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A Personalized Learning System with Adaptive Content Presentation and Affective Evaluation Facilities

by Rejaul Karim Barbhuiya, Khurram Mustafa, Suraiya Jabin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 26
Year of Publication: 2013
Authors: Rejaul Karim Barbhuiya, Khurram Mustafa, Suraiya Jabin
10.5120/12230-8360

Rejaul Karim Barbhuiya, Khurram Mustafa, Suraiya Jabin . A Personalized Learning System with Adaptive Content Presentation and Affective Evaluation Facilities. International Journal of Computer Applications. 70, 26 ( May 2013), 10-15. DOI=10.5120/12230-8360

@article{ 10.5120/12230-8360,
author = { Rejaul Karim Barbhuiya, Khurram Mustafa, Suraiya Jabin },
title = { A Personalized Learning System with Adaptive Content Presentation and Affective Evaluation Facilities },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 26 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number26/12230-8360/ },
doi = { 10.5120/12230-8360 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:53.814172+05:30
%A Rejaul Karim Barbhuiya
%A Khurram Mustafa
%A Suraiya Jabin
%T A Personalized Learning System with Adaptive Content Presentation and Affective Evaluation Facilities
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 26
%P 10-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An Intelligent Tutoring System (ITS) should be able to select appropriate chunks of learning materials as well as evaluate learning outcomes while keeping in mind learner's various meta-cognitive and meta-affective factors. But literature review suggests that such systems are rare as they are complex and time consuming to develop. We have designed an adaptive intelligent tutoring system which is being implemented as a rules-based-expert-system for the dual purpose of - i) adaptive content selection and ii) evaluation of learning gain along with remedial actions. The system is in implementation stage and through this work, we inform in details about the developmental strategies adopted, e. g. , use of Java Expert System Shell (JESS) for rules and fact base, Apachetomcat- server for Java implementation. This work also highlights the rule based implementation of domain and affective planner along with details about the rules in textual formats. Our student model is able to recognize learner's guessing (gaming) behavior, interest, independence, and confidence level. It can also differentiate - a learner's incorrect answer due to a guess from that due to lack of sufficient domain knowledge. This framework can be used as a guiding principle to build a more robust tutoring system by incorporating other student modeling attributes.

