Abstract
This paper details the design, development and evaluation of an affective tutoring system (ATS)—an e-learning system that detects and responds to the emotional states of the learner. Research into the development of ATS is an active and relatively new field, with many studies demonstrating promising results. However, there is often no practical way to apply these findings in real-world settings. The ATS described in this paper utilizes a generic affective application model to infer and appropriately respond to the learner’s affective state. This approach brings several advantages, notably the potential direct support for re-use and retrospective addition of affect sensing functionality into existing e-learning software. Skin conductivity and heart rate variability measurements were used to infer affective activation and valence. The evaluation involved an experiment in which the effectiveness of the fully functional ATS was compared with that of a non-affective version, and was conducted with 40 adult participants. The evaluation of the effectiveness of this tutoring system showed that measurable improvements in perceived learning may be obtained with a modest level of software development.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11423-016-9470-5/MediaObjects/11423_2016_9470_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11423-016-9470-5/MediaObjects/11423_2016_9470_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11423-016-9470-5/MediaObjects/11423_2016_9470_Fig3_HTML.gif)
Similar content being viewed by others
References
Afzal, S., & Robinson, P. (2011). Designing for automatic affect inference in learning environments. Educational Technology & Society, 14(4), 21–34.
Aist, G., Kort, B., Reilly, R., Mostow, J., & Picard, R. (2002). Experimentally augmenting an intelligent tutoring system with human-supplied capabilities: Adding human-provided emotional scaffolding to an automated reading tutor that listens. In 4th IEEE International Conference on Multimodal Interfaces (pp. 483-490). Pittsburgh, PA, USA: IEEE Computer Society.
Alavi, M., Marakas, G., & Yoo, Y. (2002). A comparative study of distributed learning environments on learning outcomes. Information Systems Research, 13(4), 404–415. doi:10.1287/isre.13.4.404.72.
Alepis, E., & Virvou, M. (2011). Automatic generation of emotions in tutoring agents for affective e-learning in medical education. Expert Systems with Applications, 38(8), 9840–9847. doi:10.1016/j.eswa.2011.02.021.
Alepis, E., Virvou, M., & Kabassi, K. (2008). Requirements analysis and design of an affective bi-modal intelligent tutoring system: the case of keyboard and microphone. In M. Virvou & L. C. Jain (Eds.), Intelligent Interactive Systems in Knowedge-Based Environments. Berlin: Springer-Verlag.
Alexander, S. (2007). An affect-sensitive intelligent tutoring system with an animated pedagogical agent that adapts to human emotion. Albany: Massey University.
Alexander, S., Sarrafzadeh, A., & Hill, S. (2006). Easy with Eve: A functional affective tutoring system. In G. Rebolledo-Mendez, & E. Martinez-Miron (Ed.), Proceedings of Workshop on Motivational and Affective Issues in ITS. 8th International Conference on ITS (pp. 38-45).
Ammar, M. B., Neji, M., Alimi, A. M., & Gouardères, G. (2010). The affective tutoring system. Expert Systems with Applications, 37(4), 3013–3023.
BioPac Systems (2004). Heart rate variability analysis. http://www.biopac.com/Curriculum/pdf/h32.pdf. Accessed 1 March 2012.
Bower, G. H., & Forgas, J. P. (2001). Mood and social memory. In J. P. Forgas (Ed.), Handbook of affect and social cognition (pp. 95–120). Oxford: Pergamon.
Brown, K. G. (2001). Using computers to deliver training: which employees learn and why? Personnel Psychology, 54(2), 271–296. doi:10.1111/j.1744-6570.2001.tb00093.x.
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (Eds.). (2007). Handbook of psychophysiology (3rd ed.). New York: Cambridge University Press.
Campbell, D. T., Stanley, J. C., & Gage, N. L. (1963). Experimental and quasi-experimental designs for research. Boston: Houghton Mifflin.
Caspi, A., & Blau, I. (2008). Social presence in online discussion groups: testing three conceptions and their relations to perceived learning. Social Psychology Education, 11, 323–346.
