Running Head : Student Models Initialization in Web-based ITSs Article Type : Research paper Title : A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.

[1]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[2]  Sahibsingh A. Dudani.  The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Elaine Rich,et al.  User Modeling via Stereotypes , 1998, Cogn. Sci..

[4]  Andrew Luk,et al.  A Re-Examination of the Distance-Weighted k-Nearest Neighbor Classification Rule , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Derek Sleeman,et al.  Pixie: a shell for developing intelligent tutoring systems , 1987 .

[6]  Jakob Nielsen,et al.  Hypertext and hypermedia , 1990 .

[7]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[8]  H. Ulrich Hoppe,et al.  Deductive error diagnosis and inductive error generalization for intelligent tutoring systems , 1994 .

[9]  Mark S. Schlager,et al.  Representing a Student's Learning States and Transitions , 1995 .

[10]  Raymond J. Mooney,et al.  Refinement-based student modeling and automated bug library construction , 1996 .

[11]  Dietrich Wettschereck,et al.  Relational Instance-Based Learning , 1996, ICML.

[12]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[13]  Yasuhisa Okazaki,et al.  An Implementation of an intelligent tutoring system (ITS) on the World-Wide Web (WWW , 1996 .

[14]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[15]  Gerhard Weber,et al.  User Modeling and Adaptive Navigation Support in WWW-Based Tutoring Systems , 1997 .

[16]  Maureen Murphy,et al.  Learner Modelling for Intelligent CALL , 1997 .

[17]  Erik Duval,et al.  A Simple Model for Adaptive Courseware Navigation , 1997, WebNet.

[18]  Raymund Sison,et al.  Student Modeling and Machine Learning , 1998 .

[19]  Peter Brusilovsky,et al.  Adaptive Navigation Support in Educational Hypermedia : An Evaluation of the ISIS-Tutor , 1998 .

[20]  Erik Duval,et al.  Concepts and documents for adaptive educational hypermedia: a model and a prototype , 1998 .

[21]  Wendy Hall,et al.  An Evaluation of Adapted Hypermedia Techniques Using Static User Modelling , 1998 .

[22]  Mark K. Singley,et al.  Deploying Intelligent Tutors on the Web: An Architecture and an Example , 1999 .

[23]  Constantine D. Spyropoulos,et al.  Exploiting learning techniques for the acquisition of user stereotypes and communities , 1999 .

[24]  Elaine Rich.  Users are individuals: individualizing user models , 1999, Int. J. Hum. Comput. Stud..

[25]  Emilia Pecheanu,et al.  A Hybrid Aproach to Dynamic Course Generation on the WWW , 2000 .

[26]  Paul De Bra,et al.  Pros and Cons of Adaptive Hypermedia in Web-Based Education , 2000, Cyberpsychology Behav. Soc. Netw..

[27]  Nora Koch,et al.  SmexWeb: An adaptive web-based hypermedia teaching system , 2000 .

[28]  Judy Kay,et al.  Intelligent Tutoring Systems , 2000, Lecture Notes in Computer Science.

[29]  Trude Heift,et al.  Theoretical and Practical Considerations for Web-Based Intelligent Language Tutoring Systems , 2000, Intelligent Tutoring Systems.

[30]  Joseph E. Beck,et al.  High-Level Student Modeling with Machine Learning , 2000, Intelligent Tutoring Systems.

[31]  Alfred Kobsa,et al.  Personalised hypermedia presentation techniques for improving online customer relationships , 2001, The Knowledge Engineering Review.

[32]  Hiroaki Ogata,et al.  Neclle: Network-based communicative language-learning environment focusing on communicative gaps , 2001, Comput. Educ..

[33]  Peter Brusilovsky,et al.  User modeling and user adapted interaction , 2001 .

[34]  Barry Smyth,et al.  Personalised Intelligent Tutoring for Digital Libraries , 2001, DELOS.

[35]  Maria Virvou,et al.  Student and Instructor Models: Two Kinds of User Model and Their Interaction in an ITS Authoring Tool , 2001, User Modeling.

[36]  Wolfgang Nejdl,et al.  Adaptation in Open Corpus Hypermedia , 2001 .

[37]  Maria Virvou,et al.  Web Passive Voice Tutor: an intelligent computer assisted language learning system over the WWW , 2001, Proceedings IEEE International Conference on Advanced Learning Technologies.

[38]  Petri Nokelainen,et al.  Bayesian Modeling in an Adaptive On-Line Questionnaire for Education and Educational Research , 2001 .

[39]  M. Virvou,et al.  Initializing the student model using stereotypes and machine learning , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[40]  Gilles Brassard,et al.  CLARISSE: A Machine Learning Tool to Initialize Student Models , 2002, Intelligent Tutoring Systems.

[41]  Kalina Bontcheva,et al.  Adaptivity, Adaptability, and Reading Behaviour: Some Results from the Evaluation of a Dynamic Hypertext System , 2002, AH.

[42]  Roger Nkambou,et al.  Hierarchical Representation and Evaluation of the Student in an Intelligent Tutoring System , 2002, Intelligent Tutoring Systems.

[43]  Eduardo Guzmán,et al.  Simultaneous Evaluation of Multiple Topics in SIETTE , 2002, Intelligent Tutoring Systems.

[44]  Athanasios K. Tsakalidis,et al.  Integrating Adaptive Techniques into Virtual University Learning Environment , 2002 .

[45]  M. Virvou,et al.  Dynamically initializing the student model in a Web-based language tutor , 2002, Proceedings First International IEEE Symposium Intelligent Systems.

[46]  Alfred Kobsa,et al.  Adaptivity through Unobstrusive Learning , 2002, Künstliche Intell..

[47]  Geoffrey I. Webb,et al.  Using Decision Trees for Agent Modeling: Improving Prediction Performance , 2004, User Modeling and User-Adapted Interaction.

[48]  Maria Virvou,et al.  Human Plausible Reasoning for Intelligent Help , 1999, User Modeling and User-Adapted Interaction.

[49]  Masayuki Numao,et al.  Discovering Error Classes from Discrepancies in Novice Behaviors Via Multistrategy Conceptual Clustering , 2004, User Modeling and User-Adapted Interaction.

[50]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[51]  David N. Chin.  Empirical Evaluation of User Models and User-Adapted Systems , 2001, User Modeling and User-Adapted Interaction.

[52]  Masayuki Numao,et al.  Multistrategy Discovery and Detection of Novice Programmer Errors , 2004, Machine Learning.

[53]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[54]  Hyacinth S. Nwana,et al.  User modelling and user adapted interaction in an intelligent tutoring system , 1991, User Modeling and User-Adapted Interaction.