Case-Based Reasoning and Profiling System for Learning Mathematics (CBR-PROMATH)

This paper discusses the architecture of a case-based reasoning profiling system for learning mathematics (CBR-PROMATH). The adaptive system has the ability to suggest suitable learning materials based on previous cases of learner profiles and individual learning styles. The developed learning materials use a learning tool which consists of so-called mastery, understanding, interpersonal and self-expressive styles. Two sets of experiments were carried out to test the system’s functionality. The first consisted of 10 sets of learners’ profile cases, stored previously in the database. The second presented the system with 10 real new cases. The system compared and calculated similarity values between the new and stored cases. The learning material that was most similar was presented as a solution for the new case. The experiment showed that the CBR algorithm was successfully applied in the development of the CBR-PROMATH.

[1]  Hong Kian Sam,et al.  Status of mathematics teaching and learning in Malaysia , 2009 .

[2]  Kyndra V. Middleton,et al.  Examining the Relationship Between Learning Style Preferences and Attitudes Toward Mathematics Among Students in Higher Education , 2013 .

[3]  Disparity of Learning Styles and Cognitive Abilities in Vocational Education , 2014 .

[4]  Paulo Alves,et al.  Case-Based Reasoning Approach to Adaptive Web-Based Educational Systems , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[5]  T. Ravi,et al.  An Architectural-model for Context aware Adaptive Delivery of Learning Material , 2013 .

[6]  Simon C. K. Shiu,et al.  Case-Based Reasoning: Concepts, Features and Soft Computing , 2004, Applied Intelligence.

[7]  Wei Meng,et al.  IMPLICIT DETECTION OF LEARNING STYLES - THE SMALT WAY , 2013 .

[8]  Reid Swanson,et al.  Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling , 2012, TIIS.

[9]  Ralph Bergmann,et al.  DOI: 10.1017/S000000000000000 Printed in the United Kingdom Representation in case-based reasoning , 2022 .

[11]  J. Hodgen,et al.  The Employment Equation: Why Our Young People Need More Maths for Today's Jobs. , 2013 .

[12]  Patricia Anthony,et al.  Learning How to Program in C Using Adaptive Hypermedia System , 2013 .

[13]  Abdolhossein Sarrafzadeh,et al.  "How do you know that I don't understand?" A look at the future of intelligent tutoring systems , 2008, Comput. Hum. Behav..

[14]  Zhijun Yan,et al.  Personalized recommendation for learning resources based-on case reasoning agents , 2011, 2011 International Conference on Electrical and Control Engineering.

[15]  Atakan Aral,et al.  Learning Styles for K-12 Mathematics e-Learning , 2012, CSEDU.

[16]  Gwo-Jen Hwang,et al.  A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems , 2013, J. Educ. Technol. Soc..

[17]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[18]  Sabine Graf,et al.  Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles , 2007 .

[19]  Philippe Trigano,et al.  A Model of Adaptive e-learning Hypermedia System based on Thinking and Learning Styles , 2013, MUE 2013.

[20]  Paulo Alves.  Advances in artificial intelligence to model student-centred VLEs , 2010 .

[21]  Hamidreza Kashefi,et al.  Creative Problem Solving in Engineering Mathematics through Computer-Based Tools , 2013 .

[22]  Janet L. Kolodner,et al.  An introduction to case-based reasoning , 1992, Artificial Intelligence Review.

[23]  Hwa-Shan Huang,et al.  Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach , 2007, Expert Syst. Appl..