Conference Paper

  • Design research projects can be characterized as iterative and theory based attempts simultaneously to understand and improve educational processes. To contribute to a framework for design in statistics education, this paper draws on two design research projects, one carried out by Cobb, Gravemeijer and colleagues in the United States and one by Bakker and Gravemeijer in The Netherlands, both focusing on distribution as a core concept in the instructional design. Both projects were inspired by the theory of realistic mathematics education, which includes design heuristics such as guided reinvention, historical and didactical phenomenology, and emergent modeling. Each of these heuristics is briefly illustrated with examples from these two projects.

  • Research on teacher knowledge has typically examined teachers outside of the classroom in which they use their knowledge. Recognising that it is difficult to separate a teacher's knowledge from the context in which it is used, there has been a move towards studies being conducted in the classroom. Statistics presents its own challenges for teaching and learning compared with mathematics teaching and learning, especially with the growing recognition of and research around statistical thinking. Consequently there is need for an approach to examining teacher knowledge in relation to the actual work of teaching of statistics. This paper suggests a framework for examining the knowledge of primary (elementary) teachers as they engage in teaching statistics. The framework recognises that teacher knowledge is dynamic and dependent on the context of the classroom and students within it.

  • With the dual goal of providing individualized learning and assessment, while simultaneously preserving academic integrity, we have implemented a computerized testing system to generate, administer and grade quizzes in an introductory statistics course for graduate students. A fundamental reason for individualization is to permit each student to learn at his or her own pace. At the same time, administering individualized instruction must not increase the time involvement of the instructor. The ability of the computer to randomly select questions from a test bank, to randomly generate data for the questions, and to randomly order the answer choices makes it possible for learning and assessment to occur in accord with each student's individual needs while maintaining fairness for the students and the instructor.

  • Amongst researchers of statistics education and statistics educators alike, statistical literacy, statistical reasoning and statistical thinking have gained prominence as important learning goals for the teaching of statistics. Careful examination of the three concepts shows that considerable disagreement on their definition still exists, creating problems in the attempts to develop valid and useful measurement instruments. It is argued that the fuzziness of the three constructs stems from the fact that their conception was not motivated by empirical regularities in need of explanation, but rather by the desire to create new perspectives on the future development of statistics education. The inherent ambiguity of the three concepts makes them unsuitable as learning goals for statistics education. By focussing on different aspects of statistical knowledge, however, the intended differentiation in meaningful learning goals can be met in a less disputable way.

  • Statistical literacy, reasoning, and thinking may be the most prominent objectives of statistics education; they are unsatisfactorily defined and demarcated. Therefore, they are difficult to monitor, and assess. As a consequence they are impractical as educational goals. Instead, assessment could be focused on those aspects of specific statistical knowledge that are indicative for different levels of understanding. Factual knowledge directly derived from sources of information indicates a superficial level of understanding; a comprehensive, coherent knowledge structure indicates a more profound level of understanding, and the ability to transfer knowledge (the ability to flexibly engage statistical knowledge in novel tasks) indicates an expert level of understanding. This classification of hierarchically related levels of statistical understanding may produce adequate ways of measurement and assessment.

  • This report focuses on a research project concerning individual curricula regarding the instruction of statistics and of probability theory. Individual curricula will be described as belief systems which contain teachers' subjective knowledge and conceptions about mathematics, about learning and teaching mathematics, and particularly about statistics and probability. This report stresses two aspects: the theoretical settings, and the methodological settings of the research. The theoretical settings concern central assumptions and theoretical constructs. The discussion of the methodological settings which will be illustrated by research results, includes the description of a five-step-methodology used for investigating individual curricula.

  • This paper describes data from the Community Mapping Project (CMP), a set of activities within a summer seminar for high school students. CMP was designed based on the principles of culturally relevant pedagogy to create conditions where students themselves would recognize the relevance of statistics in identifying and describing inequities that face their communities. Using mixed methods we analyzed pre- and post assessments, final projects and process data from video case studies to begin to understand how this learning was organized for the 21 twelfth graders participating in this project. Our qualitative analysis revealed several tensions that emerged between the social justice goals and statistical goals and how those tensions mediated learning. The article may help inform both teachers who wish to rethink their statistics pedagogy, and the designers of culturally relevant curricula.

  • Although the Statistics Education community has advocated using real data to teach introductory statistics for quite some time, often these data sets are not recognizably real to statisticians since the students' limited experience with "real" statistical software and data management techniques precludes the use of truly messy data. But grappling with messy and complex data sets is important for teaching Statistical Thinking (broadly defined as "thinking like a statistician") and is appropriate for an introductory statistics course. We describe our experience collecting rich data sets and developing computer lab assignments using STATA to teach statistical thinking to first-year university students using these data sets. Collecting useable, real, data sets turns out to be fairly difficult for several reasons, and teaching data management and analysis without resorting to rote-based rules is quite challenging.

  • The advancement of computer technology creates unlimited opportunities for teaching and learning statistical concepts. A significant impact is the paradigm shift from a passive teaching-centered to an active learning-centered environment. Although one should not make a paradigm shift solely for the sake of technology, there is no doubt that technology will play a crucial role in this transformation. Research has suggested that meaningful learning takes place when students are actively involved in constructing knowledge themselves through their own experiences and active participation. This article proposes an active learning environment for introductory statistics courses using an online real-time database created by students. The experience of implementing the active learning activities using the real-time online database will be shared. Some strengths and weaknesses will be discussed.

  • During the past decade, national and international organisations have been steadily increasing the number of web-based statistical databases available to general public. While often user-unfriendly (mostly due to poor design and organisation as well as lack of navigation options) in their pioneer years, many of these databases have been gradually transformed into well-managed expansive resources that can greatly enhance the teaching and learning of statistics. A number of illustrative examples are presented in this paper along with discussion of our experiences and identification of future challenges pertaining to their use in statistics courses.

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