Dynamic Approaches to Modeling Student Affect and its Changing Role in Learning and Performance

We investigate the relation between students’ affect, learning and performance in the context of the ASSISTments online math tutoring system. Moment-by-moment estimates of students’ affective states derived from a series of affect detectors accompany each student response within the tutoring system. By applying a series modified factorial hidden Markov models that account for students’ affective state at the time of the given response and comparing the models’ performance to the standard Bayesian Knowledge Tracing (BKT) approach, we evaluate the impact of affect on estimates of students’ guess and slip behavior. The investigation suggests a model based approach to improving student models in the context of online tutoring systems.

[1]  Reinhard Pekrun,et al.  Introduction to Emotion in Education , 2007 .

[2]  Beverly Park Woolf,et al.  A Dynamic Mixture Model to Detect Student Motivation and Proficiency , 2006, AAAI.

[3]  Reinhard Pekrun,et al.  Achievement Emotions: A Control-Value Approach , 2010 .

[4]  Cornelis A.W. Glas,et al.  A dynamic generalization of the Rasch model , 1993 .

[5]  A. Graesser,et al.  Dynamics of affective states during complex learning , 2012 .

[6]  Richard C. Atkinson,et al.  Ingredients for a theory of instruction. , 1972 .

[7]  A. Damasio,et al.  We feel, therefore we learn: The relevance of affective and social neuroscience to education. , 2007 .

[8]  Lisa Linnenbrink-Garcia,et al.  Students emotions and academic engagement: Introduction to the special issue , 2011 .

[9]  Cs Dweck,et al.  Messages that motivate: How praise molds students’ beliefs, motivation, and performance (in surprising ways). Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. ). Amsterdam: Academic Press. , 2002 .

[10]  Ryan Shaun Joazeiro de Baker,et al.  Detecting Student Misuse of Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[11]  Nico H. Frijda,et al.  Emotion Experience and its Varieties , 2009 .

[12]  Paul De Boeck,et al.  Descriptive and explanatory item response models , 2004 .

[13]  Thomas Huk,et al.  Combining cognitive and affective support in order to promote learning , 2009 .

[14]  F. Apel,et al.  Nice to Have , 1998 .

[15]  John R. Anderson,et al.  Student modeling in the ACT Programming Tutor. , 1995 .

[16]  Neil T. Heffernan,et al.  Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System , 2013, AIED.

[17]  J. Russell.  Core affect and the psychological construction of emotion. , 2003, Psychological review.

[18]  Zachary A. Pardos,et al.  Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing , 2010, UMAP.

[19]  Lisa Linnenbrink-Garcia,et al.  Academic Emotions and Student Engagement , 2012 .

[20]  K. Scherer.  The dynamic architecture of emotion: Evidence for the component process model , 2009 .

[21]  L. F. Barrett.  Variety is the spice of life: A psychological construction approach to understanding variability in emotion , 2009, Cognition & emotion.

[22]  D. K. Meyer,et al.  Re-conceptualizing Emotion and Motivation to Learn in Classroom Contexts , 2006 .

[23]  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..

[24]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[25]  Jim Reye,et al.  Student Modelling Based on Belief Networks , 2004, Int. J. Artif. Intell. Educ..

[26]  Kurt Reusser,et al.  Activating Positive Affective Experiences in the Classroom: "Nice to Have" or Something More?. , 2011 .

[27]  Arthur C. Graesser,et al.  The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor , 2005 .

[28]  Scotty D. Craig,et al.  Integrating Affect Sensors in an Intelligent Tutoring System , 2004 .