Computer Supported Collaborative Learning and Intelligent Tutoring Systems

In this chapter we discuss how recent advances in the field of Computer Supported Collaborative Learning (CSCL) have created the opportunity for new synergies between CSCL and ITS research. Three “hot” CSCL research topics are used as examples: analyzing individual’s and group’s interactions, providing students with adaptive intelligent support, and providing students with adaptive technological means.

[1]  Pierre Dillenbourg,et al.  Over-scripting CSCL: The risks of blending collaborative learning with instructional design , 2002 .

[2]  K. Koedinger,et al.  Using Intelligent Tutor Technology to Implement Adaptive Support for Student Collaboration , 2010 .

[3]  Patrick Jermann,et al.  Designing Integrative Scripts , 2007 .

[4]  Andreas Harrer,et al.  Bridging the Gap - Towards a Graphical Modeling Language for Learning Designs and Collaboration Scripts of Various Granularities , 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).

[5]  Manu Kapur Productive Failure , 2006, ICLS.

[6]  F. Fischer,et al.  Collaboration Scripts – A Conceptual Analysis , 2006 .

[7]  Allan Jeong A Guide to Analyzing Message–Response Sequences and Group Interaction Patterns in Computer‐mediated Communication , 2005 .

[8]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[9]  Pierre Tchounikine,et al.  A computer science perspective on TEL research , 2009 .

[10]  André Tricot,et al.  Supporting Learners' Self-organization: An Exploratory Study , 2010, Intelligent Tutoring Systems.

[11]  Jesus Favela,et al.  Groupware: Design, Implementation, and Use , 2003, Lecture Notes in Computer Science.

[12]  N. Rummel,et al.  Learning to Collaborate: An Instructional Approach to Promoting Collaborative Problem Solving in Computer-Mediated Settings , 2005 .

[13]  Frank Fischer,et al.  Scripting Computer-Supported Collaborative Learning : cognitive, computational, and educational perspectives , 2007 .

[14]  E. Cohen Restructuring the Classroom: Conditions for Productive Small Groups , 1994 .

[15]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[16]  Jack Mostow,et al.  Can Automated Questions Scaffold Children's Reading Comprehension? , 2004, Intelligent Tutoring Systems.

[17]  K. Koedinger,et al.  Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring , 2005 .

[18]  Heinz Ulrich Hoppe,et al.  Combining structural, process-oriented and textual elements to generate awareness indicators for graphical e-discussions , 2007, CSCL.

[19]  Carolyn Penstein Rosé,et al.  Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning , 2008, Int. J. Comput. Support. Collab. Learn..

[20]  Hans Spada,et al.  Learning in Humans and Machines: Towards an Interdisciplinary Learning Science , 1995 .

[21]  Jihie Kim,et al.  Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers , 2007, AIED.

[22]  Georges Gardarin,et al.  Advances in Database Technology — EDBT '96 , 1996, Lecture Notes in Computer Science.

[23]  Daniel D. Suthers,et al.  Coaching Web-based Collaborative Learning based on Problem Solution Differences and Participation , 2003, Int. J. Artif. Intell. Educ..

[24]  Pierre Tchounikine,et al.  Flexibility in macro-scripts for computer-supported collaborative learning , 2007, J. Comput. Assist. Learn..

[25]  Patrícia C. A. R. Tedesco,et al.  MArCo: Building an Artificial Conflict Mediator to Support Group Planning Interactions , 2003, Int. J. Artif. Intell. Educ..

[26]  Jörg M. Haake,et al.  Flexible Scripting in Net-Based Learning Groups , 2007 .

[27]  Bruce M. McLaren,et al.  Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques , 2010, Int. J. Artif. Intell. Educ..

[28]  Pierre Tchounikine,et al.  Flexibility in macro-scripts for CSCL , 2007 .

[29]  Amy Soller,et al.  Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning , 2004, User Modeling and User-Adapted Interaction.

[30]  Judy Kay,et al.  Mining patterns of events in students’ teamwork data , 2006 .

