Regression-Based Affect Recognition and Handling Using the Attribution Theory

This chapter presents the automatic frustration detection module and the response to it through motivational messages using a learning theory of our social networking-based language learning system, called POLYGLOT. In POLYGLOT, students can declare their affective state among “happy”, “frustrated” and “neutral”. However, their interaction with the tutoring system, i.e. experiencing difficulty in a test or receiving a bad grade, can be a blockage of their goal and as such the reason of feeling a negative emotion, such as frustration. Hence, POLYGLOT can detect students’ frustration by using the linear regression model. The relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Finally, the POLYGLOT’s response on frustration is the delivery of motivational messages based on the Attribution Theory, involving a three-stage process underlying that behavior must be observed/perceived, must be determined to be intentional and is attributed to internal or external causes. With the use of motivational messages, the students are assisted in the educational process and are not willing to quit learning.

[1]  Paul E. Spector.  ORGANIZATIONAL FRUSTRATION: A MODEL AND REVIEW OF THE LITERATURE , 1978 .

[2]  Elliot E. Entin,et al.  Introduction to Psychology, 7th ed. , 1980 .

[3]  B. Weiner,et al.  Theories and principles of motivation. , 1996 .

[4]  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.

[5]  Jonathan Klein,et al.  This computer responds to user frustration: Theory, design, and results , 2002, Interact. Comput..

[6]  Philip C. Abrami,et al.  Student Goal Orientation and Interest: Effects on Students' Use of Self-Regulated Learning Strategies. , 2001, Contemporary educational psychology.

[7]  James Paul Gee,et al.  What video games have to teach us about learning and literacy , 2007, CIE.

[8]  Ben Shneiderman,et al.  Severity and impact of computer user frustration: A comparison of student and workplace users , 2006, Interact. Comput..

[9]  Ashish Kapoor,et al.  Automatic prediction of frustration , 2007, Int. J. Hum. Comput. Stud..

[10]  Cristina Conati,et al.  Empirically building and evaluating a probabilistic model of user affect , 2009, User Modeling and User-Adapted Interaction.

[11]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[12]  Arthur C. Graesser,et al.  Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments , 2010, Int. J. Hum. Comput. Stud..

[13]  Omar AlZoubi,et al.  Affect Detection from Multichannel Physiology during Learning Sessions with AutoTutor , 2011, AIED.

[14]  James C. Lester,et al.  Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks , 2011, ACII.

[15]  Jonah E. Rockoff,et al.  Organizing Schools to Improve Student Achievement: Start Times, Grade Configurations, and Teacher Assignments. , 2011 .

[16]  Keith W. Brawner,et al.  Real-Time Monitoring of ECG and GSR Signals during Computer-Based Training , 2012, ITS.

[17]  Andrew M. Ewing.  Estimating the impact of relative expected grade on student evaluations of teachers , 2012 .

[18]  Laura M. O’Dwyer,et al.  Impact of Online Professional Development or Teacher Quality and Student Achievement in Fifth Grade Mathematics , 2012 .