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K. Trigwell, R. Ellis, Feifei Han (2012)
Relations between students' approaches to learning, experienced emotions and outcomes of learningStudies in Higher Education, 37
A. Krapp, S. Hidi, K. Renninger (1992)
Interest, Learning and Development
Brandon Amos, Bartosz Ludwiczuk, M. Satyanarayanan (2016)
OpenFace: A general-purpose face recognition library with mobile applications
Bürkner (2019)
Ordinal regression models in tutorial Advances in Practices inpsychology Methods Psychological Science, 2
A. Moors, P. Ellsworth, K. Scherer, N. Frijda (2013)
Appraisal Theories of Emotion: State of the Art and Future DevelopmentEmotion Review, 5
P. Bürkner (2017)
brms: An R Package for Bayesian Multilevel Models Using StanJournal of Statistical Software, 080
M. Soleymani (2016)
Detecting Cognitive Appraisals from Facial Expressions for Interest RecognitionArXiv, abs/1609.09761
Krapp (1991)
Interest learning and development in The role of interest in learning and development Erlbaum Hilsdale, 28
S. D’Mello, J. Westlund (2015)
A Review and Meta-Analysis of Multimodal Affect Detection SystemsACM Computing Surveys (CSUR), 47
Peter Lewinski (2016)
UvA-DARE (Digital Academic Repository) Don’t look blank, happy, or sad: patterns of facial expressions of speakers in banks’ YouTube videos predict video’s popularity over time
Peter Lewinski (2015)
Automated facial coding software outperforms people in recognizing neutral faces as neutral from standardized datasetsFrontiers in Psychology, 6
Feldman Barrett (2005)
sensitivity and self - reports of emotional experience of and molcel TheJournal Personality Social Psychology
Peter Lewinski, Tim Uyl, Crystal Butler (2014)
Automated facial coding: validation of basic emotions and FACS AUs in FaceReaderJournal of Neuroscience, Psychology, and Economics, 7
Cheng-Hung Wang, H. Lin (2018)
Constructing an Affective Tutoring System for Designing Course Learning and EvaluationJournal of Educational Computing Research, 55
Affectiva Homepage
Scott Wiltermuth, C. Heath (2009)
Synchrony and CooperationPsychological Science, 20
Bosch (2013)
What emotions do novices experience during their first computer programming learning session Technical report, 9
Chih-Hung Wu, Yueh-Min Huang, Jan-Pan Hwang (2016)
Review of affective computing in education/learning: Trends and challengesBr. J. Educ. Technol., 47
R. Flesch (1948)
A new readability yardstick.The Journal of applied psychology, 32 3
(2018)
Awhite paper by Noldus Information Technology
Alexander Soutschek, André Weinreich, T. Schubert (2018)
Facial electromyography reveals dissociable affective responses in social and non-social cooperationMotivation and Emotion, 42
Nigel Bosch, S. D’Mello, Caitlin Mills (2013)
What Emotions Do Novices Experience during Their First Computer Programming Learning Session?
D. Rogosa, Hilary Saner (1995)
Longitudinal Data Analysis Examples With Random Coefficient ModelsJournal of Educational Statistics, 20
(1978)
Wie verständlich sind unsere Zeitungen?
