The Latin American Perspective in Dade County Schools: An Initial Curricular and Teacher Survey (Dialogue #65)

This paper analyzes the knowledge about Latin America that is present in the newly required 9th grade World History Course in Dade County Public Schools. Nine recommended World History textbooks are examined in terms of their Latin American content. Also, the results of a survey questionnaire dealing with knowledge and perceptions of Latin America, which was distributed to various World History and general teachers, are discussed. The findings of this research effort while tentative, seem to indicate that there is a definite need to upgrade the Latin American knowledge base both in textbook content and among teachers. Few of the texts are considered adequate in their treatment of Latin America. Some, especially those for below average readers, present a slanted, even distorted picture of Latin American reality. While World History teachers appear to be more knowledgeable about Latin America than teachers in general, lack of knowledge and stereotyping are clearly manifested in certain persisting beliefs about the region. While this is a narrow research effort, it explores the intriguing notion that what is often considered legitimate knowledge in our classrooms can in fact be quite inadequate. The concluding section of the paper focuses on whether academic excellence is possible when there are distortions and lacunae in our classroom knowledge base.

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^