Using emotion recognition technology to assess the effects of different multimedia materials on learning emotion and performance

With the gradual adoption of multimedia technologies in curriculum designs, the need has increased for in-depth studies that explore how different presentation techniques for multimedia materials affect learner emotions and learner performance. This study employed the emWave system, a stress detector for emotional states that was developed by the Institute of HeartMath for measuring changes in learner emotional states when presented with different multimedia materials with the same learning content. By analyzing the collected emotional data and assessment of learning performance, this study explores how different multimedia learning materials affect learning emotions, and ultimately, learning performance. Preliminary results show that the video-based multimedia material generates the best learning performance and most positive emotion among three types of multimedia materials assessed in the study. Moreover, a partial correlation exists between negative learning emotion and learning performance. This study confirms that simultaneously considering pretest score and negative emotion can predict learning performance of learners who use video-based multimedia material for learning. It also finds significant gender difference in learner emotional states while using different multimedia materials for learning: female learners in this study are more easily affected by different multimedia material than male learners.

[1]  Lloyd P. Rieber,et al.  Effects of textual and animated orienting activities and practice on learning from computer-based instruction , 1988 .

[2]  A. Paivio Mental Representations: A Dual Coding Approach , 1986 .

[3]  A. Kring,et al.  Sex differences in emotion: expression, experience, and physiology. , 1998, Journal of personality and social psychology.

[4]  F. Paas,et al.  Cognitive Architecture and Instructional Design , 1998 .

[5]  Jerome Kagan,et al.  Emotions, cognition, and behavior , 1988 .

[6]  Daphne Chang Enhancing Learning Experience With Dynamic Animation , 2002 .

[7]  Ivan Kopeček Emotions and Prosody in Dialogues: An Algebraic Approach Based on User Modelling , 2000 .

[8]  F. Paas,et al.  Cognitive Load Theory and Instructional Design: Recent Developments , 2003 .

[9]  R. Mayer,et al.  Multimedia learning: Are we asking the right questions? , 1997 .

[10]  R. Mayer,et al.  The Role of Interest in Learning From Scientific Text and Illustrations: On the Distinction Between Emotional Interest and Cognitive Interest , 1997 .

[11]  Joseph E. LeDoux,et al.  Emotion, memory and the brain. , 1994, Scientific American.

[12]  Richard E. Mayer,et al.  Unique Contributions of Eye-Tracking Research to the Study of Learning with Graphics , 2010 .

[13]  N. Tractinsky,et al.  What is beautiful is usable , 2000, Interact. Comput..

[14]  R. Mccraty,et al.  The effects of emotions on short-term power spectrum analysis of heart rate variability . , 1995, The American journal of cardiology.

[15]  Richard Lowe,et al.  Animation and learning: selective processing of information in dynamic graphics , 2003 .

[16]  Tai-Hung Lee,et al.  Emotion recognition and communication for reducing second‐language speaking anxiety in a web‐based one‐to‐one synchronous learning environment , 2011 .

[17]  L. Brody,et al.  Gender differences in emotional development: A review of theories and research , 1985 .

[18]  S. Wolfson,et al.  The effects of sound and colour on responses to a computer game , 2000, Interact. Comput..

[19]  R. Kanter,et al.  Another Voice Feminist Perspectives on Social Life and Social Science , 1977 .

[20]  Eunjoon Rachel Um,et al.  The Effect of Positive Emotions on Multimedia Learning , 2007 .

[21]  Tomoya Kurokawa,et al.  A Multi-Modal Emotion-Diagnosis System to Support e-Learning , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[22]  Emmanuel G. Blanchard,et al.  Towards Advanced Learner Modeling: Discussions on Quasi Real-time Adaptation with Physiological Data , 2007, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007).

[23]  J. Sweller,et al.  Reducing cognitive load by mixing auditory and visual presentation modes , 1995 .

[24]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Talcott Parsons,et al.  Social Structure and Personality , 1964 .

[26]  Rob Reilly The Science Behind the Art of Teaching Science: Emotional State and Learning , 2004 .

[27]  A. Erez,et al.  The influence of positive affect on the components of expectancy motivation. , 2002, The Journal of applied psychology.

[28]  H. Ellis,et al.  Irrelevant thoughts, emotional mood states, and cognitive task performance , 1991, Memory & cognition.

[29]  Alain Breuleux,et al.  Multimedia and comprehension: a cognitive study , 1994 .

[30]  L. Steinberg Cognitive and affective development in adolescence , 2005, Trends in Cognitive Sciences.

[31]  A. Hochschild Emotion Work, Feeling Rules, and Social Structure , 1979, American Journal of Sociology.

[32]  David Reiss Video-based multimedia designs: A research study testing learning effectiveness , 2008 .

[33]  Morris Zelditch join,et al.  Family, Socialization and Interaction Process , 1956 .

[34]  Rollin McCraty,et al.  Enhancing Emotional, Social, and Academic Learning With Heart Rhythm Coherence Feedback , 2005 .

[35]  Rosalind W. Picard,et al.  An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion , 2001, Proceedings IEEE International Conference on Advanced Learning Technologies.

[36]  Thomas Wehrle,et al.  Emotion and Facial Expression , 1999, IWAI.

[37]  R. Mccraty,et al.  Cardiac coherence: a new, noninvasive measure of autonomic nervous system order. , 1996, Alternative therapies in health and medicine.

[38]  N. Hari Narayanan,et al.  Communicating Dynamic Behaviors: Are Interactive Multimedia Presentations Better than Static Mixed-Mode Presentations? , 2000, Diagrams.

[39]  T. D. Kemper Predicting emotions from social relations , 1991 .

[40]  T. D. Kemper,et al.  A social interactional theory of emotions , 1978 .

[41]  M. Oaksford,et al.  Mood, reasoning, and central executive processes. , 1996 .

[42]  Qing Zhang,et al.  Analysis of positive and negative emotions in natural scene using brain activity and GIST , 2009, Neurocomputing.

[43]  Helen Pain,et al.  Informing the Detection of the Students' Motivational State: An Empirical Study , 2002, Intelligent Tutoring Systems.

[44]  R. Mayer,et al.  A Split-Attention Effect in Multimedia Learning: Evidence for Dual Processing Systems in Working Memory , 1998 .

[45]  Judith A. Hall,et al.  Gender and emotion , 2008 .

[46]  Wolfgang Schnotz,et al.  Individual and co-operative learning with interactive animated pictures , 1999 .

[47]  Michael J. Blier,et al.  Gender differences in self-rated emotional expressiveness , 1989 .

[48]  Arlie Russell Hochschild,et al.  The Sociology of Feeling and Emotion: Selected Possibilities , 1975 .

[49]  L. F. Barrett,et al.  Handbook of Emotions , 1993 .

[50]  David H. Jonassen,et al.  Instructional designs for microcomputer courseware , 1988 .