[58] A. Pecchinenda. The Affective Significance of Skin Conductance Activity During a Difficult Problem-solving Task , 1996 .
[59] R. Henning,et al. Social psychophysiological compliance in a four-person research team. , 2009, Applied ergonomics.
[60] Birgit Spinath,et al. Why does intrinsic motivation decline following negative feedback? The mediating role of ability self-concept and its moderation by goal orientations , 2016 .
[61] J. Eskildsen,et al. Physiological evidence of interpersonal dynamics in a cooperative production task , 2016, Physiology & Behavior.
[62] Tamara van Gog,et al. Improving self-monitoring and self-regulation: From cognitive psychology to the classroom , 2012 .
[63] Laura M. Stapleton,et al. On the Unnecessary Ubiquity of Hierarchical Linear Modeling , 2017, Psychological methods.
[64] Jie Xu,et al. Shared Experiences of Technology and Trust: An Experimental Study of Physiological Compliance Between Active and Passive Users in Technology-Mediated Collaborative Encounters , 2014, IEEE Transactions on Human-Machine Systems.
[65] Muhterem Dindar,et al. Interplay of metacognitive experiences and performance in collaborative problem solving , 2020, Comput. Educ..
[66] J. Funke,et al. Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning , 2011 .
[67] Asher Koriat,et al. The intricate relationships between monitoring and control in metacognition: lessons for the cause-and-effect relation between subjective experience and behavior. , 2006, Journal of experimental psychology. General.
[68] M. C. Gil,et al. Social-physiological compliance as a determinant of team performance. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[69] Elena Di Lascio,et al. Unobtrusive Assessment of Students' Emotional Engagement during Lectures Using Electrodermal Activity Sensors , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[70] A. Efklides,et al. Individual differences in feelings of difficulty: The case of school mathematics , 1998 .
[71] Samuel Greiff,et al. Collaborative Problem Solving Measures in the Programme for International Student Assessment (PISA) , 2017 .
[72] Sebastian Wallot,et al. Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R , 2018, Front. Psychol..
[73] Ye Han,et al. The development of student feedback literacy: the influences of teacher feedback on peer feedback , 2019, Assessment & Evaluation in Higher Education.
[74] S. Järvelä,et al. New Frontiers: Regulating Learning in CSCL , 2013 .
[75] Sidney K. D'Mello,et al. Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving , 2019, Cogn. Sci..
[76] M. Vauras,et al. Social Interaction - What Can It Tell Us about Metacognition and Coregulation in Learning? , 2005 .
[77] Carlos Cornejo,et al. Interpersonal Coordination: Methods, Achievements, and Challenges , 2017, Front. Psychol..
[78] M. Benedek,et al. A continuous measure of phasic electrodermal activity , 2010, Journal of Neuroscience Methods.
[79] M. Vauras,et al. Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes , 2011 .
[80] Reinhard Pekrun,et al. The power of anticipated feedback: Effects on students' achievement goals and achievement emotions , 2014 .
[81] Ronny Scherer,et al. Revealing the processes of students' interaction with a novel collaborative problem solving task: An in-depth analysis of think-aloud protocols , 2017, Comput. Hum. Behav..