BSD Socket API for Simulator is a project to run untouched Real World Application (RWA) binaries on the powerful modern general-purpose network simulators. BSD Socket API for Simulator is designed to eliminate most of the drawbacks of previous works. It is simulator independence, so it can make use of the powerful functionality and versatile tools provided by modern general-purpose simulators such as NS-2. It is fully compatible with BSD Socket API, so RWA can be run on it without re-linking and re-compiling. It is transparent to the RWA, so RWAs are run on BSD Socket API for Simulator as they are on normal operating systems. BSD Socket API for Simulator is built on the concept of message redirecting. It has two critical parts: shared library and customized simulator application. The shared library is loaded into the address space of RWA. On one hand, messages sent by RWA are captured by the shared library and redirected to the customized simulator application. On the other hand, messages from simulator are redirected by the customized simulator application to the shared library. BSD Socket API for Simulator has been intensively tested. The test results show that it functions as expected and it has an acceptable performance.

This body of research focuses on supporting problem-stakeholders, decision-makers and problem-solvers faced with an ill-defined and complex real world problem. An ill-defined problem has a characteristic trait of continual refinement. That is, the definition of the problem changes throughout the problem investigation and resolution process. The central theme of this research is that a support system should provide problem stakeholders with a problem definition model for constructing and manipulating a representation of the definition of the problem as they understand it. The approach adopted herein is to first develop a problem definition model for ill-defined problems— the 6-Component problem definition model. With this model, it is then possible to move on to identifying the types of changes or modifications to the problem definition that problem stakeholders, decision makers and problem solvers may wish to explore. Importantly, there must be a connection between the surface representation of the problem and the underlying implementation of the support system. This research argues that by focusing the support system around the problem definition, it is possible to reduce the mismatch between the problem objectives and the representation of the problem that the support system offers. This research uses the Unified Modelling Language to record and explore the requirements that problem stakeholders, faced with an evolving problem definition, place on a support system. The 6-Component problem definition model is then embedded within a design for an evolutionary support system. This embedding, supported by collaboration diagrams, shows how a system using the 6-Component problem definition model will support stakeholders in their exploration, evaluation and resolution of an ill-defined and complex real-world problem. A case study provides validation of the effectiveness of the 6-Component problem definition model proposed and developed in this work. The case study uses the 6-Component problem definition model as a basis for implementing the Integration Workbench, an evolutionary support system for land-use planning. Stakeholders explore, communicate, evaluate and resolve the Tasmanian Regional Forest Agreement problem with assistance from the Integration Workbench.

Interdisciplinary Project Work (PW) was introduced as an educational initiative in Singapore schools from primary to pre-university levels in 2000. PW was posited to (a) enhance perceptions and use of inter-subject connections in real-world problems, (b) promote knowledge application, and (c) provide a platform for the use of thinking skills. The main goal of this thesis is to explore how these objectives are inter-related with factors influencing the quality of group collaborative mathematical thinking processes and mathematical outcomes during a mathematically-based interdisciplinary project. In this study, high quality mathematical thinking processes occur when the flow of group interactions is purposefully directed towards the enhancement of mathematically accurate, logical, and reasonable outcomes. / A Sequential Explanatory Mixed Methods Design consisting of consecutive quantitative and qualitative data collection and analysis procedures was used to answer the seven research questions in the study. A researcher-designed mathematically-based interdisciplinary project was implemented over 14-15 weeks with 16 classes of students (aged 13-14) belonging to two educational streams (higher and average-ability) in three Singapore government secondary schools. No teaching intervention was administered. Six scales were developed for pre- and post-project measurements of students’ mathematical confidence, perception of the value of mathematics, and perception of the interconnectedness of mathematics (N = 398). Ten student-group cases (n = 38) were selected for further in-depth qualitative data collection procedures pertaining to the nature of mathematical knowledge application, use of metacognitive monitoring and regulatory strategies, and core thinking skills application during three tasks in the interdisciplinary project. / The findings of this study clearly demonstrate the complexities of using PW to promote holistic and connected use of knowledge. Five substantial contributions to research on interdisciplinary learning arise from the thesis:1. An empirical framework synthesising factors influencing the quality of group collaborative mathematical knowledge application processes and outcomes was developed.2. The social influence of the group member activating applications of core thinking skills and metacognitive monitoring and regulatory strategies is a mediating factor influencing the flow of cognitive-metacognitive group interactions, and therefore, the quality of collaborative mathematical knowledge application processes and outcomes.3. Leaders of high-stream groups who were socially non-dominant but mathematically active were more likely to apply a higher frequency of core thinking skills than group members in other roles (i.e., questioner, recorder, and encourager) during a mathematically-based interdisciplinary project.4. The types and complexities of mathematical knowledge and skills applied during a mathematically-based interdisciplinary project did not correspond with stream.5. Whilst students were more able to appreciate the use of mathematics for inter-subject learning after participating in a mathematically-based interdisciplinary project, their beliefs about inter-subject connections and efforts at making these connections only marginally changed.These outcomes enhance our understanding of the challenges involved in the successful use of interdisciplinary tasks with middle school students and provide focuses for future teacher facilitation of mathematical learning during interdisciplinary education.