Clore, G. L., Gasper, K., & Garvin, E. (2001). Affect as information. In J. P. Forgas (Ed.), Handbook of Affect and Social Cognition (pp. 121–144). Nahwah: Erlbaum.
Conati, C. (2002). Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence, 16(7–8), 555–575.
Conati, C., & Zhao, X. (2004). Building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game. In 9th International Conference on Intelligent User Interfaces (pp. 6-13). Funchal, Madeira, Portugal: ACM.
Craig, S. D., Graesser, A. C., Sullins, J., & Gholson, B. (2004). Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241–250.
Csíkszentmihályi, M. (1990). Flow: The psychology of optimal experience. New York: Harper and Row.
Custers, R., & Aarts, H. (2005). Positive affect as implicit motivator: on the nonconscious operation of behavioral goals. Journal of Personality and Social Psychology, 89(2), 129–142.
Cytowic, R. E. (1989). Synesthesia: A Union of the Senses. New York: Springer-Verlag.
D’Mello, S. (2008). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18(1–2), 45–80. doi:10.1007/s11257-007-9037-6.
D’Mello, S., & Graesser, A. (2012a). AutoTutor and affective autotutor learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2(4), 1–39. doi:10.1145/2395123.2395128.
D’Mello, S., & Graesser, A. (2012b). Emotions during learning with AutoTutor. In P. Durlach & A. Lesgold (Eds.), Adaptive Technologies for Training and Education (pp. 117–139). Cambridge: Cambridge University Press.
D’Mello, S., Lehman, B., & Graesser, A. (2011). A motivationally supportive affect-sensitive AutoTutor. In R. A. Calvo, & S. K. D’Mello (Ed.), New perspectives on affect and learning technologies (Vol. 3, pp. 113-126, Explorations in the Learning Sciences, Instructional Systems and Performance Technologies): Springer New York.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. doi:10.1111/j.1559-1816.1992.tb00945.x.
Ellis, H. C., & Ashbrook, P. W. (1988). Resource allocation model of the effects of depressed mood states on memory. In K. Fiedler & J. P. Forgas (Eds.), Affect, Cognition and Social Behavior (pp. 25–43). Gottingen: Hogrefe.
Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: the processing efficiency theory. Cognition and Emotion, 6(6), 409–434. doi:10.1080/02699939208409696.
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2), 336–353.
Frijda, N. H. (1986). The Emotions. Cambridge: Cambridge University Press.
Fu, F., Su, R., & Yu, S. (2009). EGameFlow: a scale to measure learners’ enjoyment of e-learning games. Computers & Education, 52(1), 101–112. doi:10.1016/j.compedu.2008.07.004.
Ghani, J., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human computer interaction. The Journal of Psychology, 128(4), 381–391. doi:10.1080/00223980.1994.9712742.
Goleman, D. (1995). Emotional Intelligence. New York: Bantam Books.
Hernández, Y., Sucar, L. E., & Conati, C. (2008). An affective behavior model for intelligent tutors. In Proceedings of 9th International Conference on Intelligent Tutoring Systems (pp. 819-821). Montreal, Canada: Springer-Verlag.
Hernández, Y., Sucar, L. E., & Conati, C. (2009). Incorporating an affective behavior model into an educational game. In Twenty Second International FLAIRS Conference. Florida, USA.
Hone, K. (2006). Empathic agents to reduce user frustration: the effects of varying agent characteristics. Interacting with Computers, 18(2), 227–245.
Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? an extended TAM with social influences and flow experience. Information & Management, 41(7), 853–868. doi:10.1016/j.im.2003.08.014.
Jiang, M. (2000). A study of factors influencing students’ perceived learning in a web-based course environment. International Journal of Educational Telecommunications, 6(4), 317–338.
Kaklauskas, A., Kuzminske, A., Zavadskas, E. K., Daniunas, A., Kaklauskas, G., Seniut, M., et al. (2015). Affective tutoring system for built environment management. Computers & Education, 82, 202–216. doi:10.1016/j.compedu.2014.11.016.
Kim, C. (2012). The role of affective and motivational factors in designing personalized learning environments. Educational Technology Research and Development, 60(4), 563–584.
