The purpose of the study was to explore problems encountered in the teaching and learning of statistics in grade 11 and to offer ways of addressing them. A convenient sample of 100 grade 11 mathematics teachers and 448 grade 11 mathematics learners participated in the study. A descriptive survey design was used. Data were collected from the teachers using a teacher questionnaire, classroom observation schedule and teacher interview, and from learners through the use of a learner questionnaire, classroom observation schedule, diagnostic test and learner interview. The validity and reliability of all these instruments was established. Descriptive statistics and frequencies were used to analyse learner questionnaires, teacher questionnaires and learners‟ responses to a diagnostic test. Teacher and learner interviews were transcribed and classified according to themes. Classroom observations were analysed by using themes and checking for similarities and differences. The results showed that teachers had difficulty with the interpretation and calculation of measures of dispersion; representation and interpretation of data on graphs or plots; determining the five number summary; constructing and interpreting probability diagrams and tables; and interpreting probability terminology. Also, the results showed that learners experienced difficulties when using graphs to predict the results; interpreting and determining measures of dispersion; computation of quartiles when the total number of data values was even; representing data on graphs or plots; interpreting and determining measures of central tendency; constructing and interpreting probability graphs and tables; and interpreting probability terminology. The results found possible causes of the teachers‟ difficulties to be (1) their lack of statistics content knowledge; (2) inadequate textbooks; (3) in-service programmes which did not cover statistical topics, or which did not pay adequate attention to probability; and (4) teachers failure to attend these in-service teacher workshops. Further, the findings of the study were that the probable causes of learners‟ difficulties were (1) inadequate teaching of statistics topics in previous grades; (2) teachers‟ lack of content knowledge in statistics meant they had difficulty explaining concepts to learners; (3) inadequate learning material and learners‟ inability to use the statistics function mode on their calculators; and (4) learners‟ lack of conceptual knowledge of certain aspects of statistics. Lastly, the results of the study found that the possible ways to address the problems in the teaching and learning of statistics in grade 11 to be: (1) teachers should receive financial support from their schools/districts to attend in-service education and training programmes; (2) textbooks should be well written (provide thorough explanations) and contain all the information necessary to teach data handling and probability (i.e. formulae, more examples); (3) in-service teacher programmes should meet the needs of the teachers by offering topics that teachers find difficult to teach; and (4) more and longer inset programmes on probability, preferably five-day workshops, should be arranged. / Mathematics Education / M. Sc. (Mathematics, Science and Technology Education)

This study is concerned with how competent mathematics teachers develop pedagogical content knowledge (PCK) in statistics teaching. Pedagogical content knowledge was used as the theoretical framework that guided the research and data collection. The study’s methodology consisted of two phases. In the first phase, the six identified mathematics teachers undertook a conceptual knowledge written exercise. The result of this exercise was used to select the best four performing teachers for the second phase of the study. The second phase consisted mainly of lesson observations, interviews, written documents in the form of completed questionnaires, written diaries or reports, document analysis designed to produce rich detailed descriptions of participating teachers’ PCK in the context of teaching statistics concepts at school level. The concept mapping exercise was used to indirectly assess participating teachers’ content knowledge and their conceptions of the nature of school statistics and how it is to be taught. The qualitative data obtained were analysed to try to determine individual teachers’ content knowledge of school statistics, related pedagogical knowledge, knowledge of learners’ conceptions in statistics teaching, knowledge of learners’ learning difficulties as well as how they developed their PCK in statistics teaching. The analysis was done based on iterative coding and categorisation of responses and observations made to identify themes, patterns, and gaps, in school statistics teaching. Commonalities and differences if any, in the PCK profiles of the four participating teachers were also analysed and determined. The results of the study showed that overall, individual teachers develop their PCK in school statistics teaching by: (a) formally developing their knowledge of the subject matter in a formal undergraduate educational programme, as well as subject matter content knowledge during classroom practice; (b) using varied topic-specific instructional skills such as graphical construction skills in teaching statistical graphs; (c) using diagnostic techniques (oral questioning and pre-activity, class discussions and questioning) and a review of previous lessons to introduce lessons, and to determine learners’ preconceptions in statistics teaching ; (d) Using teaching strategies that can help to identify learners’ learning difficulties as well as intervention to address the difficulties; (e) continually updating their knowledge of school statistics by attending content knowledge workshops and other teacher development programmes designed to improve content knowledge and practice. / Thesis (PhD)--University of Pretoria, 2012. / Science, Mathematics and Technology Education / unrestricted