[31]  Niels Pinkwart,et al.  Extending a virtual chemistry laboratory with a collaboration script to promote conceptual learning , 2010 .

[32]  P. Dillenbourg,et al.  The evolution of research on collaborative learning , 1996 .

[33]  Nikol Rummel,et al.  Instructional Support for Collaboration in Desktop Videoconference Settings , 2005 .

[34]  Hans Spada,et al.  Barriers and Biases in Computer-Mediated Knowledge Communication , 2005 .

[35]  Nadira Saab,et al.  Supporting Communication in a Collaborative Discovery Learning Environment: the Effect of Instruction , 2007 .

[36]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[37]  Heinz Ulrich Hoppe,et al.  Computer supported moderation of e-discussions: the ARGUNAUT approach , 2007, CSCL.

[38]  Robert M. Aiken,et al.  Supporting Collaborative Learning With An Intelligent Web-Based System , 2007, Int. J. Artif. Intell. Educ..

[39]  Jihie Kim,et al.  Scaffolding On-Line Discussions with Past Discussions: An Analysis and Pilot Study of PedaBot , 2008, Intelligent Tutoring Systems.

[40]  Päivi Häkkinen,et al.  Specifying computer-supported collaboration scripts , 2007, Int. J. Comput. Support. Collab. Learn..

[41]  William W. Cohen,et al.  On the collective classification of email "speech acts" , 2005, SIGIR '05.

[42]  Pierre Tchounikine,et al.  Operationalizing macro-scripts in CSCL technological settings , 2008, Int. J. Comput. Support. Collab. Learn..

[43]  Nasser Mansour,et al.  Exploring creative thinking in graphically mediated synchronous dialogues , 2010, Comput. Educ..

[44]  Kenneth R. Koedinger,et al.  Integrating Collaboration and Intelligent Tutoring Data in the Evaluation of a Reciprocal Peer Tutoring Environment , 2009, Res. Pract. Technol. Enhanc. Learn..

[45]  Guido Zarrella,et al.  Using Dialogue Features to Predict Trouble During Collaborative Learning , 2006, User Modeling and User-Adapted Interaction.

[46]  Pierre Dillenbourg,et al.  Flexibility in macro CSCL scripts , 2007 .

[47]  Siriwan Suebnukarn,et al.  Modeling Individual and Collaborative Problem Solving in Medical Problem-Based Learning , 2005, User Modeling.

[48]  Carolyn Penstein Rosé,et al.  Tutorial Dialogue as Adaptive Collaborative Learning Support , 2007, AIED.

[49]  Pierre Tchounikine Directions to Acknowledge Learners' Self-organization in CSCL Macro-scripts , 2007, CRIWG.

[50]  Ian Witten,et al.  Data Mining , 2000 .

[51]  Kurt VanLehn,et al.  The Andes Physics Tutoring System: Lessons Learned , 2005, Int. J. Artif. Intell. Educ..

[52]  Bruce M. McLaren,et al.  Extensionally defining principles and cases in ethics: An AI model , 2003, Artif. Intell..

[53]  Antonija Mitrovic,et al.  Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams , 2007, Int. J. Comput. Support. Collab. Learn..

[54]  R. Slavin Research on cooperative learning and achievement: What we know, what we need to know. , 1996 .

[55]  Anastasios Karakostas,et al.  Adaptation patterns in systems for scripted collaboration , 2009, CSCL.

[56]  G. Salomon,et al.  When teams do not function the way they ought to , 1989 .

[57]  K. Koedinger,et al.  Exploring the Assistance Dilemma in Experiments with Cognitive Tutors , 2007 .

[58]  Kenneth R. Koedinger,et al.  CTRL: A research framework for providing adaptive collaborative learning support , 2009, User Modeling and User-Adapted Interaction.

[59]  Siriwan Suebnukarn,et al.  Modeling individual and collaborative problem-solving in medical problem-based learning , 2006, User Modeling and User-Adapted Interaction.

[60]  N. Rummel,et al.  Collaborative Learning with the Cognitive Tutor Algebra. An Experimental Classroom Study , 2008 .