The purpose of this meta-analysis was to examine the effect of situated learning on the academic performance of students with and without disabilities in inclusive general education classrooms. While previous research has reported the overall effectiveness of situated learning, relatively few studies have been conducted to investigate how situated learning influences students' academic performances in inclusive settings where students with and without disabilities work together. Moreover, although the main interest of situated learning is about how to apply basic knowledge and skills to an authentic context and, beyond this, how to transfer them into a similar but novel situation in everyday life, little has been known about its effectiveness on students' achievement in terms of knowledge transfer. In this study, a meta-analytical statistical method was employed to investigate the effect of situated learning, and its effectiveness was examined according to the three levels of knowledge transfer (knowledge acquisition, application, and transfer). A total of 19 situated-learning studies, both published and unpublished, were analyzed. Each primary study's effect sizes were calculated using Hedges' g with the bias correction and then combined into the three weighted average effect sizes regarding the levels of knowledge transfer. This meta-analytic study found that, on all of the levels of knowledge transfer, the situated learning is effective for the learning of students with and without disabilities in inclusive general education classrooms. In the random effects model, the situated instruction produced a weighted mean effect size estimate of 2.049 for knowledge acquisition, 1.836 for knowledge application, 1.185 for knowledge transfer. In addition, the percentage of students with special needs in general education classrooms had a negative influence on the effectiveness of situated learning. However, the pattern of results also showed that the proportion of students with special needs in general education classrooms does not influence as greatly the learning of knowledge transfer as it does knowledge acquisition or application. / text

Integrating environmental learning into mainstream education is an important countermeasure to address the challenges to the sustainability of the earth and children's integrated development. To be effectively integrated into mainstream education, an environmental learning program should be designed in ways that elicit the support of stakeholders, while at the same time without scarifying the environmental learning goals. The purpose of this study therefore is to explore an environmental learning model that meets the above mentioned goal using a case study design.Key principles for designing such environmental learning programs are identified first based on the theoretical framework. Then, the actual enactment of these principles in a practical setting and the effects on students in terms of environmental learning goals and traditional educational goals are explored through a case study of a pilot environmental learning program designed with these guiding principles. It presents a detailed portrait of the design process, the actual enacted curriculum, and the experiences of key stakeholders with this environmental learning program. It also evaluates this program's effects on students in environmental literacy (the environmental learning goal), academic achievement and social development (the traditional educational goals). The enactment of the guiding principles and factors that influence the enactment of this program are discussed thereafter. It concludes with the construction of the curriculum integration through environmental learning model based on the case study and a discussion of the model in light of the curriculum integration framework.

