A Scalable Intelligent Tutoring System Framework for Computer Science Education

Computer Science is a difficult subject with many fundamentals to be taught, usually involving a steep learning curve for many students. It is some of these initial challenges that can turn students away from computer science. We have been developing a new Intelligent Tutoring System, ChiQat-Tutor, that focuses on tutoring of Computer Science fundamentals. Here, we outline the system under development, while bringing particular attention to its architecture and how it attains the primary goals of being easily extensible and providing a low barrier of entry to the end user. The system is broadly broken down into lessons, teaching strategies, and utilities, which work together to promote seamless integration of components. We also cover currently developed components in the form of a case study, as well as detailing our experience of deploying it to an undergraduate Computer Science classroom, leading to learning gains on par with prior work.

[1]  Davide Fossati,et al.  Data D riven A utomatic F eedback G eneration in the iList I ntelligent T utoring S ystem , 2014 .

[2]  Benjamin Nye,et al.  Intelligent Tutoring Systems by and for the Developing World: A Review of Trends and Approaches for Educational Technology in a Global Context , 2014, International Journal of Artificial Intelligence in Education.

[3]  Luis Martínez-López,et al.  An Intelligent Tutoring System Architecture for Competency-Based Learning , 2011, KES.

[4]  Davide Fossati,et al.  Learning Linked Lists: Experiments with the iList System , 2008, Intelligent Tutoring Systems.

[5]  Vincent Aleven,et al.  The Cognitive Tutor Authoring Tools (CTAT): Preliminary Evaluation of Efficiency Gains , 2006, Intelligent Tutoring Systems.

[6]  Wen-Jung Hsin Teaching recursion using recursion graphs , 2008 .

[7]  Davide Fossati,et al.  Worked Out Examples in Computer Science Tutoring , 2013, AIED.

[8]  Lin Chen,et al.  Worked out examples in computer science tutoring , 2013, AIED 2013.

[9]  Kim Topley JavaFX Developer's Guide , 2010 .

[10]  K. VanLehn The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems , 2011 .

[11]  Arthur C. Graesser,et al.  AutoTutor: an intelligent tutoring system with mixed-initiative dialogue , 2005, IEEE Transactions on Education.

[12]  Tom Gaertner,et al.  Effective Teaching Strategies That Accommodate Diverse Learners , 2016 .

[13]  Davide Fossati,et al.  ChiQat-Tutor: An Integrated Environment for Learning Recursion , 2014 .

[14]  Davide Fossati,et al.  Generating Proactive Feedback to Help Students Stay on Track , 2010, Intelligent Tutoring Systems.

[15]  A. Renkl The worked-out-example principle in multimedia learning , 2005 .

[16]  Peter Brusilovsky,et al.  ELM-ART: An Intelligent Tutoring System on World Wide Web , 1996, Intelligent Tutoring Systems.

[17]  Judith Gal-Ezer,et al.  What (else) should CS educators know? , 1998, CACM.

[18]  S. Derry,et al.  Learning from Examples: Instructional Principles from the Worked Examples Research , 2000 .

[19]  J. Sweller The worked example effect and human cognition , 2006 .

[20]  John Mason,et al.  Why the high attrition rate for computer science students: some thoughts and observations , 2005, SGCS.

[21]  Davide Fossati,et al.  Supporting Computer Science Curriculum: Exploring and Learning Linked Lists with iList , 2009, IEEE Transactions on Learning Technologies.