The statistical thinking exhibited by 14-19 year-old students during clinical interview sessions is described. The students' thinking with regard to fundamental statistics concepts is reported in order to help inform instructional practice.
The statistical thinking exhibited by 14-19 year-old students during clinical interview sessions is described. The students' thinking with regard to fundamental statistics concepts is reported in order to help inform instructional practice.
In this article, we present the main findings of an experiment which involves teaching statistics in the 5th and 6th grade classes of Greek elementary schools. This experiment focused on the evaluation of the potentials of teaching statistical concepts and methods using directed projects.
This article explores the intuitions of secondary education majors regarding probability. This is accomplished by administering a two-question instrument to 113 participants. Their responses to these questions, and more importantly the explanations they provide for these answers, are analysed. The conclusions drawn may be informative to teachers of probability and statistics as they attempt to remediate common probabilistic misconceptions and devise more effective teaching strategies.
We compare students in online and lecture sections of a business statistics class taught simultaneously by the same instructor using the same content, assignments, and exams in the fall of 2001. Student data are based on class grades, registration records, and two surveys. The surveys asked for information on preparedness, reasons for section choice, and evaluations of course experience and satisfaction. Using descriptive statistics, regression analysis and standard hypothesis tests, we test for significant differences between the online and lecture sections with regard to performance and satisfaction with the course as well as motivation and preparedness for taking an online course. We report several differences, including better performance by online students.
This article uses a case study of 2001 town and city data that we analyzed for Boston Magazine. We use this case study to demonstrate the challenges of creating a valid ranking structure. The data consist of three composite indices for 147 individual townships in the Boston metropolitan area representing measures of public safety; the environment; and health. We report the data and the basic ranking procedure used in the magazine article, as well as a discussion of alternative ranking procedures. In particular, we demonstrate the impact of additional adjustment for the size of population, even when per capita data are used. This case study presents an opportunity for discussion of fundamental data analysis concepts in all levels of statistics courses.
Previous research has linked perfectionism to anxiety in the statistics classroom and academic performance in general. This article investigates the impact of the individual components of perfectionism on academic performance of students in the statistics classroom. The results of this research show a clear positive relationship between a studentÅfs personal standards and academic performance consistent with the literature. Surprisingly, the inherent need of some students for organization and structure was found to be negatively related to academic performance. This finding suggests that the organization of statistics as perceived by some students may not always foster understanding, resulting in student confusion and lack of achievement. This infers that statistics instructors may need to put sufficient emphasis on the underlying composition of statistical ideas and the linking of statistical techniques that are presented in the classroom and in the textbook. The implications of these results are discussed in terms of current trends in the reform of the statistics curriculum and approaches that may improve the clarity of the underlying structure of statistics.
Pupils in England and Wales are increasingly being asked to undertake investigative-type work, be it the new compulsory projects in data handling for GCSE Mathematics (age 14-16) (see Browne 2002) or the Key Skills topic application of number. This article shows how teachers can generate realistic project scenarios using real data and produce indicative model solutions from the same data. The projects range from simple presentational problems for data,through hypothesis testing to complex modelling scenarios.
The 2003 AERA theme: Accountability for Educational Quality: Shared Responsibility provides a solid foundation for research on the means by which quality assessment can improce both instructional delivery and student learning. A counterpoint to the prevalent accountability focus is intentionally sought to promote participation by both faculty and students toward assessment methos that can improce teaching and learning. This paper provides a progress report on an effort to enhance assessment of quantitative reasoning with an eye toward greater student engagement and assessment results that might better inform pedagogy.
In this paper we report on our ongoing efforts to identify and assess key ideas in data analysis (or statistics) that we maintain should be at the focus of middle school instruction. It was in the hopes of locating items that we could use to assess some of these more complex objectives that we searched the collection of items released by the National Assessment of Educational Progress (NAEP) and the various states. We first describe in more detail the nature of items being used on large-scale state assessments. We then offer some of our views on what we should be teaching and present some items that we are designing to tap these ideas.
This study was carried out as a preparation to the development of instruction material for statistics. The history of statistics was studied with special attention to the development of the average values: the arithmetic, geometric, harmonic mean; median, mode, and midrange. Also sampling and distribution are discussed. After an introduction on phenomenology, this article firstly discusses a so-called historical and then a didactical phenomenology of the average values.<br>The average values form a large family of notions that in early times were not yet strictly conceptions. It appears to be important that students discover many qualitative aspects of the average values before they learn how to calculate the arithmetic means and the median. From history, it is concluded that estimation, fair distribution and simple decision theory can be fruitful starting points for a statistical instruction sequence.