Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis Export

@article{stathacopoulou05, abstract = {In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neurofuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments.}, address = {New York, NY, USA}, author = {Stathacopoulou, Regina and Magoulas, George D. and Grigoriadou, Maria and Samarakou, Maria}, citeulike-article-id = {4647047}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1063232}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.ins.2004.02.026}, doi = {10.1016/j.ins.2004.02.026}, issn = {0020-0255}, journal = {Inf. Sci. Inf. Comput. Sci.}, keywords = {exploratory\_environments, fuzzy\_logic, intelligent\_support, magoulas, neural\_networks, physics, stathacopulou}, number = {2-4}, pages = {273--307}, posted-at = {2009-05-27 17:44:59}, priority = {0}, publisher = {Elsevier Science Inc.}, title = {Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis}, url = {http://dx.doi.org/10.1016/j.ins.2004.02.026}, volume = {170}, year = {2005} }

TY - JOUR ID - stathacopoulou05 L3 - citeulike-article-id:4647047 N2 - In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neurofuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments. IS - 2-4 JF - Inf. Sci. Inf. Comput. Sci. SN - 0020-0255 AD - New York, NY, USA EP - 307 TI - Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis PB - Elsevier Science Inc. VL - 170 SP - 273 KW - exploratory_environments KW - fuzzy_logic KW - intelligent_support KW - magoulas KW - neural_networks KW - physics KW - stathacopulou AU - Stathacopoulou, Regina AU - Magoulas, George AU - Grigoriadou, Maria AU - Samarakou, Maria PY - 2005/// UR - http://dx.doi.org/10.1016/j.ins.2004.02.026 ER -