Sorry, you need to enable JavaScript to visit this website.

Proceedings

  • Anchoring probability situations which are conceptually analogous to misconception-prone/target probability situations were generated and tested with secondary mathematics students. The testing showed that probability misconceptions were common but also that anchors for overcoming these misconceptions could be generated. Anchoring situations were effectively utilized in overcoming students' probability misconceptions in the short term. A follow-up study showed that short term effects were retained at the rate of 0.65 over a six month period thereby establishing the long term effectiveness of the approach.

  • This qualitative study examined students' experiences with the constructivist approach to learning in an introductory statistics university course. An overview of students' difficulties and frustrations were addressed for three pedagogical aspects. Findings suggested that students' difficulties were related to their epistemological beliefs of the nature of statistical knowledge, the simplicity of knowledge, and the source of knowledge. Therefore, students' frustrations seems to be connected to the agreement between their epistemological beliefs and those of the professor.

  • The Sampling Distributions program and ancillary instructional materials were developed to guide student exploration and discovery. The program provides graphical, visual feedback which allows students to construct their own understanding of sampling distribution behavior. Diagnostic, graphics-based test items were developed to capture students' conceptual understanding before and after use of the program. Several versions of the activity have been used to date with mixed results. Our findings demonstrate that while software can provide the means for a rich classroom experience, computer simulations alone do not guarantee conceptual change.

  • In this talk I will give some principles and many examples of "objective" questions that test statistical concepts. In some of our large classes we must of necessity use machine-gradable tests. Is it still possible to test concepts (and not just computations) in such a setting? I will try to argue (mostly by example) that we can test concepts this way. The most important principle for any testing is: Test what you believe is important!

  • This paper examines the role of assessment in research studies focused on the teaching and learning of statistics at the undergraduate or graduate level. Some advantages and limitations for types of assessment methods typically used in statistics education research are summarized. An alternative framework is offered for conceptualizing assessment and its role in studies of statistics education. This framework is based on the theory of conceptual change. An illustration will be offered: a study of the impact of the use of computer simulations on learning statistical inference. Examples of the types of assessment embedded in this ongoing research project will be shared.

  • This paper describes the student projects we have used in both introductory and in second-level statistics classes. It addresses the issues of motivating, monitoring, and evaluating student projects, and it discusses some special considerations student projects present for instructors using them.

  • The purpose of this study is to examine the use of computing technology in secondary school mathematics, particularly in the probability and statistics curriculum.

  • This paper discusses the topic of assessment in statistics. It focuses on what assessment is, what traditional ways have been used to assess statistics learning, goals for statistics learners, the need for alternative assessment approaches, assessment challenges, and the use of technology in assessment.

  • This study is aimed at the investigation of the non-cognitive factors related to students' belief and attitude before and after taking an introductory statistics using an interview methodology. Of particular interest is to compare students' belief and attitude between students from a technology-rich class and from a traditional class.

  • PACE stands for projects, activities, class lectures, and exercises. The approach begins with in-class hands-on activities and cooperative team work. The class lectures are organized to provide the basic concepts and guide students through the activities using team work and computer to help students understand the concepts and problem-solving strategies. Projects are self-selected by students under some guidance provided by the instructor. Report writing and oral presentations are emphasized. This article reports an assessment of the PACE model applied in an introductory statistics class.

Pages