Teachers' knowledge, awareness, and pedagogy of global education in secondary schools

In 1979, the Florida State Board of Education approved the teaching of global education in the state of Florida. The purpose of this study was to examine the factors that contributed to teachers' global knowledge, global mindedness, and pedagogy in global education. The Hanvey model of teaching from a global perspective was the theoretical framework for the study. ^ A total of 90 secondary teachers from Miami-Dade County Public Schools were randomly selected and placed in three groups: Globally Oriented Social Studies Program (GOSSE), Non-Globally Oriented Social Studies Program (non-GOSSE), and Teachers Who Teach Other Subjects (TWTOS). Seven teachers, two of whom team-taught a class, were selected for classroom observations and interviews. A mixed methods design that combined quantitative and qualitative data was used. ANOVA and Chi square techniques were used to determine whether the factors that contributed to teachers' global knowledge and global mindedness differ among groups. Classroom observations and interviews were conducted to determine whether the instructional strategies differ among the seven selected teachers. ^ The findings of the study show that teachers who were trained in teaching from a global perspective differed in their global knowledge and used more appropriate instructional strategies than teachers who were not trained in teaching global perspectives. There was no significant difference in the combined global knowledge of the non-GOSSE and TWTOS groups when compared with the GOSSE group. There was no significant difference in the combined global knowledge of the GOSSE and non-GOSSE groups when compared with the TWTOS group. There was no significant difference among the teachers in their global mindedness. Observation and interview data indicate that current events, role-playing, simulations, open-ended discussion, debates, and projects were the predominant instructional strategies used by globally trained teachers. Cable networks, Internet, magazines, and newspapers were found to be the dominant tools for teaching global education. ^ This study concluded that teachers who were trained in globally oriented programs had more global knowledge than teachers who were not. It is recommended that teacher education programs should incorporate a global perspective in the preparation of social studies teachers, with particular attention to developing their global attitudes. ^

Space division multiple access for wireless sensor networks

This dissertation proposed a self-organizing medium access control protocol (MAC) for wireless sensor networks (WSNs). The proposed MAC protocol, space division multiple access (SDMA), relies on sensor node position information and provides sensor nodes access to the wireless channel based on their spatial locations. SDMA divides a geographical area into space divisions, where there is one-to-one map between the space divisions and the time slots. Therefore, the MAC protocol requirement is the sensor node information of its position and a prior knowledge of the one-to-one mapping function. The scheme is scalable, self-maintaining, and self-starting. It provides collision-free access to the wireless channel for the sensor nodes thereby, guarantees delay-bounded communication in real time for delay sensitive applications. This work was divided into two parts: the first part involved the design of the mapping function to map the space divisions to the time slots. The mapping function is based on a uniform Latin square. A Uniform Latin square of order k = m 2 is an k x k square matrix that consists of k symbols from 0 to k-1 such that no symbol appears more than once in any row, in any column, or in any m x in area of main subsquares. The uniqueness of each symbol in the main subsquares presents very attractive characteristic in applying a uniform Latin square to time slot allocation problem in WSNs. The second part of this research involved designing a GPS free positioning system for position information. The system is called time and power based localization scheme (TPLS). TPLS is based on time difference of arrival (TDoA) and received signal strength (RSS) using radio frequency and ultrasonic signals to measure and detect the range differences from a sensor node to three anchor nodes. TPLS requires low computation overhead and no time synchronization, as the location estimation algorithm involved only a simple algebraic operation.

Trust based security mechanisms for wireless sensor networks

Wireless sensor networks are emerging as effective tools in the gathering and dissemination of data. They can be applied in many fields including health, environmental monitoring, home automation and the military. Like all other computing systems it is necessary to include security features, so that security sensitive data traversing the network is protected. However, traditional security techniques cannot be applied to wireless sensor networks. This is due to the constraints of battery power, memory, and the computational capacities of the miniature wireless sensor nodes. Therefore, to address this need, it becomes necessary to develop new lightweight security protocols. This dissertation focuses on designing a suite of lightweight trust-based security mechanisms and a cooperation enforcement protocol for wireless sensor networks. This dissertation presents a trust-based cluster head election mechanism used to elect new cluster heads. This solution prevents a major security breach against the routing protocol, namely, the election of malicious or compromised cluster heads. This dissertation also describes a location-aware, trust-based, compromise node detection, and isolation mechanism. Both of these mechanisms rely on the ability of a node to monitor its neighbors. Using neighbor monitoring techniques, the nodes are able to determine their neighbors’ reputation and trust level through probabilistic modeling. The mechanisms were designed to mitigate internal attacks within wireless sensor networks. The feasibility of the approach is demonstrated through extensive simulations. The dissertation also addresses non-cooperation problems in multi-user wireless sensor networks. A scalable lightweight enforcement algorithm using evolutionary game theory is also designed. The effectiveness of this cooperation enforcement algorithm is validated through mathematical analysis and simulation. This research has advanced the knowledge of wireless sensor network security and cooperation by developing new techniques based on mathematical models. By doing this, we have enabled others to build on our work towards the creation of highly trusted wireless sensor networks. This would facilitate its full utilization in many fields ranging from civilian to military applications.