Outlets for students to develop mathematical ideas and skills to solve real-life problems and applicable situations have been neglected in secondary classrooms (Gainsburg, 2008). Designing curricula that applies real-life situations has been promoted by the National Council of Teachers of Mathematics (2000), the National Research Council (1998), and the Commission on Behavioral and Social Sciences and Education (2000) and also is an expectation of state standards for student learning (Texas Education Agency, 2009). Contrary, evidence has shown low benefits to classroom real-life examples perceived by students. This study served dual purposes: 1) Determine the relationship between place-based education and mathematics learning, and 2) Investigate teacher conceptions of place-based education opportunities in high school, mathematics curriculum. This study employed two methodologies. A mixed-methods approach was employed for the meta-analysis of place-based programs and the second employed qualitative methods of structured interviewing to determine teachers’ conceptions of place-based pedagogy. Upon completion of the study, I concluded: 1) Place-based pedagogies align toward more foundational mathematic skills (e.g. measurement, number sense) when implemented, and 2) Teachers’ conceive place-based as a general effective tool for student engagement and real-world context of how mathematics functions in society.

I denna systematiska litteraturstudie sammanställs forskning om ämnet utomhusmatematik. Insamlingen av datan har skett via sökningar i databasen ERIC (Ebsco) efter vetenskapligt granskade artiklar. Artiklarna har analyserats och resultatet av studien har gett en tydligare inblick av utomhusmatematik, men även om utomhuspedagogik. Det som återkommer är sambandet mellan matematiken och elevernas vardag, att eleverna måste förstå dessa kopplingar för att kunna utvecklas i matematiken. Studien har visat på vikten av elevdelaktighet, problemlösning samt för- respektive nackdelar med utomhusmatematik. Slutsatsen är att förståelsen för sambanden mellan matematikundervisningen och vardagen är viktig, praktisk utomhusmatematik bör förekomma oftare i skolorna samt att variation av undervisning är uppskattat. / This systematic literature review compiles research on the topic "outside mathematics". The collection of data has been made through searches within the database ERIC (Ebsco) for peer-reviewed articles. The articles have been analyzed and the results of the study have given a clearer insight on outdoor mathematics, but also about outdoor education. What recurs is the connection between mathematics and students' daily lives, which students need to understand these linkages to be developed in mathematics. The study has shown the importance of student participation, problem solving, as well as the advantages and disadvantages of outdoor mathematics. The conclusion is that the understanding of the connections between mathematics teaching and everyday life is important, practical outdoor mathematics should occur more frequently in schools, and that the variation of teaching is appreciated.