Indiana University-Purdue University Indianapolis (IUPUI) / The thesis extends the work done in [1] [2] by Rovnyak, et al. where the authors have described about transient event prediction and response based one shot control using decision trees trained and tested in a 176 bus model of WECC power system network. This thesis contains results from rigorous simulations performed to measure robustness of the existing one shot control subjected to missing PMU's data ranging from 0-10%. We can divide the thesis into two parts in which the first part includes understanding of the work done in [2] using another set of one-shot control combinations labelled as CC2 and the second part includes measuring their robustness while assuming missing PMU's data. Previous work from [2] involves use of decision trees for event detection based on different indices to classify a contingency as a 'Fault' or 'No fault' and another set of decision trees that decides either to actuate 'Control' or 'No control'. The actuation of control here means application of one-shot control combination to possibly bring the system to a new equilibrium point which would otherwise attain loss of synchronism. The work done in [2] also includes assessing performance of the one shot control without event detection. The thesis is organized as follows- Chapter 1 of the thesis highlights the effect of missing PMUs' data in a power system network and the need to address them appropriately. It also provides a general idea of transient stability and response of a transient fault in a power system. Chapter 2 forms the foundation of the thesis as it describes the work done in [1] [2] in detail. It describes the power system model used, contingencies set, and different indices used for decision trees. It also describes about the one shot control combination (CC1) deduced by Rovnyak, et.al. of which performance is later tested in this thesis assuming different missing data scenarios. In addition to CC1, the chapter also describes another set of control combination (CC2) whose performance is also tested assuming the same missing data scenarios. This chapter also explains about the control methodology used in [2]. Finally the performance metrics of the DTs are explained at the end of the chapter. These are the same performance metrics used in [2] to measure the robustness of the one shot control. Chapter 2 is thus more a literature review of previous work plus inclusion of few simulation results obtained from CC2 using exactly the same model and same control methodology. Chapter 3 describes different techniques of handling missing data from PMUs most of which have been used in and referred from different previous papers. Finally Chapter 4 presents the results and analysis of the simulation. The thesis is wrapped up explaining future enhancements and room for improvements.

Data classification is a novel data analysis technique that involves extracting valuable information with potential utility from databases. It has found extensive applications in various domains, including finance, insurance, government, education, transportation, and defense. There are several methods available for data classification, with decision tree algorithms being one of the most widely used. These algorithms are based on instance-based inductive learning and offer advantages such as rule extraction, low computational complexity, and the ability to highlight important decision attributes, leading to high classification accuracy. According to statistics, decision tree algorithms[1] are among the most widely utilized data mining algorithms. To address these challenges, a decision tree algorithm is employed to solve classification problems. However, the existing decision tree algorithm exhibits limitations such as low calculation efficiency and multi-valued[2] bias. Therefore, a data classification system based on an optimized decision tree algorithm written in Python and a data storage system based on PostgreSQL were developed. The proposed algorithm surpasses traditional classification algorithms in terms of dimensionality reduction, attribute selection, and scalability. Ultimately, a combined optimization decision tree classifier system is introduced, which exhibits superior performance compared to the widely used ID3[3] algorithm. The improved decision tree algorithm has both theoretical and practical significance for data mining applications. / Dataklassificering är en ny dataanalysteknik som innebär att man extraherar värdefull information med potentiell nytta från databaser. Den har hittat omfattande tillämpningar inom olika domäner, inklusive finans, försäkring, regering, utbildning, transport och försvar. Det finns flera metoder tillgängliga för dataklassificering, där beslutsträdsalgoritmer är en av de mest använda. Dessa algoritmer är baserade på instansbaserad induktiv inlärning och erbjuder fördelar som regelextraktion, låg beräkningskomplexitet och förmågan att lyfta fram viktiga beslutsattribut, vilket leder till hög klassificeringsnoggrannhet. Enligt statistik är beslutsträdsalgoritmer bland de mest använda datautvinningsalgoritmerna. För att hantera dessa utmaningar används en beslutsträdsalgoritm för att lösa klassificeringsproblem. Den befintliga beslutsträds-algoritmen uppvisar dock begränsningar såsom låg beräkningseffektivitet och flervärdig bias. Därför utvecklades ett dataklassificeringssystem baserat på en optimerad beslutsträdsalgoritm skriven i Python och ett datalagringssystem baserat på PostgreSQL. Den föreslagna algoritmen överträffar traditionella klassificeringsalgoritmer när det gäller dimensionsreduktion, attributval och skalbarhet. I slutändan introduceras ett kombinerat optimeringsbeslutsträd-klassificeringssystem, som uppvisar överlägsen prestanda jämfört med den allmänt använda ID3-algoritmen. Den förbättrade beslutsträdsalgoritmen har både teoretisk och praktisk betydelse för datautvinningstillämpningar.

