This paper discusses student-centered learning within the context of an introductory statistics course.
This paper discusses student-centered learning within the context of an introductory statistics course.
Random assignment is one of the more difficult concepts in introductory statistics classes. Many textbook authors admonish students to check on the comparability of two randomly assigned groups by conducting statistical tests on pretest means to determine if randomization worked. A Monte Carlo study was conducted on a sample of n = 2 per group, where each participant's personality profile was represented by 7,500 randomly selected and assigned scores. These values were obtained from real data sets from applied education and psychology research. Then, independent samples t-tests were conducted at the 0.01 alpha level on these scores. Results demonstrated that x-bar(1) does not equal x-bar(2) for only 33 out of 7,500 variables, indicating that random assignment was successful in equating the two groups on 7,467 variables, even with a sample size of n = 2. The students' focus is redirected from the ability of random assignment to create comparable groups to testing the claims of randomization schemes.
The value to students of active learning has been recognized. This has led to the wide use of assignments in statistical methods courses where students use statistical software and computing equipment to analyze data. These assignments enable most students to master the mechanics of data analysis. The amount of experience that a student can get with such assignments, however, is limited. A sizable proportion of students have difficulty grasping some of the many concepts that are introduced in these courses. Nevertheless, these concepts are important for effective modeling and data analysis, and instructors should focus on them. By using current computing technology, it is possible to supplement standard data analysis assignments and algebraic derivations and have students become actively involved in the learning of important statistical concepts. The learning experience can be enhanced by giving students additional statistical "experiences" by using combinations of carefully designed and implemented multiple simulations and dynamic graphics to illustrate key ideas. In this article we describe and illustrate several instructional modules and corresponding software that have been designed to assist instructors in teaching introductory statistics courses.
The interaction between new curricular goals for students and alternative methods of assessing student learning is described. Suggestions are offered for teachers of statistics who wish to re-examine their classroom assessment practices in light of these changes. Examples are offered of some innovative assessment approaches that have been used in introductory statistics courses, and current challenges to statistics educators are described.
We cannot discuss assessment of students without also discussing student assignments. Why? The content of a course depends on the customized curriculum of each individual instructor, department, or even, in some cases, the institution. Therefore, here I am going to focus on what we might choose to assess, how we might choose to assess our students, and how we can design our assignments to assist us in our assessment strategies. Finally, I will present an example from my applided multiple regression course.
An important theme in an introductory statistics course is the connection between statistics and the outside world. This article describes some assignments that have been useful in getting students to learn how to gather and process information presented in the newspaper articles and scientific reports they read. We discuss two related assignments. For the first kind of assignment, students work through prepared instructional packets. Each packet contains a newspaper article that reports on a scientific study or statistical analysis, the original report on which the article was based, a worksheet with guidelines for summarizing the reported study, and a series of questions. In the second kind of assignment, each student is required to find a newspaper article themselves, track down the original report, summarize the study using our guidelines, and write a critique of the article. Here, we describe the guidelines we developed to help the student in reading the newspaper article and orginal source, and the procedures we used for each type of assignment. Examples of handouts and assignments appear as appendixes.
This brief guide provides some practical guidelines for the assessment of students' statistical knowledge and reasoning about data. It is intended for teachers who are just beginning to teach statistics (usually as part of the mathematics curriculum) and who have relatively little experience in this area.
One objective of this chapter is to introduce a diagnostic approach to assess how well students answer questions about a research report. A second objective is to show how the proposed assessment tools can be used to identify teaching strategies for overcoming students' errors in interpreting reports. A third objective is to suggest how the interpretation-of-research assessment questions can be used to help students identify what information needed for interpretation is missing in journal or media reports.
Good teachers have always used stories in their teaching, and often stories turn out to be more effective than arguements and explanations. I will discuss five categories of stories and their uses here.
This chapter describes various methods for assessing statistical knowledge, understanding and skills. It covers traditional written forms of assessment, multiple-choice vs. open-ended questions, essays, practical work and projects, and oral assessments. It also discusses issues pertaining to the monitoring of on-going progress, groups that may be disadvantaged as the result of particular assessment methods, experiences of changing assessment methods, and the assessment of teachers.