This thesis presents several novel methods to address some of the real world data modelling issues through the use of local and individualised modelling approaches. A set of real world data modelling issues such as modelling evolving processes, defining unique problem subspaces, identifying and dealing with noise, outliers, missing values, imbalanced data and irrelevant features, are reviewed and their impact on the models are analysed. The thesis has made nine major contributions to information science, includes four generic modelling methods, three real world application systems that apply these methods, a comprehensive review of the real world data modelling problems and a data analysis and modelling software. Four novel methods have been developed and published in the course of this study. They are: (1) DyNFIS – Dynamic Neuro-Fuzzy Inference System, (2) MUFIS – A Fuzzy Inference System That Uses Multiple Types of Fuzzy Rules, (3) Integrated Temporal and Spatial Multi-Model System, (4) Personalised Regression Model. DyNFIS addresses the issue of unique problem subspaces by identifying them through a clustering process, creating a fuzzy inference system based on the clusters and applies supervised learning to update the fuzzy rules, both antecedent and consequent part. This puts strong emphasis on the unique problem subspaces and allows easy to understand rules to be extracted from the model, which adds knowledge to the problem. MUFIS takes DyNFIS a step further by integrating a mixture of different types of fuzzy rules together in a single fuzzy inference system. In many real world problems, some problem subspaces were found to be more suitable for one type of fuzzy rule than others and, therefore, by integrating multiple types of fuzzy rules together, a better prediction can be made. The type of fuzzy rule assigned to each unique problem subspace also provides additional understanding of its characteristics. The Integrated Temporal and Spatial Multi-Model System is a different approach to integrating two contrasting views of the problem for better results. The temporal model uses recent data and the spatial model uses historical data to make the prediction. By combining the two through a dynamic contribution adjustment function, the system is able to provide stable yet accurate prediction on real world data modelling problems that have intermittently changing patterns. The personalised regression model is designed for classification problems. With the understanding that real world data modelling problems often involve noisy or irrelevant variables and the number of input vectors in each class may be highly imbalanced, these issues make the definition of unique problem subspaces less accurate. The proposed method uses a model selection system based on an incremental feature selection method to select the best set of features. A global model is then created based on this set of features and then optimised using training input vectors in the test input vector’s vicinity. This approach focus on the definition of the problem space and put emphasis the test input vector’s residing problem subspace. The novel generic prediction methods listed above have been applied to the following three real world data modelling problems: 1. Renal function evaluation which achieved higher accuracy than all other existing methods while allowing easy to understand rules to be extracted from the model for future studies. 2. Milk volume prediction system for Fonterra achieved a 20% improvement over the method currently used by Fonterra. 3. Prognoses system for pregnancy outcome prediction (SCOPE), achieved a more stable and slightly better accuracy than traditional statistical methods. These solutions constitute a contribution to the area of applied information science. In addition to the above contributions, a data analysis software package, NeuCom, was primarily developed by the author prior and during the PhD study to facilitate some of the standard experiments and analysis on various case studies. This is a full featured data analysis and modelling software that is freely available for non-commercial purposes (see Appendix A for more details). In summary, many real world problems consist of many smaller problems. It was found beneficial to acknowledge the existence of these sub-problems and address them through the use of local or personalised models. The rules extracted from the local models also brought about the availability of new knowledge for the researchers and allowed more in-depth study of the sub-problems to be carried out in future research.

Interdisciplinary Project Work (PW) was introduced as an educational initiative in Singapore schools from primary to pre-university levels in 2000. PW was posited to (a) enhance perceptions and use of inter-subject connections in real-world problems, (b) promote knowledge application, and (c) provide a platform for the use of thinking skills. The main goal of this thesis is to explore how these objectives are inter-related with factors influencing the quality of group collaborative mathematical thinking processes and mathematical outcomes during a mathematically-based interdisciplinary project. In this study, high quality mathematical thinking processes occur when the flow of group interactions is purposefully directed towards the enhancement of mathematically accurate, logical, and reasonable outcomes. / A Sequential Explanatory Mixed Methods Design consisting of consecutive quantitative and qualitative data collection and analysis procedures was used to answer the seven research questions in the study. A researcher-designed mathematically-based interdisciplinary project was implemented over 14-15 weeks with 16 classes of students (aged 13-14) belonging to two educational streams (higher and average-ability) in three Singapore government secondary schools. No teaching intervention was administered. Six scales were developed for pre- and post-project measurements of students’ mathematical confidence, perception of the value of mathematics, and perception of the interconnectedness of mathematics (N = 398). Ten student-group cases (n = 38) were selected for further in-depth qualitative data collection procedures pertaining to the nature of mathematical knowledge application, use of metacognitive monitoring and regulatory strategies, and core thinking skills application during three tasks in the interdisciplinary project. / The findings of this study clearly demonstrate the complexities of using PW to promote holistic and connected use of knowledge. Five substantial contributions to research on interdisciplinary learning arise from the thesis:1. An empirical framework synthesising factors influencing the quality of group collaborative mathematical knowledge application processes and outcomes was developed.2. The social influence of the group member activating applications of core thinking skills and metacognitive monitoring and regulatory strategies is a mediating factor influencing the flow of cognitive-metacognitive group interactions, and therefore, the quality of collaborative mathematical knowledge application processes and outcomes.3. Leaders of high-stream groups who were socially non-dominant but mathematically active were more likely to apply a higher frequency of core thinking skills than group members in other roles (i.e., questioner, recorder, and encourager) during a mathematically-based interdisciplinary project.4. The types and complexities of mathematical knowledge and skills applied during a mathematically-based interdisciplinary project did not correspond with stream.5. Whilst students were more able to appreciate the use of mathematics for inter-subject learning after participating in a mathematically-based interdisciplinary project, their beliefs about inter-subject connections and efforts at making these connections only marginally changed.These outcomes enhance our understanding of the challenges involved in the successful use of interdisciplinary tasks with middle school students and provide focuses for future teacher facilitation of mathematical learning during interdisciplinary education.