References
  1. Ieee learning technologies standards committee, learning object metadata (lom) working group 12. http://www. ieeeltsc. org:8080/Plone/workinggroup/ learning-object-metadata-working-group- 12/learningobject-metadata-lom-working-group-12, 2007.
  2. Vincent Aleven, Bruce Mclaren, Ido Roll, and Kenneth Koedinger. Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education, 16(2):101–128, April 2006.
  3. Ivon Arroyo and Beverly Park Woolf. Inferring learning and attitudes from a bayesian network of log file data. In Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pages 33–40, Amsterdam, The Netherlands, The Netherlands, 2005. IOS Press.
  4. Ryan S. J. D. Baker, Sujith M. Gowda, Michael Wixon, Jessica Kalka, Angela Z. Wagner, Aatish Salvi, Vincent Aleven, Gail Kusbit, Jaclyn Ocumpaugh, and Lisa Rossi. Sensor-free automated detection of affect in a cognitive tutor for algebra. In EDM, pages 126–133, 2012.
  5. Amal Battou, Ali El Mezouary, Chihab Cherkaoui, and Driss Mammass. An adaptive learning system architecture based on a granular learning object framework. International Journal of Computer Applications, 32(5):18–27, 2011.
  6. Elizabeth J. Brown, Timothy J. Brailsford, Tony Fisher, and Adam Moore. Evaluating learning style personalization in adaptive systems: Quantitative methods and approaches. IEEE Trans. Learn. Technol. , 2:10–22, January 2009.
  7. Rafael A. Calvo and Sidney DMello. Affect detection: An interdisciplinary review of models, methods, and theirapplications. IEEE Transactions on Affective Computing, 1(1):18–37, 2010.
  8. Hyun Jin Cha, Yong Se Kim, Seon Hee Park, Tae Bok Yoon, Young Mo Jung, and Jee-Hyong Lee. Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system. In Intelligent Tutoring Systems'06, pages 513–524, 2006.
  9. C. Conati, R. Chabbal, and H. Maclaren. A Study on Using Biometric Sensors for Detecting User Emotions in Educational Games. In Proceedings of the Workshop Assessing and Adapting to User Attitude and Affects: Why, When and How? In conjunction with UM '03, 9th International Conference on User Modeling, 2003.
  10. Cristina Conati and Heather Maclaren. Empirically building and evaluating a probabilistic model of user affect. User Model. User-Adapt. Interact. , 19(3):267–303, 2009.
  11. Keeley A. Crockett, Annabel Latham, David McLean, Zuhair Bandar, and James O'Shea. On predicting learning styles in conversational intelligent tutoring systems using fuzzy classification trees. In FUZZ-IEEE, pages 2481– 2488. IEEE, 2011.
  12. Michel Desmarais and Ryan S. J. D. Baker. A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, pages 1–30, 2011.
  13. Sidney DMello and Art Graesser. Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145 – 157, 2012.
  14. Sidney K. D'Mello, Scotty D. Craig, Amy Witherspoon, Bethany Mcdaniel, and Arthur Graesser. Automatic detection of learner's affect from conversational cues. User Modeling and User-Adapted Interaction, 18(1-2):45–80, February 2008.
  15. S. K. DMello, R. S. Taylor, and A. Graesser. Monitoring affective trajectories during complex learning. In Proceedings of 29th Annual Meeting of the Cognitive Science Society, pages 203–208, Austin, TX, 2007.
  16. Richard M. Felder and Rebecca Brent. Understanding student differences. Journal of Engineering Education, 94:57– 72, 2005.
  17. K. Forbes-Riley and D. J. Litman. Predicting emotion in spoken dialogue from multiple knowledge sources. In Proceedings of Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), pages 201–208, 2004.
  18. Charles L. Forgy. Rete: a fast algorithm for the many pattern/ many object pattern match problem. Artificial Intelligence, page 1737, 1982.
  19. Ernest Friedman-Hill. Jess in Action: Java Rule-Based Systems. Manning Publications Co. , Greenwich, CT, USA, 2003.
  20. Patricio Garc´ýa, Anal´ýa Amandi, Silvia Schiaffino, and Marcelo Campo. Evaluating bayesian networks' precision for detecting students' learning styles. Comput. Educ. , 49:794–808, November 2007.
  21. P. Honey and A Mumford. The manual of learning styles. Peter Honey Publications, 1992.
  22. Kenneth R. Koedinger, John R. Anderson, William H. Hadley, and Mary A. Mark. Intelligent tutoring goes to school in the big city. Int. Journal of Artificial Intelligence in Education, 3:30–43, 1997.
  23. Panayiotis Kyriakou, Ioannis Hatzilygeroudis, and John Garofalakis. A tool for managing domain knowledge and helping tutors in intelligent tutoring systems. Journal of Universal Computer Science, 16(19):2841–2861, oct 2010.
  24. Scott W. McQuiggan, Bradford W. Mott, and James C. Lester. Modeling self-efficacy in intelligent tutoring systems: An inductive approach. User Modeling and User- Adapted Interaction, 18:81–123, 2008.
  25. A. Mitrovic, K. R. Koedinger, and B. Martin. A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modelling. In P. Brusilovsky, A. Corbett, and F. d. Rosis, editors, UM 2003, volume 2702 of LNAI, pages 313–322, Berlin, 2003. Springer-Verlag.
  26. Antonija Mitrovic, Michael Mayo, Pramuditha Suraweera, and Brent Martin. Constraint-based tutors: A success story. In Lszl Monostori, Jzsef Vncza, and Moonis Ali, editors, Engineering of Intelligent Systems, volume 2070 of Lecture Notes in Computer Science, pages 931–940. Springer Berlin Heidelberg, 2001.
  27. Antonija Mitrovic and Stellan Ohlsson. Evaluation of a constraint-based tutor for a database language. International Journal of Artificial Intelligence in Education, 10(3):238–256, 1999.
  28. Selene Mota and Rosalind W. Picard. Automated posture analysis for detecting learners interest level. In Conference on Computer Vision and Pattern Recognition. Workshop on Computer Vision and Pattern Recognition for Human- Computer Interaction, volume 5, page 49. IEEE Computer Society Press, Madison, 2003.
  29. I. B. Myers and M. H. McCaulley. Manual: A guide to the development and use of the Myers-Briggs type indicator. Consulting Psychologists Press, Palo Alto, CA, 1985.
  30. Dodds Philip and E. Thropp Schawn. Sharable Content Object Reference Model (SCORM) 2004 3rd Edition Conformance Requirements Version 1. 0. Advanced Distributed Learning (ADL), 3rd edition.
  31. Jennifer Sabourin, Bradford Mott, and James C. Lester. Generalizing models of student affect in game-based learning environments. In Sidney DMello, Arthur Graesser, Bjrn Schuller, and Jean-Claude Martin, editors, Affective Computing and Intelligent Interaction, volume 6975 of Lecture Notes in Computer Science, pages 588–597. Springer Berlin Heidelberg, 2011.
  32. G. Santos and A. Figueira. Web-based intelligent tutoring systems using the scorm 2004 specification - a conceptual framework for implementing scorm compliant intelligent web-based learning environments. In Advanced Learning Technologies (ICALT), 2010 IEEE 10th International Conference on, pages 676–678, 2010.
  33. Teresa Del Soldato and Benedict du Boulay. Implementation of motivational tactics in tutoring systems. J. Artif. Intell. Educ. , 6:337–378, January 1995.
  34. Kurt VanLehn, Collin Lynch, Kay Schulze, Joel A. Shapiro, Robert Shelby, Linwood Taylor, Don Treacy, Anders Weinstein, and Mary Wintersgill. The andes physics tutoring system: Five years of evaluations. In Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pages 678–685, Amsterdam, The Netherlands, The Netherlands, 2005. IOS Press.
  35. Angel De Vicente and Helen Pain. Motivation diagnosis in intelligent tutoring systems. In Proceedings of the 4th International Conference on Intelligent Tutoring Systems, ITS '98, pages 86–95, London, UK, UK, 1998. Springer- Verlag.
  36. Angel De Vicente and Helen Pain. Informing the detection of the students' motivational state: An empirical study. In Proceedings of the 6th International Conference on Intelligent Tutoring Systems, ITS '02, pages 933–943, London, UK, 2002. Springer-Verlag.
  37. J. E. Villaverde, D. Godoy, and A. Amandi. Learning styles' recognition in e-learning environments with feedforward neural networks. Journal of Computer Assisted Learning, 22(3):197–206, 2006.
  38. Qiao Xiangjie, Wang Zhiliang, Yu Jun, and Meng Xiuyan. An affective intelligent tutoring system based on artificial psychology. In Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3, ICICIC '06, pages 402–405, Washington, DC, USA, 2006. IEEE Computer Society.
Index Terms

Computer Science
Information Sciences

Keywords

Student Model Affect Gaming Guess Learning Performance JESS Physical Sensor Learning Objective

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