(2018)
Methodology Note white paper by Technology Technical report Amsterdam
R. Calvo, S. D’Mello (2010)
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their ApplicationsIEEE Transactions on Affective Computing, 1
L. Barrett, R. Adolphs, S. Marsella, Aleix Martinez, S. Pollak (2019)
Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial MovementsPsychological Science in the Public Interest, 20
P. Ekman, Daniel Cordaro (2011)
What is Meant by Calling Emotions BasicEmotion Review, 3
S. Mathôt, Daniel Schreij, J. Theeuwes (2011)
OpenSesame: An open-source, graphical experiment builder for the social sciencesBehavior Research Methods, 44
M. Soleymani, M. Mortillaro (2018)
Behavioral and Physiological Responses to Visual Interest and Appraisals: Multimodal Analysis and Automatic RecognitionFrontiers ICT, 5
M. Heino, Matti Vuorre, N. Hankonen (2018)
Bayesian evaluation of behavior change interventions: a brief introduction and a practical exampleHealth Psychology and Behavioral Medicine, 6
G. Bonanno, D. Keltner (2004)
Brief Report The coherence of emotion systems: Comparing “on‐line” measures of appraisal and facial expressions, and self‐reportCognition and Emotion, 18
P. Zimmermann, Sissel Guttormsen, B. Danuser, P. Gomez (2003)
Affective Computing—A Rationale for Measuring Mood With Mouse and KeyboardInternational Journal of Occupational Safety and Ergonomics, 9
(2012)
The Belfast inducednatural emotiondatabase
S. D’Mello, Arvid Kappas, J. Gratch (2018)
The Affective Computing Approach to Affect MeasurementEmotion Review, 10
K. Scherer (2005)
What are emotions? And how can they be measured?Social Science Information, 44
Y. Suhr (2017)
FaceReader, a Promising Instrument for Measuring Facial Emotion Expression? A Comparison to Facial Electromyography and Self-Reports
L. Barrett, K. Quigley, E. Bliss-Moreau, K. Aronson (2004)
Interoceptive sensitivity and self-reports of emotional experience.Journal of personality and social psychology, 87 5
Nigel Bosch, S. D’Mello, Jaclyn Ocumpaugh, R. Baker, V. Shute (2016)
Using Video to Automatically Detect Learner Affect in Computer-Enabled ClassroomsACM Trans. Interact. Intell. Syst., 6
Hyeon‐Jeong Suk (2006)
Color and Emotion- a study on the affective judgment across media and in relation to visual stimuli
Joseph Grafsgaard, Joseph Wiggins, K. Boyer, E. Wiebe, James Lester (2013)
Automatically Recognizing Facial Expression: Predicting Engagement and Frustration
R. Team (2014)
R: A language and environment for statistical computing.MSOR connections, 1
Ashish Kapoor, Selene Mota, Rosalind Picard (2001)
Towards a Learning Companion that Recognizes Affect
Grzegorz Brodny, A. Kołakowska, A. Landowska, M. Szwoch, W. Szwoch, M. Wróbel (2016)
Comparison of selected off-the-shelf solutions for emotion recognition based on facial expressions2016 9th International Conference on Human System Interactions (HSI)
P. Bürkner, Matti Vuorre (2019)
Ordinal Regression Models in Psychology: A TutorialAdvances in Methods and Practices in Psychological Science, 2
P. Ekman, Wallace Friesen (1976)
Measuring facial movementEnvironmental psychology and nonverbal behavior, 1
Russell Journal Personality Social Psychology, 29
R. Pekrun, Elisabeth Vogl, Krista Muis, G. Sinatra (2017)
Measuring emotions during epistemic activities: the Epistemically-Related Emotion ScalesCognition and Emotion, 31
J. Russell (1980)
A circumplex model of affect.Journal of Personality and Social Psychology, 39
Virginia Tze, L. Daniels, R. Klassen (2016)
Evaluating the Relationship Between Boredom and Academic Outcomes: A Meta-AnalysisEducational Psychology Review, 28
Jason Harley, François Bouchet, R. Azevedo (2013)
Aligning and Comparing Data on Emotions Experienced during Learning with MetaTutor
Grafsgaard (2013)
Automatically recognizing facial expression : predicting engagement and frustration Educational Data Mining
Bethany McDaniel, S. D’Mello, Brandon King, Patrick Chipman, K. Tapp, A. Graesser (2007)
Facial Features for Affective State Detection in Learning Environments, 29
AbstractMeasuring emotions non-intrusively via affective computing provides a promising source of information for adaptive learning and intelligent tutoring systems. Using non-intrusive, simultaneous measures of emotions, such systems could steadily adapt to students emotional states. One drawback, however, is the lack of evidence on how such modern measures of emotions relate to traditional self-reports. The aim of this study was to compare a prominent area of affective computing, facial emotion recognition, to students’ self-reports of interest, boredom, and valence. We analyzed different types of aggregation of the simultaneous facial emotion recognition estimates and compared them to self-reports after reading a text. Analyses of 103 students revealed no relationship between the aggregated facial emotion recognition estimates of the software FaceReader and self-reports. Irrespective of different types of aggregation of the facial emotion recognition estimates, neither the epistemic emotions (i.e., boredom and interest), nor the estimates of valence predicted the respective self-report measure. We conclude that assumptions on the subjective experience of emotions cannot necessarily be transferred to other emotional components, such as estimated by affective computing. We advise to wait for more comprehensive evidence on the predictive validity of facial emotion recognition for learning before relying on it in educational practice.
Open Computer Science – de Gruyter
Published: Jan 1, 2019
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