Journal Article

  • While many teachers of statistics are likely to focus on transmitting knowledge, many students are likely to have trouble with statistics due to non-cognitive factors, such as negative attitudes or beliefs towards statistics. Such factors can impede learning of statistics, or hinder the extent to which students will develop useful statistical intuitions and apply what they have learned outside the classroom. This paper reviews the role of affect and attitudes in the learning of statistics, critiques current instruments for assessing attitudes and beliefs of students, and explores assessment methods teachers can use to gauge students' dispositions regarding statistics.

  • Current recommendations for reforming statistics education include the use of cooperative learning activities as a form of active learning to supplement or replace traditional lectures. This paper describes the use of cooperative learning activities in teaching and learning statistics. Different ways of using cooperative learning activities are described along with reasons for implementing this type of instructional method. Characteristics of good activities and guidelines for the use of groups and evaluation of group products are suggested.

  • Changes in educational assessment are currently being called for, both within the fields of measurement and evaluation as well as in disciplines such as statistics. Traditional forms of assessment of statistical knowledge provide a method for assigning numerical scores to determine letter grades but rarely reveal information about how students actually understand and can reason with statistical ideas or apply their knowledge to solving statistical problems. As statistics instruction at the college level begins to change in response to calls for reform (e.g., Cobb 1992), there is an even greater need for appropriate assessment methods and materials to measure students' understanding of probability and statistics and their ability to achieve more relevant goals, such as being able to explore data and to think critically using statistical reasoning. This paper summarizes current trends in educational assessment and relates these to the assessment of student outcomes in a statistics course. A framework is presented for categorizing and developing appropriate assessment instruments and procedures.

  • This paper defines statistical reasoning and reviews research on this topic. Types of correct and incorrect reasoning are summarized, and statistical reasoning about sampling distributions is examined in more detail. A model of statistical reasoning is presented, and suggestions are offered for assessing statistical reasoning. The paper concludes with implications for teaching students in ways that will facilitate the development of their statistical reasoning.

  • Over the past twenty years much has been written about the introductory or service course in statistics. Historically, this course has been viewed as difficult and unpleasant by many students and frustrating and unrewarding to teach by many instructors. Dissatisfactions with the introductory course have led people to suggest new models for the course, to lead workshops to reexamine this course (Hogg 1992), and to offer recommendations for how the course should be changed (Cobb 1992). This paper presents the results of a survey of teachers of the first statistics course, to determine the impact of reform efforts on the teaching of statistics. Suggestions and guidelines for teaching these courses are offered, based on the results of the survey.

  • One of the main themes of statistics courses is to teach about variability, as well as location. This is especially important for non-statistics students, who often overlook variability. We consider particularly the problem of comparing variability among k samples (k > 2) that are not necessarily drawn from Gaussian populations. This can also be viewed as testing for homoskedasticity of samples. We examine tools for this problem from the perspective of their suitability for inclusion in elementary statistics courses for students of non-mathematical subjects. The ideas are illustrated by an example that arose in a student project.

  • Statistics and research design textbooks routinely highlight the importance of a priori estimation of power in empirical studies. Unfortunately, many of these textbooks continue to rely on difficult-to-read charts to estimate power. That these charts can lead students to estimate power incorrectly will not surprise those who have used them, but what is surprising is that textbooks continue to employ these charts when computer software for this purpose is widely available and relatively easy to use. The use of power charts is explored, and computer software that can be used to teach students to estimate power is illustrated using the SPSS and SAS data analysis programs.

  • This study examined the relative effects of cooperative vs. lecture methods of instruction. Two sections of an undergraduate statistics course were studied. Test scores were dependent variables. Students in one section were randomly assigned to cooperative groups. Students in both sections completed assignments and practice problems -- in the cooperative class in groups during class, and in the lecture class individually, outside of class. Students in the cooperative learning class achieved higher test scores. Implications of the study and resulting questions are discussed.

  • So that students can acquire a conceptual understanding of basic statistical concepts, the orientation of the introductory statistics course must change from a lecture-and-listen format to one that engages students in active learning. This is the premise underlying an effort of the authors to produce and use a collection of hands-on activities that illustrate the basic concepts of statistics covered in most introductory college courses. Such activities promote the teaching of statistics more as an experimental science and less as a traditional course in mathematics. An activity-based approach enhances learning by improving the students' attention, motivation, and understanding. This paper presents examples of the types of activities that work well in various classroom settings along with comments from colleagues and students on their effectiveness.

  • Sampling distributions are central to understanding statistical inference, yet they are one of the most difficult concepts for introductory statistics students. Although hands-on teaching methods are preferred, finding the right balance between theory and practical experience has not been easy. Simulation activities have not always captured the research situations that statisticians work with. This paper describes a method developed by the author to teach sampling distributions using a collaborative learning simulation based on political polling. Anecdotally, students found the polling scenario easy to understand, interesting, and enjoyable, and they were able to explain the meaning of sample results and inferences about the population. Sample examination questions are included, with examples of students' responses that suggest that the method helped them to understand sampling error and its role in statistical inference.

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