Abstract: Recent research in neural networks science and
neuroscience for modeling complex time series data and statistical
learning has focused mostly on learning from high input space and
signals. Local linear models are a strong choice for modeling local
nonlinearity in data series. Locally weighted projection regression is
a flexible and powerful algorithm for nonlinear approximation in
high dimensional signal spaces. In this paper, different learning
scenario of one and two dimensional data series with different
distributions are investigated for simulation and further noise is
inputted to data distribution for making different disordered
distribution in time series data and for evaluation of algorithm in
locality prediction of nonlinearity. Then, the performance of this
algorithm is simulated and also when the distribution of data is high
or when the number of data is less the sensitivity of this approach to
data distribution and influence of important parameter of local
validity in this algorithm with different data distribution is explained.
Abstract: Although several factors that affect learning to
program have been identified over the years, there continues to be no
indication of any consensus in understanding why some students learn
to program easily and quickly while others have difficulty. Seldom
have researchers considered the problem of how to help the students
enhance the programming learning outcome. The research had been
conducted at a high school in Taiwan. Students participating in the
study consist of 330 tenth grade students enrolled in the Basic
Computer Concepts course with the same instructor. Two types of
training methods-instruction-oriented and exploration-oriented were
conducted. The result of this research shows that the
instruction-oriented training method has better learning performance
than exploration-oriented training method.