Proceedings

  • This paper follows two previous studies regarding an analysis of the efficacy and efficiency of the academic system. A bivariate, multilevel model has been proposed in order to measure the relative efficacy of each course by quantifying its contribution in obtaining a particular outcome, net of individual, environmental and course- specific factors. The concept of technical efficiency is also presented and two evaluating methodologies, which are based on a frontier function, are analysed. Both methods take into account differences in students' academic ability (which characterize the university system) and these are analysed on a geographical basis, thereby aiming at an investigation of differentials throughout all the regions in Italy. The results of this analysis will be presented during the conference.

  • This paper reports on the use of a system designed to encourage statistics students to read the course text as a primary source of information and ideas. Reading and limited assessment would precede classroom teaching. The system has been implemented for eight semesters. Summaries of data collected will be presented, as will qualitative feedback from students.

  • The capacity for abstraction and concentration has changed. Methods of teaching Probability and Statistics must also evolve. Following is a presentation of an organized set of images, analysed using Graphs Theory, specifically adapted by syntax to each type of information. We use these, not as an illustration but as an exhaustive proof. We make this claim after having established the isomorphism between possibilities and elementary surfaces. Probability and statistics are thus unified and simplified. Combinatory Algebra and Mathematical Analysis remain tools. No more theorem will need to be learnt by heart. Fear and mathematical inferiority will be changed into an intellectual comfort. We have been using this teaching method for more than 25 years, in a basic course for non-specialized students, having written the corresponding books. Clinical and statistical tests already show its efficacy.

  • Many schools, like Carnegie Mellon University, are now teaching introductory statistical reasoning courses in a way that emphasizes conceptual understanding of the basic ideas of data analysis. There are several challenges in teaching such a course; foremost among them is the difficulty of conveying a sense of the "Big Picture." This paper describes a computerized learning tool that we have developed to help overcome this obstacle. This tool is a cognitive tutor in which students solve data-analysis problems and receive individually tailored feedback. We discuss our cognitive tutor's use in the course and its measured effectiveness in a controlled experiment.

  • Previous research has shown a consistent, albeit weak, negative correlation (r ( -0.20) between statistics anxiety and statistics achievement. Additionally, self-efficacy has been shown to be a consistent predictor of both anxiety and achievement. This study showed that if self-efficacy is assumed to reflect a distribution of confidence, then the relationship between statistics anxiety and statistics achievement can be explained by the differential impact of two features of the self-efficacy distribution. Although only outcome expectancies predict statistics achievement, statistics anxiety is predicted by the interaction between outcome expectancies and outcome uncertainty. It is suggested that these results are indicative of at least two sources (or cognitive interpretations) of statistics anxiety, namely lack of confidence about one's ability and uncertainty in one's performance. The results are discussed in terms of cognitive appraisals of threat and challenge.

  • Preservice Mathematics teachers are faced with the task of learning Mathematics subject content and developing pedagogical knowledge. This paper describes an attempt to address these tasks simultaneously by designing a course in which preservice teachers collect data related to learners' understanding of statistics and fractions, and develop their own statistical understanding and expertise through analysis of this data. The preservice teachers' statistical thinking was assessed by analysis of articles they wrote based on their own data. It is asserted that even in the presence of the requisite raw materials, statistical thinking is not intuitive and requires explicit teaching.

  • Following a course in elementary statistics, students are able to demonstrate a basic knowledge of statistical concepts and ideas, but often fail to apply this knowledge to concrete problems. From research in cognitive psychology, we know that the organization of knowledge starts with the mental storage of initially isolated concepts and simple principles. A certain amount of conceptual understanding is reached when the student succeeds in forming relationships between these knowledge elements. The task faced by any teacher in statistics, is to enable the student to form such integrated knowledge networks. Research has shown that the formation of such networks is stimulated when students, confronted with a statistical problem that requires the application of their basic knowledge, actively try to explain the solution of the problem to themselves. This paper discusses a didactic method that seeks to stimulate such self explanatory activity in students.

  • Taught modules on sample survey methods provide a useful means of integrating and extending a range of statistical ideas. Knowledge and expertise gained in basic Statistics modules at Levels 1 and 2 can be brought together and applied in sample surveys, and provide the platform for the development and application of more advanced concepts. This paper mainly concerns Level 3 modules in the programme of Statistics learning in the undergraduate degree(s) at The Nottingham Business School, but the principle has been applied elsewhere.

  • Statistics South Africa is the official supplier of statistics for the South African government. It supplies various types of statistics to government departments, industry, financial houses as well as economic and developmental planners. Being an organisation where many subject experts are employed and where transfer of knowledge takes place, it is only natural to see that the quality of the organisation lies in its people and in the continual development of people's skills. SAS is the most widely used data management and statistical tool, especially in social and economic statistics. Training is provided as part of a well defined development plan that each employee has in terms of their position. Most of the training deals with data management and data manipulation as well as statistical analysis. It is intended that as people learn more that people will want to know more and the stage has been reached where features such as SQL and Macro Language amongst others are being trained. Training of Statistics can therefore take place with SAS being used as an analytical tool. Products such as SAS Analyst and Enterprise Guide allow the instructor to practically demonstrate the application of statistical techniques. Various different statistical procedures can be performed from simple descriptive statistics to complex inferential statistical procedures like data mining and time series analysis. Training of this nature goes hand in hand very effectively with the more theoretical type of statistical tuition that someone might receive elsewhere. However, if a trainee is able to see the link between the theoretical approach and the practical application thereof then everything becomes clearer, it stimulates the desire to learn more and everything falls into place. Skills development in South Africa is very important to the extent that legislation has enabled Statistics SA to be part of a Public Sector Education & Training Authority (PSETA). Through this Stats SA has proposed to create SAS learnerships, which would allow individuals to learn and apply knowledge gained by SAS in the workplace. Hence, this paper aims to show the value that the training and usage of SAS has to an organisation like Statistics SA and what new developments and initiatives can be pursued to further meet this aim.

  • The importance of statistical graphics, as well as their practical use in day-to-day scientific research, makes it worth assessing and appraising teachers' conceptions concerning this matter. Accordingly, we should shed some light on the most specific components of dealing with such graphics as understood by math secondary level teachers. At the end, we try to give some guidelines that could be useful when elaborating on a content for teachers' training.

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