Kim, Y., Baylor, A. L., & Shen, E. (2007). Pedagogical agents as learning companions: the impact of agent emotion and gender. Journal of Computer Assisted learning, 23(3), 220–234.
Kirsch, D. (1997). The Sentic Mouse : Develo** a tool for measuring emotional valence. http://affect.media.mit.edu/projectpages/archived/projects/sentic_mouse.html. Accessed 5th Nov 2012.
Klein, J., Moon, Y., & Picard, R. W. (2002). This computer responds to user frustration: theory, design and results. Interacting with Computers, 14(2), 119–140.
Kort, B., Reilly, R., & Picard, R. W. An affective model of interplay between emotions and learning: Reengineering educational pedagogy—Building a learning companion. In IEEE International Conference on Advanced Learning Technologies, Madison, USA, 2001 (pp. 43-48).
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104.
Lin, H. C. K., Chao, C.-J., & Huang, T.-C. (2015). From a perspective on foreign language learning anxiety to develop an affective tutoring system. [journal article]. Educational Technology Research and Development., 63(5), 727–747. doi:10.1007/s11423-015-9385-6.
Lin, A. C. H., Fernandez, W. D., & Gregor, S. (2012). Understanding web enjoyment experiences and informal learning: a study in a museum context. Decision Support Systems, 53(4), 846–858. doi:10.1016/j.dss.2012.05.020.
Lin, H. C. K., Su, S. H., Chao, C. J., Hsieh, C. Y., & Tsai, S. C. (2016). Construction of multi-mode affective learning system: taking affective design as an example. Educational Technology & Society, 19(2), 132–147.
Lisetti, C. L. A user model of emotion-cognition. In Workshop on Attitude, Personality, and Emotions in User-Adapted Interaction at the International Conference on User-Modeling (UM’99), Banff, Canada, 1999.
Litman, D. J., & Silliman, S. (2004). ITSPOKE: An intelligent tutoring spoken dialogue system. In Human Language Technology Conference 4th Meeting of the North American Chapter of the Association for Computational Linguistics (pp. 5-8). Boston, USA: Association for Computational Linguistics.
Lowendahl, J.-M. (2012). Hype Cycle for Education. http://www.gartner.com/DisplayDocument?doc_cd=233974&ref=g_sitelink. Accessed 1st September 2012.
Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction. Cognitive Science, 5(4), 349–361.
Mayer, R. E. (1998). A split-attention effect in multimedia learning: evidence for dual processing systems in working memory. Journal of Educational Psychology, 90(2), 312–320. doi:10.1037/0022-0663.90.2.312.
Mayer, R. E. (2005). Cognitive Theory of Multimedia Learning. New York: Cambridge University Press.
Mayer, R. E., & Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12–18. doi:10.1016/j.learninstruc.2014.02.004.
Microsoft Corporation (2009). Microsoft Agent. http://www.microsoft.com/products/msagent/main.aspx. Accessed 22 June 2012.
Molster, C., Charles, T., Samanek, A., & O’Leary, P. (2009). Australian study on public knowledge of human genetics and health. Public Health Genomics, 12(2), 84–91.
Moreno, R. (2005). Multimedia learning with animated pedagogical agents. In R. Mayer (Ed.), TheCambridge Handbook of Multimedia Learning (pp. 507–524). Cambridge: Cambridge University Press.
Moreno, R. (2006). Does the modality principle hold for different media? A test of the method-affects-learning hypothesis. Journal of Computer Assisted learning, 22(3), 149–158.
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York: McGraw-Hill.
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.
Picard, R. W. (1997). Affective Computing. Massachusetts: MIT Press.
Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: effects of shape and color on affect and learning. Learning and Instruction, 29, 128–140. doi:10.1016/j.learninstruc.2013.02.006.
Prendinger, H., Dohi, H., Wang, H., Mayer, S., & Ishizuka, M. (2004). Empathic embodied interfaces: Addressing users’ affective state. In E. André, L. Dybkjær, W. Minker, & P. Heisterkamp (Ed.), Tutorial and Research Workshop on Affective Dialogue Systems 2004 (pp. 53-64, Lecture Notes in Computer Science). Kloster Irsee, Germany: Springer Berlin/Heidelberg.
