Using Graph-based Modelling to explore changes in students' affective states during exploratory learning tasks

This paper describes how graph-based modelling can be used to explore interactions associated with a change in students' affective state when they are working with an exploratory learning environment (ELE). We report on a user study with an ELE that is able to detect students' affective states from their interactions and speech. The data collected during the user study was modelled, visualized and queried as a graph. We were interested in exploring if there was a difference between low- and high-performing students in the kinds of interactions that occurred during a change in their affective state. Our findings provide new insights into how students are interacting with the ELE and the effects of the system's interventions on students' affective states.

[1]  Beverly Park Woolf,et al.  Affect-aware tutors: recognising and responding to student affect , 2009, Int. J. Learn. Technol..

[2]  Manolis Mavrikis,et al.  Combining Exploratory Learning With Structured Practice to Foster Conceptual and Procedural Fractions Knowledge , 2016, ICLS.

[3]  Dan Suthers,et al.  From contingencies to network-level phenomena: multilevel analysis of activity and actors in heterogeneous networked learning environments , 2015, LAK.

[4]  Manolis Mavrikis,et al.  Exploring the Potential of Speech Recognition to Support Problem Solving and Reflection - Wizards Go to School in the Elementary Maths Classroom , 2014, EC-TEL.

[5]  Nabil Belacel,et al.  A Binary Integer Programming Model for Global Optimization of Learning Path Discovery , 2014, EDM.

[6]  Michael Eagle,et al.  Exploring Differences in Problem Solving with Data-Driven Approach Maps , 2014, EDM.

[7]  Manolis Mavrikis,et al.  Diagnosing and acting on student affect: the tutor’s perspective , 2008, User Modeling and User-Adapted Interaction.

[8]  Lars Schmidt-Thieme,et al.  Perceived Task-Difficulty Recognition from Log-file Information for the Use in Adaptive Intelligent Tutoring Systems , 2016, International Journal of Artificial Intelligence in Education.

[9]  Bruce M. McLaren,et al.  CASE: A Configurable Argumentation Support Engine , 2013, IEEE Transactions on Learning Technologies.

[10]  R. Pekrun The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice , 2006 .

[11]  Arthur C. Graesser,et al.  AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back , 2012, TIIS.

[12]  Michael Eagle,et al.  Exploring networks of problem-solving interactions , 2015, LAK.

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

[14]  A. Graesser,et al.  Confusion can be beneficial for learning. , 2014 .

[15]  Alexandra Poulovassilis,et al.  Graph-based Modelling of Students' Interaction Data from Exploratory Learning Environments , 2015, EDM.

[16]  Benedict du Boulay,et al.  Implementation of motivational tactics in tutoring systems , 1995 .

[17]  Manolis Mavrikis,et al.  Affective learning: improving engagement and enhancing learning with affect-aware feedback , 2017, User Modeling and User-Adapted Interaction.

[18]  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..

[19]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[20]  Dror Ben-Naim,et al.  A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials , 2009, EDM.

[21]  Manolis Mavrikis,et al.  A Separation of Concerns for Engineering Intelligent Support for Exploratory Learning Environments , 2012 .

[22]  Ya'akov Gal,et al.  On-Line Plan Recognition in Exploratory Learning Environments , 2014, EDM.

[23]  Vincent Aleven,et al.  Graph Grammars: An ITS Technology for Diagram Representations , 2008, FLAIRS Conference.

[24]  Andreas Harrer,et al.  Empowering researchers to detect interaction patterns in e-collaboration , 2007, AIED.

[25]  Minjuan Wang,et al.  Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment , 2009, J. Educ. Technol. Soc..

[26]  Collin Lynch,et al.  InVis: An EDM Tool For Graphical Rendering And Analysis Of Student Interaction Data , 2014, EDM.

[27]  Ryan Shaun Joazeiro de Baker,et al.  Comparing Expert and Metric-Based Assessments of Association Rule Interestingness , 2014, EDM.

[28]  Manolis Mavrikis,et al.  Exploring Students' Affective States During Learning with External Representations , 2017, AIED.