201407 A System Architecture for Affective Meta Intelligent Tutoring Systems

2,725 views
2,662 views

Published on

Intelligent Tutoring Systems (ITSs) constitute an alternative to expert human tutors, providing direct customized instruction and feedback to students. ITSs could positively impact education if adopted on a large scale, but doing that requires tools to enable their mass production. This circumstance is the key motivation for this work. We present a component-based approach for a system architecture for ITSs equipped with meta-tutoring and affective capabilities. We elicited the requirements that those systems might address and created a system architecture that models their structure and behavior to drive development efforts. Our experience applying the architecture in the incremental implementation of a four-year project is discussed.

Published in: Technology, Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,725
On SlideShare
0
From Embeds
0
Number of Embeds
1,994
Actions
Shares
0
Downloads
0
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

201407 A System Architecture for Affective Meta Intelligent Tutoring Systems

  1. 1. A System Architecture for Affective Meta Intelligent Tutoring Systems Javier Gonzalez-Sanchez, Maria Elena Chavez-Echeagaray, Kurt VanLehn, Winslow Burleson, Sylvie Girard, Yoalli Hidalgo-Pontet, and Lishan Zhang
  2. 2. A System Architecture for Affective Meta Intelligent Tutoring Systems Support ITS development and inclusion of Affective and Meta-Tutoring Capabilities Affective Meta-Tutor (AMT) Project(VanLehn et. al., 2014) Step-based ITS + Meta-Tutor + Affective Companion 4-year research project
  3. 3. Outline 1.  Background 2.  Approach 3.  Evaluation 4.  Discussion 5.  Conclusion
  4. 4. Support ITS Development | Related Work §  ITS design patterns (Harrer, Pinkwart, McLaren, & Scheuer, 2008; Devedzic & Harrer, 2005) §  Reusable components and learning objects (Koedinger, Suthers, & Forbus, 1999; Ritter, Blessing, & Wheeler, 2003; Ritter & Koedinger, 1997) §  Use of off-the-shelf tools as an integrated part of the ITS development process (e.g., Aleven, Sewall, McLaren, & Koedinger, 2006) §  Authoring tools (e.g., Murray, Blessing & Ainsworth, 2003) 1
  5. 5. Support ITS Development | Architecture Code   (integration) Design   (pa2erns) Architecture 1
  6. 6. AMT Project | Summary1 2009 2010 2011 2012 2013 ITS ITS ITS ITS MetaTutor MetaTutor MetaTutor ALC §  Affective Meta Tutor (VanLehn et. al., 2014) §  Schedule constraints §  Robustness §  One click installation §  Work with and without network access §  Multiplatform Tool Summer Camps High School Students Several Components
  7. 7. AMT Project | Summary1 2009 2010 2011 2012 2013 ITS ITS ITS ITS MetaTutor MetaTutor MetaTutor ALC §  Modifiability §  Extensibility §  Robustness §  Cost-Effectiveness •  Undergrad students (developers) •  High turnover in the development team Students graduate after 1 year (average) Change is Expected
  8. 8. Motivation | structural integrity1
  9. 9. Approach •  Architectural Principles: o  encapsulation o  low coupling o  centralized shared data o  layering •  Component-Based and Pattern Oriented design: o  “divide and conquer” o  complex components are split into several collaborative components •  Developers know their part •  Developers have constrained freedom 2
  10. 10. Why to care about architecting the AMT?
  11. 11. AMT 2009, version 0.1 •  Interactive graphical interface (student freely manipulate nodes and connect them) •  Task (problems to be solved) are edited by human experts (outside the tutor) 2
  12. 12. AMT 2009, version 0.7 •  Tutor, version 2009 release 0.7 •  User can assign expressions to nodes and expressions are parsed in real time to provide visual feedback 2
  13. 13. AMT 2009, version 0.81 •  New look and feel of nodes and interconnection rules •  User can “run” and can create plots of the model 2
  14. 14. AMT 2009, version 0.91 •  ITS runs the model •  Characteristics that the model should satisfy are verified as questions in a Quiz •  Metatutor and ALC becomes external agents with internal advocates 2
