Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces

Designing an Intelligent Tutoring System (ITS) usually requires precise models of the underlying domain, as well as of how a human tutor would respond to student mistakes. As such, the applicability of ITSs is typically restricted to welldefined domains where such a formalization is possible. The extension of ITSs to ill-defined domains constitutes a challenge. In this paper, we propose the provision of feedback based on solution spaces which are automatically clustered by machine learning techniques operating on sets of student solutions. We validated our approach in an expert evaluation with a data set from a programming course. The evaluation confirmed the feasibility of the proposed feedback provision strategies.

[1]  Barbara Hammer,et al.  Relational Neural Gas , 2007, KI.

[2]  Barbara Hammer,et al.  Topographic Mapping of Large Dissimilarity Data Sets , 2010, Neural Computation.

[3]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  Stephen E. Robertson,et al.  Understanding inverse document frequency: on theoretical arguments for IDF , 2004, J. Documentation.

[6]  Rebecca Nugent,et al.  Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers , 2010, EDM.

[7]  R. Duin,et al.  The dissimilarity representation for pattern recognition , a tutorial , 2009 .

[8]  Paul M. B. Vitányi,et al.  Clustering by compression , 2003, IEEE Transactions on Information Theory.

[9]  Sami Surakka,et al.  Plaggie: GNU-licensed source code plagiarism detection engine for Java exercises , 2006, Baltic Sea '06.

[10]  Chen-Lin C. Kulik,et al.  Timing of Feedback and Verbal Learning , 1988 .

[11]  Lutz Prechelt,et al.  JPlag: Finding plagiarisms among a set of programs , 2000 .

[12]  P. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 1999 .

[13]  Wei Wang,et al.  Learning portfolio analysis and mining in SCORM compliant environment , 2004, 34th Annual Frontiers in Education, 2004. FIE 2004..

[14]  Engelbert Mephu Nguifo,et al.  Learning task models in ill-defined domain using an hybrid knowledge discovery framework , 2011, Knowl. Based Syst..

[15]  Judy Kay,et al.  Clustering and Sequential Pattern Mining of Online Collaborative Learning Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[16]  Vincent Aleven,et al.  Concepts, Structures, and Goals: Redefining Ill-Definedness , 2009, Int. J. Artif. Intell. Educ..

[17]  Gilles Bisson,et al.  Inducing High-Level Behaviors from Problem-Solving Traces Using Machine-Learning Tools , 2007, IEEE Intelligent Systems.

[18]  Zachary A. Pardos,et al.  Spectral Clustering in Educational Data Mining , 2011, EDM.

[19]  Tom Murray,et al.  Authoring Tools for Advanced Technology Learning Environments , 2003 .

[20]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[21]  Erica Melis.  Choice of Feedback Strategies , 2005, CELDA.

[22]  Michelene T. H. Chi,et al.  Self-Explanations: How Students Study and Use Examples in Learning To Solve Problems. Technical Report No. 9. , 1987 .

[23]  Christian D. Schunn,et al.  Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system , 2007, Comput. Educ..

[24]  Sinclair Goodlad,et al.  Peer Tutoring: A Guide to Learning by Teaching , 1989 .

[25]  J. Gregory Trafton,et al.  Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems , 1992 .

[26]  Dinh Phung,et al.  Journal of Machine Learning Research: Preface , 2014 .