Made available in DSpace on 2016-04-27T17:13:01Z (GMT). No. of bitstreams: 1 Luis Cesar Friolani.pdf: 6668241 bytes, checksum: 46895869e526f407572fc9d5867fbbc0 (MD5) Previous issue date: 2007-05-14 / Made available in DSpace on 2016-08-25T17:25:36Z (GMT). No. of bitstreams: 2 Luis Cesar Friolani.pdf.jpg: 3447 bytes, checksum: 02ba6555a715f9f4f1034dfd61fb2185 (MD5) Luis Cesar Friolani.pdf: 6668241 bytes, checksum: 46895869e526f407572fc9d5867fbbc0 (MD5) Previous issue date: 2007-05-14 / Secretaria da Educação do Estado de São Paulo / Our research had as its aims to verify the organisation of Middle-school mathematics textbooks (Grades 5 to 8) in relation to the topic Data Handling, analyse whether this organisation favours the construction of stochastic thinking and to examine whether the textbooks follow the orientation proposed in the PCN (National Curriculum Parameters). Given that the textbook tends to be the principal pedagogic support used by teachers (Lajolo, 1996 and Dante, 1996), we analyse three textbook collections using as our basis the idea of praxeological organisation (Chevallard, 1995), which involves identifying the tasks, the techniques and the theoretical-technological discourse. We also analyse the levels of statistical literacy, which, according to Shamos (1995), can be classified as cultural, functional e scientific. The results of the research indicate little exploration on the part of the authors in relations to the topic of Data Handling / Nossa pesquisa teve como objetivo verificar qual a organização que os livros didáticos do Ensino Fundamental (5ª a 8ª série) fazem, referente ao tema Tratamento da Informação e se essa organização favorece a construção do pensamento Estocástico e também se eles atendem às orientações propostas pelos PCN. Sendo o livro didático o principal apoio pedagógico dos professores (Lajolo, 1996 e Dante, 1996), analisamos três coleções de livros didáticos segundo a Organização Praxeológica (Chevallard, 1995), em que buscamos identificar as tarefas, as técnicas e o discurso teórico-tecnológico, bem como o nível de letramento estatístico que, segundo Shamos (1995), se classifica em cultural, funcional e científico. Porém os resultados dessa pesquisa indicam a pouca exploração por parte dos autores em relação ao tema Tratamento da Informação

Quantitative Techniques is traditionally a subject with a poor pass rate at Walter Sisulu University. In search of a turnaround strategy, the purpose of this study was to determine the level of problem-solving proficiency of Quantitative Techniques students, which is suspected to influence achievement in this subject. A descriptive survey design was used in this research. Second-year ND (Marketing) students (128) took part in the study. A questionnaire and a written test were used to collect data. A profile of participants’ problem-solving was determined. Their weaknesses and strengths in problem-solving were investigated. The problem-solving proficiency of participants with regards to the biographical variables of Gender, Age, Mathematics background and whether they took Data Handling training at school or not were explored. A model, based on Polya’s four stages of problem-solving, was used to measure the students’ level of problem-solving proficiency, which was 59,16%. Findings suggest that the students achieved highest in understanding a problem (72,29%) and making a plan to solve the problem (73,77%). They are weakest at interpreting their results (29,38%). MANOVA results showed no statistical significance for the biographical variables. The univariate results suggest that age, Data Handling training at school and Gender could affect problem-solving proficiency. Since the findings of this study indicate a strong relationship between participants’ problem-solving proficiency and their actual achievement, some intervention is recommended. An intervention could be in the form of a section on problem-solving in the course, supplemental instruction or an introductory course. Course and curriculum content should be revised to address students’ proficiency in problem-solving. / Mathematical Sciences / M. A. (Statistics Education)

Quantitative Techniques is traditionally a subject with a poor pass rate at Walter Sisulu University. In search of a turnaround strategy, the purpose of this study was to determine the level of problem-solving proficiency of Quantitative Techniques students, which is suspected to influence achievement in this subject. A descriptive survey design was used in this research. Second-year ND (Marketing) students (128) took part in the study. A questionnaire and a written test were used to collect data. A profile of participants’ problem-solving was determined. Their weaknesses and strengths in problem-solving were investigated. The problem-solving proficiency of participants with regards to the biographical variables of Gender, Age, Mathematics background and whether they took Data Handling training at school or not were explored. A model, based on Polya’s four stages of problem-solving, was used to measure the students’ level of problem-solving proficiency, which was 59,16%. Findings suggest that the students achieved highest in understanding a problem (72,29%) and making a plan to solve the problem (73,77%). They are weakest at interpreting their results (29,38%). MANOVA results showed no statistical significance for the biographical variables. The univariate results suggest that age, Data Handling training at school and Gender could affect problem-solving proficiency. Since the findings of this study indicate a strong relationship between participants’ problem-solving proficiency and their actual achievement, some intervention is recommended. An intervention could be in the form of a section on problem-solving in the course, supplemental instruction or an introductory course. Course and curriculum content should be revised to address students’ proficiency in problem-solving. / Mathematical Sciences / M. A. (Statistics Education)

No description available.

In data-centric environments, for example, in the field of scientific computing, the transmission of large amount of structured data to Web services is required. In service-oriented environments (SOA), the Simple Object Access Protocol (SOAP) is commonly used as the main transport protocol. However, the resulting 'by value' data transmission approach is not efficiently applicable in data-centric environments. One challenging bottleneck of SOAP arises from the XML serialization and deserialization when processing large SOAP messages. In this paper, we present an extended Web service framework which explicitly considers the data aspects of functional Web services. Aside from the possibility to integrate specialized data transfer methods in SOA, this framework allows the efficient and scalable data handling and processing within Web services. In this case, we combine the advantages of the functional perspective (SOA) and the data perspective to efficiently support data-centric environments.

No description available.