Intelligent fuzzy spelling evaluator for e-Learning systems

Evaluating Learners’ Response in an e-Learning environment has been the topic of current research in areas of Human Computer Interaction, e-Learning, Education Technology and even Natural Language Processing. The current paper presents a twofold strategy to evaluate single word response of a learner in an e-Learning environment. The response of the learner to be evaluated would consist of errors committed due to lack of knowledge and also out of inadvertent mistakes committed while typing the answers. The proposed system benevolently considers such errors and still marks the learner partially. The feature incorporated in this work adds the human element to the mechanised system of evaluation and assessment in an e-Learning environment.

[1]  Martin Bush.  ALTERNATIVE MARKING SCHEMES FOR ON-LINE MULTIPLE- CHOICE TESTS , 1999 .

[2]  Patrick F. Reidy.  An Introduction to Latent Semantic Analysis , 2009 .

[3]  Stephen G. Pulman,et al.  Automatic Short Answer Marking , 2005, ACL 2005.

[4]  Martha W. Evens,et al.  CIRCSIM-Tutor: An Intelligent Tutoring System Using Natural Language Dialogue , 1997, ANLP.

[5]  Alex Koohang,et al.  Learners' Perceptions toward the Web-based Distance Learning Activities/Assignments Portion of an Undergraduate Hybrid Instructional Model , 2003, J. Inf. Technol. Educ..

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

[7]  Peter W. Foltz,et al.  Automated Essay Scoring: Applications to Educational Technology , 1999 .

[8]  Arthur C. Graesser,et al.  Why/AutoTutor: A Test of Learning Gains from a Physics Tutor with Natural Language Dialog , 2003 .

[9]  S. Roy,et al.  Neural network based intelligent analysis of learners' response for an e-Learning environment , 2010, 2010 2nd International Conference on Education Technology and Computer.

[10]  Stephen Pulman,et al.  Auto−marking 2: An update on the UCLES−Oxford University research into using computational linguistics to score short‚ free text responses , 2004 .

[11]  Da Ruan,et al.  Fuzzy Logic: A Spectrum of Theoretical & Practical Issues , 2007 .

[12]  W. Russell Johnson,et al.  Effects of alternative positioning of open-ended questions in multiple-choice questionnaires. , 1974 .

[13]  Tim Halverson.  Integrating models of human-computer visual interaction , 2006, CHI EA '06.

[14]  Fuzzy Automata Induction using Construction Method , 2006 .

[15]  Peter Linz,et al.  An Introduction to Formal Languages and Automata , 1997 .

[16]  J.R. Bellegarda,et al.  Exploiting latent semantic information in statistical language modeling , 2000, Proceedings of the IEEE.

[17]  Arthur C. Graesser,et al.  Teaching Tactics and Dialog in AutoTutor , 2001 .

[18]  Arthur C. Graesser,et al.  Intelligent Tutoring Systems with Conversational Dialogue , 2001, AI Mag..

[19]  Minjuan Wang,et al.  Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment , 2009, J. Educ. Technol. Soc..

[20]  B. Noble,et al.  On certain integrals of Lipschitz-Hankel type involving products of bessel functions , 1955, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[21]  Arthur C. Graesser,et al.  AutoTutor: A simulation of a human tutor , 1999, Cognitive Systems Research.

[22]  Emil M. Popa,et al.  Intelligent system modeling with total fuzzy grammars , 2006 .

[23]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[24]  Samir Roy,et al.  Fuzzy Automata Inspired Intelligent Assesment of Learning Achievement , 2011, IICAI.