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.

Trust Based Security Mechanisms for Wireless Sensor Networks

Wireless sensor networks are emerging as effective tools in the gathering and dissemination of data. They can be applied in many fields including health, environmental monitoring, home automation and the military. Like all other computing systems it is necessary to include security features, so that security sensitive data traversing the network is protected. However, traditional security techniques cannot be applied to wireless sensor networks. This is due to the constraints of battery power, memory, and the computational capacities of the miniature wireless sensor nodes. Therefore, to address this need, it becomes necessary to develop new lightweight security protocols. This dissertation focuses on designing a suite of lightweight trust-based security mechanisms and a cooperation enforcement protocol for wireless sensor networks. This dissertation presents a trust-based cluster head election mechanism used to elect new cluster heads. This solution prevents a major security breach against the routing protocol, namely, the election of malicious or compromised cluster heads. This dissertation also describes a location-aware, trust-based, compromise node detection, and isolation mechanism. Both of these mechanisms rely on the ability of a node to monitor its neighbors. Using neighbor monitoring techniques, the nodes are able to determine their neighbors’ reputation and trust level through probabilistic modeling. The mechanisms were designed to mitigate internal attacks within wireless sensor networks. The feasibility of the approach is demonstrated through extensive simulations. The dissertation also addresses non-cooperation problems in multi-user wireless sensor networks. A scalable lightweight enforcement algorithm using evolutionary game theory is also designed. The effectiveness of this cooperation enforcement algorithm is validated through mathematical analysis and simulation. This research has advanced the knowledge of wireless sensor network security and cooperation by developing new techniques based on mathematical models. By doing this, we have enabled others to build on our work towards the creation of highly trusted wireless sensor networks. This would facilitate its full utilization in many fields ranging from civilian to military applications.

Teachers' knowledge, awareness, and pedagogy of global education in secondary schools

Professor Mohammed K. Farouk, Major Professor In 1979, the Florida State Board of Education approved the teaching of global education in the state of Florida. The purpose of this study was to examine the factors that contributed to teachers' global knowledge, global mindedness, and pedagogy in global education. The Hanvey model of teaching from a global perspective was the theoretical framework for the study. A total of 90 secondary teachers from Miami-Dade County Public Schools were randomly selected and placed in three groups: Globally Oriented Social Studies Program (GOSSE), Non-Globally Oriented Social Studies Program (non-GOSSE), and Teachers Who Teach Other Subjects (TWTOS). Seven teachers, two of whom team-taught a class, were selected for classroom observations and interviews. A mixed methods design that combined quantitative and qualitative data was used. ANOVA and Chi square techniques were used to determine whether the factors that contributed to teachers' global knowledge and global mindedness differ among groups. Classroom observations and interviews were conducted to determine whether the instructional strategies differ among the seven selected teachers. The findings of the study show that teachers who were trained in teaching from a global perspective differed in their global knowledge and used more appropriate instructional strategies than teachers who were not trained in teaching global perspectives. There was no significant difference in the combined global knowledge of the non-GOSSE and TWTOS groups when compared with the GOSSE group. There was no significant difference in the combined global knowledge of the GOSSE and non- GOSSE groups when compared with the TWTOS group. There was no significant difference among the teachers in their global mindedness. Observation and interview data indicate that current events, role-playing, simulations, open-ended discussion, debates, and projects were the predominant instructional strategies used by globally trained teachers. Cable networks, Internet, magazines, and newspapers were found to be the dominant tools for teaching global education. This study concluded that teachers who were trained in globally oriented programs had more global knowledge than teachers who were not. It is recommended that teacher education programs should incorporate a global perspective in the preparation of social studies teachers, with particular attention to developing their global attitudes.

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.