Prediction Model of School Readiness

Studying the school readiness is an interesting domain that has attracted the attention of the public and private sectors in education. Researchers have developed some techniques for assessing the readiness of preschool kids to start school. Here we benefit from an integrated approach which combines Data Mining (DM) and social network analysis towards a robust solution. The main objective of this study is to explore the socio-demographic variables (age, gender, parents' education, parents' work status, and class and neighbourhood peers influence), achievement data (Arithmetic Readiness, Cognitive Development, Language Development, Phonological Awareness), and data that may impact school readiness. To achieve this, we propose to apply DM techniques to predict school readiness. Real data on 306 preschool children was used from four different elementary schools: (1) Life school for Creativity and Excellence a private school located in Ramah village, (2) Sisters of Saint Joseph missionary school located in Nazareth, (3) Franciscan missionary school located in Nazareth and (4) Al-Razi public school located in Nazareth, and white-box classification methods, such as induction rules were employed. Experiments attempt to improve their accuracy for predicting which children might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification. The outcomes have been compared and the models with the best results are shown.

[1]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[2]  Sebastián Ventura,et al.  Multiple instance learning for classifying students in learning management systems , 2011, Expert Syst. Appl..

[3]  Jane Waldfogel,et al.  Maternal employment and child cognitive outcomes in the first three years of life: The NICHD study of early child care , 2002 .

[4]  V. Mcloyd,et al.  Socioeconomic disadvantage and child development. , 1998, The American psychologist.

[5]  G. Duncan,et al.  School readiness and later achievement. , 2007, Developmental psychology.

[6]  K. V. Roe,et al.  Maternal Education and Cognitive Processing at Three Months as Shown by the Infants' Vocal Response to Mother vs. Stranger , 1988 .

[7]  P. Glewwe,et al.  Developmental potential in the first 5 years for children in developing countries , 2007, The Lancet.

[8]  Sotiris B. Kotsiantis.  Educational data mining: a case study for predicting dropout-prone students , 2009, Int. J. Knowl. Eng. Soft Data Paradigms.

[9]  A. Stenner,et al.  Assessment Issues in the Testing of Children at School Entry , 2005, The Future of children.

[10]  J. Douglas Willms,et al.  Family, Classroom, and School Effects on Childrens Educational Outcomes in Latin America , 2001 .

[11]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[13]  G. Duncan,et al.  Can Family Socioeconomic Resources Account for Racial and Ethnic Test Score Gaps? , 2005, The Future of children.

[14]  Dale C. Farran,et al.  Title I funded preschools as a developmental context for children's play and verbal behaviors. , 2001 .

[15]  Sebastián Ventura,et al.  An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering , 2009, User Modeling and User-Adapted Interaction.

[16]  M. Fowler,et al.  Preschool Risk Factors as Predictors of Early School Performance , 1986, Journal of developmental and behavioral pediatrics : JDBP.

[17]  Àngela Nebot,et al.  Applying Data Mining Techniques to e-Learning Problems , 2007 .

[18]  G. Duncan,et al.  The effects of poverty on children. , 1997, The Future of children.

[19]  Sebastián Ventura,et al.  Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems , 2009, Comput. Educ..

[20]  J. Fantuzzo,et al.  Preschool competency in context: an investigation of the unique contribution of child competencies to early academic success. , 2004, Developmental psychology.

[21]  Adele Diamond,et al.  Biological processes in prevention and intervention: the promotion of self-regulation as a means of preventing school failure. , 2008 .

[22]  Jack Mostow,et al.  Some useful tactics to modify, map and mine data from intelligent tutors , 2006, Natural Language Engineering.

[23]  Dale C. Farran,et al.  Handbook of Early Childhood Intervention: Another Decade of Intervention for Children Who Are Low Income or Disabled: What Do We Know Now? , 2000 .

[24]  Alberto Salguero,et al.  Factors influencing university drop out rates , 2009, Comput. Educ..

[25]  C. Coll,et al.  Psychological and demographic correlates of early academic skill development among American Indian and Alaska Native youth: a growth modeling study. , 2007, Developmental psychology.

[26]  Vassilis Loumos,et al.  Dropout prediction in e-learning courses through the combination of machine learning techniques , 2009, Comput. Educ..

[27]  Jack Block,et al.  "Some misgivings about the Matching Familiar Figures Test as a measure of reflection-impulsivity": Commentary reply. , 1975 .

[28]  C. Rouse,et al.  Introducing the Issue , 2006 .

[29]  Carlos Márquez-Vera,et al.  Predicting School Failure and Dropout by Using Data Mining Techniques , 2013, IEEE Journal of Latin-American Learning Technologies.

[30]  Qinglin Guo,et al.  Implement web learning environment based on data mining , 2009, Knowl. Based Syst..

[31]  Sotiris B. Kotsiantis,et al.  A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education , 2010, Knowl. Based Syst..

[32]  Ralph Scott,et al.  Family Size and Learning Readiness Profiles of Socioeconomically Disadvantaged Preschool Whites. , 1975 .

[33]  Jane Waldfogel,et al.  First-year maternal employment and child outcomes: Differences across racial and ethnic groups. , 2008, Children and youth services review.

[34]  A. Sameroff,et al.  Environmental risk factors in infancy. , 1998, Pediatrics.

[35]  Christopher J. Lonigan,et al.  Home environments and young Latino children's school readiness. , 2006 .