An analysis of Haitian parents' perceptions of their children with disabilities

Parental involvement is an integral part of the educational system in the U.S. Yet, parents from non-mainstream racial/ethnic backgrounds have not fully grasped the nature of parental involvement expectations in the educational process and how these expectations may impact student achievement. The purpose of this study was to identify Haitian parents’ perceptions of their children with disabilities and the education these children were receiving. Several authors have conducted studies on parents of children with disabilities to better gain an understanding of the level of their involvement with their children’s education, their perceptions of the children, and their views on the school system (Harry, 1992a, 1992b). In this study, Haitian parents of children with disabilities were interviewed using an interview protocol. Through these interviews, this study explored 10 Haitian parents’ perceptions of their child with a disability, the education the child was receiving, their interaction with the school system, and how the disability had affected their relationship with their child and their involvement with the school. Findings of the present study revealed that these Haitian parents seldom disagreed with school personnel and did not seem to fully grasp the different methods available to address their concerns as parents of children with disabilities nor the role they were expected to play in the process. The majority did not have basic literacy skills in Creole or English. The parents in this study were overwhelmed by school written communication. Additionally, this study discovered that parents’ perceptions were guided by two core concepts: coping mechanisms and locus of control. Parents with an internal locus of control, who tended to be more educated, focused inward to find solutions to problems encountered. Those with an external locus of control relied on outside influences to resolve their problems. Parental involvement was strongly influenced by their values, beliefs, customs, and conceptual knowledge about disability; all closely aligned with culture and acculturation. Overall, these parents’ perceptions greatly influenced their thoughts and behaviors when they realized that their children with disabilities might fall short of their immigrant dreams of success they held for these children.

Analysis of Haitian Parents’ Perceptions of the Education of Their Children with Disabilities

This study explored Haitian parents’ perceptions of their children with disabilities. Findings revealed parents’ perceptions were guided by two core concepts: coping mechanisms and locus of control. Parental involvement was strongly influenced by values, beliefs, customs, and conceptual knowledge that were closely aligned with culture and acculturation.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Analysis of Students’ Misconceptions and Error Patterns in Mathematics: The Case of Fractions

This study analyzed three fifth grade students’ misconceptions and error patterns when working with equivalence, addition and subtraction of fractions. The findings revealed that students used both conceptual and procedural knowledge to solve the problems. They used pictures, gave examples, and made connections to other mathematical concepts and to daily life topics. Error patterns found include using addition and subtraction of numerators and denominators, and finding the greatest common factor.

An Analysis of Haitian Parents' Perceptions of Their Children With Disabilities

Parental involvement is an integral part of the educational system in the U.S. Yet, parents from non-mainstream racial/ethnic backgrounds have not fully grasped the nature of parental involvement expectations in the educational process and how these expectations may impact student achievement. The purpose of this study was to identify Haitian parents’ perceptions of their children with disabilities and the education these children were receiving. Several authors have conducted studies on parents of children with disabilities to better gain an understanding of the level of their involvement with their children’s education, their perceptions of the children, and their views on the school system (Harry, 1992a, 1992b). In this study, Haitian parents of children with disabilities were interviewed using an interview protocol. Through these interviews, this study explored 10 Haitian parents’ perceptions of their child with a disability, the education the child was receiving, their interaction with the school system, and how the disability had affected their relationship with their child and their involvement with the school. Findings of the present study revealed that these Haitian parents seldom disagreed with school personnel and did not seem to fully grasp the different methods available to address their concerns as parents of children with disabilities nor the role they were expected to play in the process. The majority did not have basic literacy skills in Creole or English. The parents in this study were overwhelmed by school written communication. Additionally, this study discovered that parents’ perceptions were guided by two core concepts: coping mechanisms and locus of control. Parents with an internal locus of control, who tended to be more educated, focused inward to find solutions to problems encountered. Those with an external locus of control relied on outside influences to resolve their problems. Parental involvement was strongly influenced by their values, beliefs, customs, and conceptual knowledge about disability; all closely aligned with culture and acculturation. Overall, these parents’ perceptions greatly influenced their thoughts and behaviors when they realized that their children with disabilities might fall short of their immigrant dreams of success they held for these children.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.