Analyzing academic achievement of junior high school students by an improved rough set model

Rough set theory (RST) is an emerging technique used to deal with problems in data mining and knowledge acquisition. However, the RST approach has not been widely explored in the field of academic achievement. This investigation developed an improved RST (IMRST) model, which employs linear discriminant analysis to determine a reduct of RST, and analyzed the academic achievements of junior high school students in Taiwan. An interactive interface was created so that students could answer questions to predict their academic achievement and they could learn essential skills for improving their academic achievement. Empirical results showed that the IMRST model selects crucial information from data without predetermining factors and can provide accurate rates for inference rules. Hence, the developed IMRST model is a promising alternative for analyzing academic achievement data.

[1]  Hongyun Zhang,et al.  Rough set based hybrid algorithm for text classification , 2009, Expert Syst. Appl..

[2]  Xiangyang Wang,et al.  Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[3]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[4]  Ching-Chun Shih,et al.  Learning Strategies and Other Factors Influencing Achievement via Web Courses. , 1998 .

[5]  Hongke Zhang,et al.  Linear discriminant analysis in network traffic modelling: Research Articles , 2006 .

[6]  Yuen-kuang Cliff Liao,et al.  Effects of computer-assisted instruction on students' achievement in Taiwan: A meta-analysis , 2007, Comput. Educ..

[7]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[8]  Xiangyang Wang,et al.  Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma , 2006, Comput. Methods Programs Biomed..

[9]  Daoqiang Zhang,et al.  Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA , 2005, Pattern Recognit. Lett..

[10]  Qinghua Hu,et al.  A weighted rough set based method developed for class imbalance learning , 2008, Inf. Sci..

[11]  D. Dai,et al.  Generalized Discriminant Analysis for Tumor Classification with Gene Expression Data , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[12]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[13]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[14]  Renpu Li,et al.  Mining classification rules using rough sets and neural networks , 2004, Eur. J. Oper. Res..

[15]  Folkvard Nívdal.  Home-PC usage and achievement in English , 2007 .

[16]  Glen H. Elder,et al.  Intergenerational Bonding in School: The Behavioral and Contextual Correlates of Student-Teacher Relationships , 2004 .

[17]  Masao Fukushima,et al.  Tabu search for attribute reduction in rough set theory , 2008, Soft Comput..

[18]  Zdzislaw Pawlak,et al.  Rough sets, decision algorithms and Bayes' theorem , 2002, Eur. J. Oper. Res..

[19]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[20]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[21]  Ronald D. Taylor,et al.  Family management practice, school achievement, and problem behavior in African American adolescents: Mediating processes , 2005 .

[22]  Wayne K. Hoy,et al.  School Characteristics and Educational Outcomes: Toward an Organizational Model of Student Achievement in Middle Schools , 2000 .

[23]  Rafael Bello,et al.  A model based on ant colony system and rough set theory to feature selection , 2005, GECCO '05.

[24]  Yi-Shih Chung,et al.  Analyzing heterogeneous accident data from the perspective of accident occurrence. , 2008, Accident; analysis and prevention.

[25]  D. A. Peterson.  Facilitating Education for Older Learners , 1983 .

[26]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[27]  Salvatore Greco,et al.  Rough set approach to multiple criteria classification with imprecise evaluations and assignments , 2009, Eur. J. Oper. Res..

[28]  Sun Ya-min.  Linear discriminant analysis in network traffic modeling , 2005 .

[29]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[30]  L. Pettersen.  Japan's "Cram Schools.". , 1993 .

[31]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[32]  Harold H. Wenglinsky.  How Money Matters: The Effect of School District Spending on Academic Achievement. , 1997 .

[33]  Yunde Jia,et al.  A linear discriminant analysis framework based on random subspace for face recognition , 2007, Pattern Recognit..

[34]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[35]  Qiang Shen,et al.  Finding Rough Set Reducts with Ant Colony Optimization , 2003 .

[36]  A. Goonetilleke,et al.  Assessment via discriminant analysis of soil suitability for effluent renovation using undisturbed soil columns , 2006 .

[37]  A. L. Amaral,et al.  Recognition of Protozoa and Metazoa using image analysis tools, discriminant analysis, neural networks and decision trees. , 2007, Analytica chimica acta.

[38]  Hongke Zhang,et al.  Linear discriminant analysis in network traffic modelling , 2006, Int. J. Commun. Syst..

[39]  Yeong Min Kim,et al.  Rough set algorithm for crack category determination of reinforced concrete structures , 2009, Adv. Eng. Softw..

[40]  Gift Dumedah,et al.  Minimizing effects of scale distortion for spatially grouped census data using rough sets , 2008, J. Geogr. Syst..

[41]  Frank Witlox,et al.  The application of rough sets analysis in activity-based modelling. Opportunities and constraints , 2004, Expert Syst. Appl..

[42]  D. Wiley,et al.  The Teaching—Learning Process in Elementary Schools: A Synoptic View , 1976 .

[43]  Piotr Jankowski,et al.  Discerning landslide susceptibility using rough sets , 2008, Comput. Environ. Urban Syst..

[44]  Vasilios Katos,et al.  Network intrusion detection: Evaluating cluster, discriminant, and logit analysis , 2007, Inf. Sci..

[45]  Folkvard Nævdal.  Home-PC usage and achievement in English , 2007, Comput. Educ..

[46]  Byeong Seok Ahn,et al.  The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .

[47]  Jacek Zak,et al.  Technical diagnostic of a fleet of vehicles using rough set theory , 2009, Eur. J. Oper. Res..