Educational Data Mining for Problem Identification

Students face a number of difficulties studying mathematics on all educational levels. Data mining techniques applied on educational data can allow better understanding of the large amount of challenges students meet taking mathematical courses. Many researches focus on investigating various factors causing exam failure or even drop out. They often refer to study anxiety as being one of the serious reasons for students failure in mathematics. We believe that application of methods from market basket analysis can assist for identifying students, experiencing significant difficulties in their studies.

[1]  Eyke Hüllermeier,et al.  A Note on Quality Measures for Fuzzy Asscociation Rules , 2003, IFSA.

[2]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[3]  I. Arroyo,et al.  Bayesian networks and linear regression models of students’ goals, moods, and emotions , 2010 .

[4]  Chris Cornelis,et al.  Elicitation of fuzzy association rules from positive and negative examples , 2005, Fuzzy Sets Syst..

[5]  Mykola Pechenizkiy,et al.  Handbook of Educational Data Mining , 2010 .

[6]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[7]  Noraini Idris,et al.  Exploring the Effects of TI-84 plus on Achievement and Anxiety in Mathematics. , 2006 .

[8]  M. H. Margahny,et al.  FAST ALGORITHM FOR MINING ASSOCIATION RULES , 2014 .

[9]  Mohammed J. Zaki.  Generating non-redundant association rules , 2000, KDD '00.

[10]  Chris Cornelis,et al.  Mining Positive and Negative Fuzzy Association Rules , 2004, KES.

[11]  Gerd Stumme,et al.  Mining frequent patterns with counting inference , 2000, SKDD.

[12]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..

[13]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[14]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[15]  Claudio Carpineto,et al.  Concept data analysis - theory and applications , 2004 .

[16]  Jiangmin Xu,et al.  The causal ordering of mathematics anxiety and mathematics achievement: a longitudinal panel analysis. , 2004, Journal of adolescence.

[17]  Mark Sweiger,et al.  Clickstream Data Warehousing , 2002 .

[18]  M. Erden.  PREDICTIVE POWER OF MATH ANXIETY AND PERCEIVED SOCIAL SUPPORT FROM TEACHER FOR PRIMARY STUDENTS' MATHEMATICS ACHIEVEMENT , 2010 .

[19]  Daniel Sánchez,et al.  Mining association rules with improved semantics in medical databases , 2001, Artif. Intell. Medicine.