Neural network based intelligent analysis of learners' response for an e-Learning environment

This paper presents a novel neural network based scheme for intelligent analysis of learners' response for an e-Learning environment. The scheme applies to typed-in single-word textual response from the learners' end. The proposed system is intelligently adaptive to inadvertent mistakes committed by the learner while responding to the system's queries. Two-layered feed-forward neural net is employed to build the analyzer. Experimental results, carried out on a wide variety of sample responses show that the proposed scheme successfully simulates a human instructor communicating with the learner through single-word typed-in text.

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

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

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

[4]  Jean-Luc Gauvain,et al.  Discriminative Classifiers for Language Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[5]  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.

[6]  Felix G. Hamza-Lup,et al.  Feel the Pressure: E-learning Systems with Haptic Feedback , 2008, 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[7]  E. S. Borisov,et al.  Self-training classifier of natural-language texts , 2007 .

[8]  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..

[9]  Oleg G. Rudenko,et al.  Image compression based on the neural network art , 2008 .

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

[11]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

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

[13]  R.P. Lippmann,et al.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[14]  R. Lippmann.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[15]  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 .

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

[17]  Christopher Monterola,et al.  Characterizing the effect of seating arrangement on classroom learning using neural networks , 2009 .

[18]  P. Gaur.  Neural networks in data mining , 2018 .

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

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

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