An intelligent learning diagnosis system for Web-based thematic learning platform

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners' past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners' final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based ''theme-based learning'' model.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  V. Vapnik.  The Support Vector Method of Function Estimation , 1998 .

[3]  Alan S. Morris,et al.  A fuzzy expert system for fault detection in statistical process control of industrial processes , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[4]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[5]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[6]  Hang Joon Kim,et al.  Support Vector Machines for Texture Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Hamdy A. Taha,et al.  Operations research: an introduction / Hamdy A. Taha , 1982 .

[8]  Colin Depradine.  Expert system for extracting syntactic information from Java code , 2003, Expert Syst. Appl..

[9]  Jia-Sheng Heh,et al.  Learning and diagnosis of individual and class conceptual perspectives: an intelligent systems approach using clustering techniques , 2005, Comput. Educ..

[10]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  T.L. Hein,et al.  Teaching to students' learning styles: approaches that work , 1999, FIE'99 Frontiers in Education. 29th Annual Frontiers in Education Conference. Designing the Future of Science and Engineering Education. Conference Proceedings (IEEE Cat. No.99CH37011.

[13]  Stephen R. Garner,et al.  WEKA: The Waikato Environment for Knowledge Analysis , 1996 .

[14]  Tharam S. Dillon,et al.  An improved naive Bayesian classifier technique coupled with a novel input solution method [rainfall prediction] , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[15]  Siu-Ming Yiu,et al.  SmartTutor: An intelligent tutoring system in web-based adult education , 2003, J. Syst. Softw..

[16]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[17]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[18]  Guodong Guo,et al.  Content-based audio classification and retrieval by support vector machines , 2003, IEEE Trans. Neural Networks.

[19]  Grammatiki Tsaganou,et al.  Evaluating an intelligent diagnosis system of historical text comprehension , 2003, Expert Syst. Appl..

[20]  Hui-Mei Wang,et al.  Effects of confidence scores and remedial instruction on prepositions learning in adaptive hypermedia , 2004, Comput. Educ..

[21]  Vladimir Cherkassky,et al.  Vapnik-Chervonenkis (VC) learning theory and its applications , 1999 .

[22]  Pradipta Kishore Dash,et al.  Classification of power system disturbances using a fuzzy expert system and a Fourier linear combiner , 2000 .

[23]  Alexander J. Smola,et al.  Classification in a normalized feature space using support vector machines , 2003, IEEE Trans. Neural Networks.

[24]  Jane H. Leuthold.  Is computer-based learning right for everyone? , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[25]  Karen L. Murphy,et al.  Constructivism and collaboration on the Internet: case study of a graduate class experience , 1995 .