Proceedings

  • In 2008 The University of Melbourne introduced the `Melbourne Model' - a significant reform of<br>its degree structure. Students enrol in one of six new degrees; 25% of their degree points must be<br>taken as "breadth" material outside their core degree. This requirement can be met by enrolling<br>in a "University Breadth Subject" which is available to all students and has no pre-requisites. We<br>developed a subject called "Critical thinking with data". It has the bold intention of teaching<br>important elements of statistical science, with minimal mathematics. We present our approaches<br>to content and delivery of the subject. We made extensive use of visual and other media,<br>integrating case studies from the press and elsewhere with the pedagogical content. Much of the<br>background information is available via our learning management system. Three eminent guest<br>lecturers provided inspiration from fields in which critical thinking about data is integral.

  • "Critical thinking with data" is a new "University Breadth Subject" developed for first year<br>students under The University of Melbourne's "Melbourne Model". It aims to teach important<br>elements of statistical science, with minimal mathematics, and was taught in first semester 2008.<br>We present our approaches to assessment of the subject. This has required the use of approaches<br>that are quite distinct from mainstream statistical subjects, since students are not really being<br>taught to do statistical work. They are required to make astute judgments of material with<br>quantitative information, including such texts as a short article about some research in the<br>newspaper. We have used a variety of forms of assessment, including weekly quizzes, (very) short<br>assignments, and a larger project. The style of assessment is more consistent with that used in<br>humanities subjects, and therefore has some important challenges for staff involved in marking,<br>for example.

  • This study attempts to construct the profiles of two types of statistics learners: namely those<br>with a positive and a negative attitude towards statistics. The contribution of this work lies in<br>its attempt to characterize each profile of learner by relating to his/her perceived attitudes<br>toward statistics, types of learners, mode of study, program structure, age, gender and<br>learners' evaluation towards the statistics course. These variables are used as predictors that<br>discriminate learners with positive and negative attitudes toward statistics. The results<br>indicate that learners with positive attitudes can be reliably distinguished from learners with<br>negative attitudes toward statistics. This then can assist instructors to optimize the teaching<br>and learning of statistics in the classroom.

  • This study measures the evaluation of teaching given by students against their final outcomes<br>in a subject. The subject in question had an enrolment across four campuses of 1073 students<br>at the time of the evaluation and is a statistics subject that is core (i.e. compulsory) to several<br>undergraduate business degrees. This study is based on the 373 students (34.8%) who<br>responded to the survey, and their final results. The evaluations were open for a period of six<br>weeks leading up to and just after the final exam. The study matches the responses to the<br>question "This unit was well taught" to final outcomes, in an attempt to ascertain whether<br>there is a link between student evaluation of teaching and performance. The analysis showed<br>that for the students who self-selected to complete the survey:<br>&bull; Students who perform well in the subject generally give higher scores than lower<br>performing students.<br>&bull; The same general pattern prevailed when other secondary factors were taken into<br>account, such as, when the evaluation was completed, campus and gender.<br>&bull; The timing of when a student completes the evaluation seems the most important of<br>these secondary variables.<br>&bull; In general, students who submitted their evaluations after the exam gave higher ratings<br>if they eventually obtained a pass grade or better, and lower grades if they failed.

  • Student evaluations of teaching have increased in importance to universities in Australia over<br>recent years due to changes in government policy. There has been significant debate in the<br>literature as to the validity and usefulness of such evaluations and as to whether students who<br>respond to the evaluations are indeed representative of the student population. A potential<br>invalidating issue is self selection in the evaluation process. In this paper, we consider student<br>evaluations of a large first year business statistics subject that had 1073 eligible students<br>enrolled across four campuses at the time of the evaluation. The study is based on the 373<br>students (34.8%) who responded to the survey, and their final results. The evaluations were<br>open for a period of six weeks leading up to and just after the final exam. The study looks in<br>detail at the student population identifying such attributes as gender; home campus; course of<br>study; domestic/international; Commonwealth Supported Place/full fee paying, etc. and then<br>mapping these results to those of the students who responded to the survey.

  • In this paper the status, content and assessment of statistics in South African primary and secondary school curricula are discussed. With the new school curriculum, fully implemented in 2005, the scope of statistics has been broadened considerably; teacher training has however not yet caught up with the requirements for the teaching of the subject. A survey of teacher training programmes presented at the universities in South Africa was done to determine the status and content of statistics education in these programmes. Results show that many of these programmes do not yet train statistics teachers adequately for their task to prepare learners to be statistically literate citizens and that very few statistics education research studies on the post graduate level have been completed in the country.

  • In order that students become able to exercise their full rights of citizenship, they need to develop abilities and competencies related to statistical literacy at compulsory school levels. These include being able to read, interpret and criticize media information and take conscious decisions in the face of these readings. In Brazil, in 1998, it was suggested that, in the middle school, the study of statistics be incorporated into the mathematics curriculum, and in 2002 the same was prescribed for the high school level. In this context, the aim of this paper is to identify the institutional practices of statistical literacy as specified by the Education Ministry in their official documents and to analyse in the pedagogical orientations, also supplied by the Ministry, the expected levels of statistical literacy levels. Results show that there have been great improvements in relation to statistical literacy, but much has yet to be done in the compulsory school

  • Analysis of the curricula for primary schools in England and Brazil indicates that in both countries while there is emphasis given in policy documents to the importance of problem solving, the materials that are designed to support teachers' implementation of the curriculum in their classrooms reflects a more passive approach to the teaching of graphing. We draw on research evidence from studies with primary school children and with student teachers to argue for the importance of active use of graphing for the emergence of transparency (Meira, 1998). We discuss the implications for initial teacher education in order to support teachers whose own confidence and experience in statistics is very limited.

  • This analysis of the K-8 statistics standards in 41 United States of America (USA) state documents that include grade level expectations (GLEs) is timely given the increased need for statistical literacy as the quantity of available data around us grows. This analysis endeavors to answer the question: What are K-8 students in the USA expected to know and be able to do with regard to statistics as represented in the state standards documents? The study was framed using the four process components outlined in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: (1) formulate questions, (2) collect data, (3) analyze data, and (4) interpret results (Franklin et al., 2007). Among other findings, the analysis highlights two major types of knowledge expected in the documents, the knowledge expected to "do" each of the four processes and the knowledge expected to "understand" and/or "evaluate" the processes.

  • In France, recent mathematics curricula reinforce the teaching of statistics and probability. They recommend starting with an experimental approach introducing the observation of sampling fluctuations and the construction of random experiment simulations in order to prepare students for theory. This approach raises the problem of the didactical practice of random experiment modeling and simulations.

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