• Describes the role of an instructional sequence and two accompanying computer-based tools in supporting students' developing understandings of statistical data analysis. Documents the emergence of the sociomathematical norm of what counts as a mathematical argument in the context of data analysis.

  • This paper considers the possibilities of using computers not only to amplify, but to reorganize children's thinking and mental functioning. These two different conceptualizations of the transformational role of noncomputer cognitive technologies (such as written language) in human intelligence and cognitive change are sketched, and the different implications to be drawn from these conceptualizations are considered in relation to human thinking and the educational processes. Several examples of software as cognitive technologies are analyzed, and the advantages of the reorganizer approach are detailed. It is argued that since the cognitive technologies we invent can serve as instruments of cultural redefinition (shaping who we are by what we do), the selecting of values for educational goals becomes important. Finally, it is suggested that the urgency of updating educational aims and methods recommends an activist research paradigm for simultaneously creating and studying changes in the processes and outcomes of human learning with new cognitive and educational technologies.

  • Computers are playing a fundamental role in enhancing exploratory learning techniques in education. This volume in the NATO Special Programme on Advanced Educational Technology covers the state of the art in the design and use of computer systems for exploratory learning. Contributed chapters treat principles, theory, practice, and examples of some of the best contemporary computer-based learning environments: Logo, Boxer, Microworlds, Cabri-Géomètre, Star Logo, Table Top, Geomland, spreadsheets, Function Machines, and others. Emphasis is on mathematics and science education. Synthetic chapters provide an overview of the current scene in computers and exploratory learning, and analyses from the perspectives of epistemology, learning, and socio-cultural studies.

  • Provides an overview of the structure of data analysis, the interrelationship between data analysis and probability, and the connection between data analysis and other components of the mathematics curriculum. Presents a possible order for topics being consistent with modern statistical practice and allows the topics to grow as students move through grade levels.

  • This paper presents a strategy for the wise use of information technologies to<br>support significant improvements in school mathematics and science. As a result,<br>this article makes no attempt to cover the entire educational technology landscape<br>with an even hand. What is attempted is to map out a balanced strategy that cash-strapped schools could pursue as part of a larger effort to make substantial<br>improvements in teaching in these fields.

  • Since the mid-1980's, confidence intervals (CIs) have been standard in medical journals. We sought lessons for psychology from medicine's experience with statistical reform by investigating two attempts by Kenneth Rothman to change statistical practices. We examined 594 American Journal of Public Health (AJPH) articles published between 1982 and 2000 and 110 Epidemiology articles published in 1990 and 2000. Rothman's editorial instruction to report CIs and not p values was largely effective: In AJPH, sole reliance on p values dropped from 63% to 5%, and CI reporting rose from 10% to 54%; Epidemiology showed even stronger compliance. However, compliance was superficial: Very few authors referred to CIs whn discussing results. The results of our survey support what other research has indicated: Editorial policy alone is not a succicient mechanism for statistical reform. Achieving substantial, desirable change will required further guidance regarding use and interpretation of CIs and appropriate effect size measures. Nevessary steps will include studying researchers' understanding of CIs, improving education, and developing empirically justified recommendations for improved statistical practice.

  • Quantitative literacy addresses citizen's needs to function knowledgably in a society that is data-intensive. Such literacy is as necessary as reading and writing. Colleges and universities fulfill this educational imperative with a variety of goals and requirements. To equip students for life in the Computer Era, a sharper focus is called for , in undergraduate education for numeracy.

  • Data are hot! Everyone -- students, teachers, parents, employers -- is interested in data, but few know how to collect and interpret data intelligently. Data is the basis of science, and statistical thinking is key to the scientific method, yet few graduates of high school and college understand how science works. For these and other reasons, statistics must become a major component of the modern K-12 mathematics curriculum and achieve a stronger presence in the undergraduate curriculum, as recommended by a wide variety of educational groups.

  • The necessity for stimulating the interest of pupils in mathematics in general and statistics in particular is made clear by the results of surveys. The studies showed, that pupils are tired of mathematics. Mathematics is generally regarded as one of the most unpopular subjects at school. The pupils don't achieve so well in this subject . The causes might originate in several variables. The spectrum of possible causes for these bad results stretches from genetic disposition to deficits in learning behaviour. One of the aspect is that mathematics/statistics use the symbol language. And just exactly this language is what we want to look at more closely. As an essential feature of symbols the fact must be emphasized that they "all mean something other than themselves, that they all point to something besides themselves".

  • This study examined the role of attitudes toward statistics, mathematics anxiety, mathematics attitude, mathematics background, demographic variables, and performance for students in an undergraduate introductory statistics course. The study participants were 155 students enrolled in five classes of introductory statistics at a four year college in metropolitan Atlanta. Using a self-selected ID to assure anonymity, the students completed the Survey of Attitudes Toward Statistics (SATS) at the beginning and end of the term. The SATS provides scale scores for Affect, Cognitive Competence, Value, and Difficulty. They also completed a mathematics attitude and anxiety measure, a demographic questionnaire, and a mathematics history. Students revealed their ID's after completion of the study. This allowed performance data from the course and prerequisite mathematics information to be linked with other student data. Students participating in this study had fairly positive attitudes concerning their Cognitive Competence and the Value of statistics at the beginning of the course. Their feeling of Affect was almost neutral and they expected the course to be somewhat difficult. Statistics attitudes were slightly less positive at the end of course. There were no statistically significant differences in attitudes between first time enrollees and those who were repeating the course or between students who did and did not complete the course. Pre-course SATS attitudes were generally not related to gender or age of the students nor to the years of high school mathematics or number of college mathematics courses. All of the SATS subscales were correlated with student grades in the prerequisite course. Pre-course Affect and Cognitive Competence scales were highly correlated to mathematics attitude, math self-concept and statistics self-confidence and moderately correlated with mathematics anxiety. Path analysis was used to develop a conceptual model for statistics attitude and performance in the course using mathematics attitude, mathematics anxiety, and prequisite grade as the exogeneous variables. In the path model, performance in the course was not influenced by either the pretest or posttest SATS. Performance during the statistics course did affect the posttest SATS scores.