This thesis presents several novel methods to address some of the real world data modelling issues through the use of local and individualised modelling approaches. A set of real world data modelling issues such as modelling evolving processes, defining unique problem subspaces, identifying and dealing with noise, outliers, missing values, imbalanced data and irrelevant features, are reviewed and their impact on the models are analysed. The thesis has made nine major contributions to information science, includes four generic modelling methods, three real world application systems that apply these methods, a comprehensive review of the real world data modelling problems and a data analysis and modelling software. Four novel methods have been developed and published in the course of this study. They are: (1) DyNFIS – Dynamic Neuro-Fuzzy Inference System, (2) MUFIS – A Fuzzy Inference System That Uses Multiple Types of Fuzzy Rules, (3) Integrated Temporal and Spatial Multi-Model System, (4) Personalised Regression Model. DyNFIS addresses the issue of unique problem subspaces by identifying them through a clustering process, creating a fuzzy inference system based on the clusters and applies supervised learning to update the fuzzy rules, both antecedent and consequent part. This puts strong emphasis on the unique problem subspaces and allows easy to understand rules to be extracted from the model, which adds knowledge to the problem. MUFIS takes DyNFIS a step further by integrating a mixture of different types of fuzzy rules together in a single fuzzy inference system. In many real world problems, some problem subspaces were found to be more suitable for one type of fuzzy rule than others and, therefore, by integrating multiple types of fuzzy rules together, a better prediction can be made. The type of fuzzy rule assigned to each unique problem subspace also provides additional understanding of its characteristics. The Integrated Temporal and Spatial Multi-Model System is a different approach to integrating two contrasting views of the problem for better results. The temporal model uses recent data and the spatial model uses historical data to make the prediction. By combining the two through a dynamic contribution adjustment function, the system is able to provide stable yet accurate prediction on real world data modelling problems that have intermittently changing patterns. The personalised regression model is designed for classification problems. With the understanding that real world data modelling problems often involve noisy or irrelevant variables and the number of input vectors in each class may be highly imbalanced, these issues make the definition of unique problem subspaces less accurate. The proposed method uses a model selection system based on an incremental feature selection method to select the best set of features. A global model is then created based on this set of features and then optimised using training input vectors in the test input vector’s vicinity. This approach focus on the definition of the problem space and put emphasis the test input vector’s residing problem subspace. The novel generic prediction methods listed above have been applied to the following three real world data modelling problems: 1. Renal function evaluation which achieved higher accuracy than all other existing methods while allowing easy to understand rules to be extracted from the model for future studies. 2. Milk volume prediction system for Fonterra achieved a 20% improvement over the method currently used by Fonterra. 3. Prognoses system for pregnancy outcome prediction (SCOPE), achieved a more stable and slightly better accuracy than traditional statistical methods. These solutions constitute a contribution to the area of applied information science. In addition to the above contributions, a data analysis software package, NeuCom, was primarily developed by the author prior and during the PhD study to facilitate some of the standard experiments and analysis on various case studies. This is a full featured data analysis and modelling software that is freely available for non-commercial purposes (see Appendix A for more details). In summary, many real world problems consist of many smaller problems. It was found beneficial to acknowledge the existence of these sub-problems and address them through the use of local or personalised models. The rules extracted from the local models also brought about the availability of new knowledge for the researchers and allowed more in-depth study of the sub-problems to be carried out in future research.