Conference Paper

  • What do statistics teachers believe makes a good student of statistics? What part does the ability to communicate statistical results and ideas playing this judgment? IN this paper, we investigate these questions and suggest answers based on a recent empirical study carried out by e-mail interview with IASE members from around the world. The responses alert us to the diversity of views on the relative courses. Some teachers propose that communication skills are essential to learning statistics at university, others do not mention communication. In any discussion on statistical communication, we should be aware of the range of views held by statistics educators themselves, and the range of views that they communicate to students through their teaching.

  • At the department of mathematics &amp; informatics in our University we are in the process of<br>redesigning our introductory course on stochastics (probability and statistics) for future<br>mathematics teachers. In this course we now use the software FATHOM, which students learn as a (cognitive and culturally mediated) tool for exploratory data analysis, for simulation and for inferential statistics as well as a tool for experimenting with statistical methods. We use various types of Internet based materials to support the learning process of our students. Experimental learning environments and working environments containing data and exploratory guides are constructed with FATHOM. FATHOM offers meta-medium and meta-tool capabilities that offer high adaptability and versatility for the teacher of a course. In addition, we have developed Java applets, screen videos and web-based hypertexts as further

  • 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.

  • The importance of facilitating study and practice materials that are consistent with graded assessments and instructional objectives is well known, if not commonly used , in educaitonal practice. Reciprocal Peer Tutoring (RPT) is a collaborative approach that embeds assessment in a formalized learning process to facilitate student involvement with course content and improve achievement. Students engaging in RPT activity, each student of a dyad is independently responsible for synthesizing course content and constructing practice multiple-choice test questions, complete with answers, based on the course curriculum. Each dyad then administers practice tests to each other prior to formal class examinations. Upon completion of the practice exams, partners score each other's work and alternate roles as tutors, and tutees to assess each other's performance, give feedback on missed items, and discuss individual questions and course content. In this dual role as tutor and tutee, students benefit through the preparation and instruction in which tutors engage, as well as from the instruction that tutees receive. This study examines the impact of reciprocal peer tutoring (RPT) on student achievement over six sections of an introductory statistics course. A comparison of RPT treatment relative to a control accounting for instructor, showed an effect of RPT treatment at the time of the last examination of the semester. This finding is tempered by additional analyses into the effectiveness fo the RPT treatment. Student achievement relative to increasing levels of cognitive complexity of exam items showed mixed results. Furthermore, a comprehensive analysis of the student work within RPT treatment revelaed students having difficulties implementing the intervention.

  • The current assessment reform movement in statistics encourages instructors to think more broadly about cognitive measures which assess student learning. In response, statistics instructors have begun incorporating innovative methods of assessment into their courses, the most common of these procedures being athentic assessment, performance assessment, and portfolio assessment. Thus, this paper will provide a typology of different effective ways of assessing performance in statistics classess for the various contexts (e.g., undergraduate vs. master's vs. doctoral), content (e.g., measurement vs. evaluation vs. research design), and pedagogical styles (e.g., web-based vs. traditional; theory vs. concepts vs. application).

  • Quality of TQM have become busswords of the 90s. They follow you everywhere, at work, at school, and even into your classroom. Applying TQM to the teaching of statistics means that we need to know how our students learn in order to affect the quality of our teaching. The time and energy that an instructor puts into preparation and teaching og a course will be wasted and the teacher will be ineffective, if he or she does not moticate and direct student learning. Boroto and Zahn (1989) claim that "quality improvement iscritical for all levels of statistics education if we are to avoid withering and dying as a discipline" (p. 71).

  • No discussion of the context of teaching statistics would be complete without acknowledgement of the anxiety that students bring to clss. According to Onwuegbuzie (in press) two-thirds to four-fifths of graduate students experience high levels of stress while enrolled in statistics courses. Some delay taking these courses until late in their academic programs (Onwuegbuzie, 1997a, 1997b); some drop ot completely (Richardson &amp; Suinn, 1972). Some just "labor through the course, making it a high anxiety arena for their classmates and instructors: (Wilson, 1999, P. 2).<br>As statistics instructors, there are at least four questions we need to examine:<br>(1) Should we acknowledge the existence of statistics anxiety or just ignore it?<br>(2) If we acknowledge it, should we attempt to reduce it?<br>(3) If we attempt to reduce it, what strategies might we employ?<br>(4) Should we differentiate instruction--content, proess, and product--in order to address teh comfort levels as well as the learning styles and peferences of our students?

  • According to Vygotsky (1934), the meaning of words are the main units to analyse psychological activity, since words relfect the union of thought and language, and include the properties of the concept to which they refer. As such, one main goal in statistics education research is finding out what meanings students assign to statistical concepts, symbols and representations and explaining how these meanings are constructed during problem solving activities and how they evolve as a consquence of instruction to progressively adapt to the meanings we are trying to help students construct.<br><br>In trying to develop a systematic research program for mathematics and statistics education at the University of Granada, Spain we have developed a theoretical model to carry out these analysie (Godino &amp; Batanero, 1994; 1998), which has been successfully applied in defferent research work in statistics education., in particular in some PhD these carried out at different Universities in Spain. The aim of this paper is to describe this model and suggest a research agenda for statistics education based on the same.

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