  15. 15. AMT 2010, version 1.11 •  The system can run on 2 profiles: student and professor. The system supports the authoring process. •  Competition flavor, where points are given for each correct step. Asking for hint costs points. •  Companion with non- animated human face. 2
  16. 16. AMT 2010, version 1.9 •  Increase student freedom: nodes has menus and color indicators. •  Fine-grain student support. 2
  17. 17. AMT 2011, version 2.xx •  ITS is stable •  Improved meta tutor 2
  18. 18. AMT 2012, version 3.xx •  ITS and meta tutor are stable •  Diverse approaches for ALC: graphical character, real-time affect-driven interaction 2
  19. 19. AMT Architecture2 Affective Companion Emotion Recognition Subsystem Shared Repository Knowledge Base User Interface (Tool or Environment) Tutor Meta Tutor
  20. 20. AMT Architecture2 Affective Companion Emotion Recognition Subsystem Shared Repository Knowledge Base User Interface (Tool or Environment) Affective Engine Tutor Step Analyzer Assessor Pedagogical Module Task Selector Meta Tutor Inspector Engine Event Selector
  21. 21. AMT •  1,643 revisions posted to our SVN repository •  8 released versions •  16 packages •  120 classes •  1507 methods •  1810 attributes •  37,374 lines of code •  2 third-party subsystems: a production-rule system and a third-party implementation of emotion recognition algorithms were used to support the application development •  4 developers was working concurrently in every stage (15 developers in total) 2
  22. 22. Evaluation The structural software quality was evaluated using software metrics for size, complexity, and coupling: •  number of packages (P), •  number of classes (F), •  number of functions (Fn), •  number of lines of code (LoC), •  number of comments (LoCm), •  average cyclomatic complexity (AvC), •  maximum afferent coupling (MaxAC), and •  maximum efferent coupling (MaxEC). 3
  23. 23. Evaluation We report the evaluation of four AMT application releases, one from each development year, as follows: (1)  Release 742 implemented the first deployed Tutor; coding the skeleton of the system was the primary goal during this year. (2)  Release 1277 refashioned the User Interface and implemented an enhanced Tutor. (3)  Release 1545 included a Meta Tutor, continued refashioning the User Interface, and enhanced the Tutor module. (4)  Release 1643 added the Affective Companion capabilities, enhanced the Meta Tutor, and refactored the User Interface and Tutor. 3
  24. 24. Evaluation3
  25. 25. Discussion4 Size measurements (P, F, Fn, LoC, and LoCm) show a correspondence of the requirements implemented in each release and the size of the application, as well as a balance in its granularity
  26. 26. Discussion4 The average complexity (AvgC) at the module level remains within acceptable ranges – below 5; however, at a fine-grain level (classes), not shown in the table, those values are not always acceptable.
  27. 27. Discussion4 The decrease in average complexity in the latest versions of Tutor shows the refactoring outcome (functionality was fixed and developers focused on code improvements).
  28. 28. Discussion4 Lower values in coupling measures (MaxAC and MaxEC) are better since they are a sign of independence; the high values of coupling in Tutor can be justified because they belong to the User Interface (highly connected); Meta Tutor values are acceptable, but Affective Companion values suggest that a refactoring would be required in the implementation of this module.
  29. 29. Summary •  Shared our experience developing an ITS with meta and affective capabilities. •  Presented AMT system architecture. •  Defined requirements and qualities and have shown how the AMT system architecture addresses them to support large- scale reuse. •  Use software metrics for different releases of one AMT application to show how the system architecture provided a flexible partition of the system that facilitates modifiability, extensibility, and integrability. 5
  30. 30. A System Architecture for Affective Meta Intelligent Tutoring Systems javiergs@asu.edu | helenchavez@asu.edu

×