Richards, M. (1996). Lay and professional knowledge of genetics and inheritance. Public Understanding of Science, 5(3), 217–230.
Sarrafzadeh, A., Alexander, S., Dadgostar, F., Fan, C., & Bigdeli, A. (2008). ‘‘How do you know that I don’t understand?’’ a look at the future of intelligent tutoring systems. Computers in Human Behaviour, 24(4), 1342–1363.
Schwarz, N. (1990). Feelings as information: Informational and motivational functions of affective states. In E. T. Higgins & R. M. Sorrentino (Eds.), Handbook of Motivation and Cognition: Foundations of Social Behaviour (pp. 527–561). New York: Guildford Press.
Schwarz, N., & Clore, G. L. (1988). How do I feel about it? the informative function of affective states. In K. Fiedler & I. Forgas (Eds.), Affect, Cognition, and Social Behavior (pp. 44–62). Göttingen: Hogrefe.
Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning: using “emotional” data to improve learning in pervasive learning environment. Educational Technology & Society, 12(2), 176–189.
Sofer, W., & Gribbin, M. (2010). Morgan : A genetics tutorial. http://morgan.rutgers.edu/MorganWebFrames/How_to_use/HTU_Frameset.html. Accessed 1 August 2010.
Stein, N. L., & Levine, L. J. (1991). Making sense out of emotion. In W. Kessen, A. Ortony, & F. Kraik (Eds.), Memories, Thoughts, and Emotions: Essays in Honor of George Mandler (pp. 295–322). Hillsdale: Erlbaum.
Susarla, S., Adcock, A., Van Eck, R., Moreno, K., & Graesser, A. Development and evaluation of a lesson authoring tool for AutoTutor. In Artifical Intelligence in Education Conference, Sydney, Australia, 2003 (pp. 378-387).
Thompson, N., Koziniec, T., & McGill, T. (2012). An open affective computing platform. In Proceedings of the IEEE 3rd International Conference on Networked and Embedded Systems for Every Application (pp. 1-10). Liverpool, UK.
Thompson, N., & McGill, T. (2015). Affective stack—a model for affective computing application development. Journal of Software, 10(8), 919–930.
Thought Technology (2010). CardioPro Infiniti HRV analysis module user manual. http://www.thoughttechnology.com/pdf/manuals/SA7590%20CardioPro%20Infiniti%20HRV%20Analysis%20Module%20User%20Manual.pdf. Accessed 1st February 2012.
Um, E., Plass, J. L., Hayward, E. O., & Homer, B. D. (2012). Emotional design in multimedia learning. Journal of Educational Psychology, 104(2), 485–498.
van der Meij, H. (2013). Motivating agents in software tutorials. Computers in Human Behavior, 29(3), 845–857. doi:10.1016/j.chb.2012.10.018.
Wine, J. (1971). Test anxiety and direction of attention. Psychological Bulletin, 76(2), 92.
Woolf, B., Burelson, W., & Arroyo, I. Emotional intelligence for computer tutors. In AIED (Ed.), 13th International Conference on Artificial Intelligence in Education, Los Angeles, USA, 2007 (pp. 6-15).
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology, 4(3), 129–164.
Wu, C.-H., Huang, Y.-M., & Hwang, J.-P. (2015). Review of affective computing in education/learning: trends and challenges. British Journal of Educational Technology,. doi:10.1111/bjet.12324.
Yannakakis, G., Hallam, J., & Lund, H. (2008). Entertainment capture through heart rate activity in physical interactive playgrounds. User Modeling and User-Adapted Interaction, 18(1), 207–243. doi:10.1007/s11257-007-9036-7.
Zakharov, K., Mitrovic, A., & Johnston, L. (2007). Pedagogical agents trying on a caring mentor role. Frontiers in Artificial Intelligence and Applications, 158, 59–66.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Thompson, N., McGill, T.J. Genetics with Jean: the design, development and evaluation of an affective tutoring system. Education Tech Research Dev 65, 279–299 (2017). https://doi.org/10.1007/s11423-016-9470-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11423-016-9470-5