The Pedagogy of English Teaching-Learning at Primary Level in Rural Government Schools: A Data Mining View

The Pedagogy of English Teaching-Learning at Primary Level in Rural Government Schools: A Data Mining View

P. Sunil Kumar (BPUT, India), Sateesh Kumar Pradhan (Utkal University, India) and Sachidananda Panda (BPUT, India)
DOI: 10.4018/978-1-4666-8737-0.ch006
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Abstract

English language is accepted as the global language in all walks of life today. Hence it becomes mandatory for everyone to learn English in order to be successful at the individual as well as social levels. Although Government has taken number of initiatives, it is necessary to mention that our rural schools at the primary level are adversely affected in this aspect, as the children are not properly taken care of in English teaching and learning skills. This paper is based on a survey work done amongst the students, parents and teachers by using data mining techniques like association rule mining measures and other interesting measures to reveal the facts for better implementation.
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Educational data mining has emerged as an independent research area in recent years, culminating in 2008 with the establishment of the annual International Conference on Educational Data Mining, and the Journal of Educational Data Mining. Romero and Ventura (2007) provides a comprehensive study of EDM from 1995 to 2005. It describes the need for analyzing the student data which can be used by students, educators and administrators.

Z.N. Khan (2005) found Girls with high socio-economic status were relatively higher achievers in science stream and boys with low socio-economic status were relatively higher achievers in general.

Madhyastha and Tanimoto (2009) investigated the relationship between consistency and student performance with the aim to provide guidelines for scaffolding instruction.

Beck and Mostow (2008) and Pechenizkiy et al. (2008) discovered which types of pedagogical support are most effective, either overall or for different groups of students or in different situations. McQuiggan et al. (2008),found whether students are experiencing poor self-efficiency. Baker (2007) identified students who are off-task. D'Mello et al. (2008) studied on students who are bored or frustrated. Dekker et al. (2009), Romero et al. (2008) and Superby et al. (2006) found short comings that predict student shortcoming or non-retention in college courses.

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