Education Curriculum of Learning In Children

To develop in children a broad range of skills, including the problem solving, interpersonal and communication skills that are essential for successful living in a rapidly changing society.  The curriculum encourages student initiative by providing children with materials, equipment, and time to pursue activities they choose.  At the same time, it provides teachers with a framework for guiding children’s independent activities toward sequenced learning goals. There are seven specific types of learning styles. Visual learners prefer to learn mathematics through pictures, diagrams etc.  A well-balanced intelligent child is able to develop all the types of learning styles. The students have to understand and accept their type of learning style earlier so that learning becomes easier and less stressful in the future. But it is important to train and practice the other types of learning styles so that the children can utilize them as effectively as possible. The teacher plays a key role in instructional activities by selecting appropriate, developmentally sequenced material and by encouraging children to adopt an active problem-solving approach to learning. This teacher-student interaction teachers helping students achieve developmentally sequenced goals while also encouraging them to set many of their own goals uniquely distinguishes the High/Scope Curriculum from direct-instruction and child-centered curricula (high/Scope Educational Research Foundation, 1989).  Teachers keep notes about significant behaviors, changes, statements, and things that help them better understand a child’s way of thinking and learning.  Teachers use two mechanisms to help them collect data: the key experiences note form and a portfolio.  The High/Scope Child Observation Record is also used to assess children’s development.  According to Ronald Barnett, learning may or may not take place when a subject is taught.  While discussing this point he has presented two contrasting images of quality.  They are institutional performance and student experience, student learning or student achievement.  The teacher in his opinion is central to higher education.  Teaching may be able to improve the quality of student’s learning but the teacher should remind himself that it may also impair the quality of student’s learning.  This is partly because student’s learning strategies vary under two polarities, one between deep and surface understanding and the other between holistic and atomistic understanding of their learning experiences.  He goes on to add that for a student, learning has three distinct aspects: learning style, motivation and curriculum demands.  Therefore teachers have to pursue, beyond teaching strategies to enable their students to attain certain specific skills.

New Diagnostic Methods for Influential Observations in Linear Regression With Some Biased Estimation Methods

This thesis is concerned with the expansion of diagnostic methods in parametric regression models with some biased estimators. Of which, the Liu estimator, modified ridge estimator, improved Liu estimator and ridge estimator have been developed as an alternative to the ordinary least squares estimator in the presence of multicollinearity in linear regression models. Firstly, we introduce a type of Pena’s statistic for each point in Liu regression. Using the forecast change property, we simplify the Pena’s statistic in a numerical sense. It is found that the simplified Pena’s statistic behaves quite well as far as detection of influential observations is concerned. We express Pena’s statistic in terms of the Liu leverages and residuals. For numerical evaluation, simulated studies are given and a real data set has been analyzed for illustration. Secondly, we formulated Pena’s statistic for each point while considering the modified ridge regression estimator. Using this statistic, we showed that when modified ridge regression was used to mitigate the effects of multicollinearity, the influence of some observations could be significantly changed. The normality of this statistic was also discussed and it was proved that it could detect a subset of high modified ridge leverage outliers. The Monte Carlo simulations were used for empirical results and an example of real data was presented for illustration. Next, we introduce a type of Pena’s statistic for each point in the improved Liu estimator. Using this statistic, we showed that when the improved Liu estimator was used to mitigate the effects of multicollinearity, the influence of some observations could be significantly changed. The Monte Carlo simulations were used for empirical results and an example of real data was presented for illustration. The ridge estimator having growing and wider applications in statistical data analysis as an alternative technique to the ordinary least squares estimator to combat multicollinearity in linear regression models. In regression diagnostics, a large number of influence diagnostic methods based on numerous statistical tools have been discussed. Finally, we focus on ridge version of Nurunnabi et al. (2011) method for identification of multiple influential observation in linear regression. The efficiency of the proposed method is presented through several well-known data sets, an artificial large data with high-dimension and heterogeneous sample and a Monte Carlo simulation study.