For traditionally trained statistics teachers, developing active learning material is difficult. We present representative active learning materials that we have used over the last several years. We also give examples of exam questions that we have used to test conceptual understanding gained through the class exercises.
Discusses some of the myths surrounding grading on the curve. Provides a simple explanation of such statistical terms as histograms, relative frequency, normal distribution, mean, and standard deviation. Describes how to restructure the curve, including the program listing for a computer program that will assist the teacher. (TW)
Argues that an SAS program enables instructors to provide individual students with simulated data for the 1-way between Ss design. The instructor chooses the starting values: means, standard deviations, and number of Ss. For each student, the program produces an ASCII data file that can be analyzed by calculator or by many statistical software packages. For the instructor, the program produces a summary ANOVA table for each analysis. Individual student names appear on the data sets and the summary file for the instructor. (PsycLIT Database Copyright 1994 American Psychological Assn, all rights reserved)
Describes elementary graph theory statistics, features that characterize differences in cognitive study strategies, and the use of STUDY for gathering trace data for research on patterns of cognition. STUDY affords a high degree of learner control and is an excellent medium for collecting data on individual differences. STUDY users navigate through content and apply studying actions such as underlining, taking notes, requesting elaborations, and attempting test items. As this happens, STUDY creates detailed time-stamped records of the learner's interactions in a log file. This sequence of study actions is reduced to a set of nodes representing action types and a set of links representing a temporal relation. The output of STUDY can yield resemblance statistics that can allow comparison of single actions by a student as well as comparison across students to reveal differences in cognitive processing routines. (French abstract) (PsycLIT Database Copyright 1995 American Psychological Assn, all rights reserved)
Describes the use of a microcomputer-based authoring system to facilitate student-centered discovery oriented learning in an undergraduate social science course. Use of the authoring system to implement student-generated experiments, to become familiar with statistics and experimental methods, and to promote empirical thinking is discussed. (14 references) (LRW)
Teaching elementary statistical inference from a traditional viewpoint can be hard, due to the difficulty in teaching sampling distributions and the correct interpretation of statistical confidence. Bayesian methods have the attractive feature that statistical conclusions can be stated using the language of subjective probability. Simple methods of teaching Bayes' rule are described, and these methods are illustrated for inference and prediction problems for one and two proportions. We discuss the advantages and disadvantages of traditional and Bayesian approaches in teaching inference and give texts that provide examples and software for implementing Bayesian methods in an elementary class.
This article describes the evaluation of the teaching of statistical inference in a first statistics class. A sample survey project is described as a means of assessing the effectiveness of a Bayesian approach in communicating the basis tenets of inference. There are several advantages of the Bayes viewpoint in performing this survey project, including the explicit modeling of one's prior opinion by means of a probability distribution and the relative ease in reporting statistical conclusions. Some evidence is presented to show that students with sufficient knowledge can accurately specify probability distributions. The success of the survey project is evaluated, and changes to the structure of the project are described that facilitate the interaction of the instructor with the students.
An introductory statistics course is described that is entirely taught from a baseball perspective. Topics in data analysis, including methods for one batch, comparison of batches, and relationships, are communicated using current and historical baseball data sets. Probability is introduced by describing and playing tabletop baseball games. Inference is taught by first making the distinction between a player's "ability" and his "performance", and then describing how one can learn about a player's ability based on his season performance. Baseball issues such as the proper interpretation of situational and "streaky" data are used to illustrate statistical inference.
The concepts of hypothesis testing, trade-offs between Type I and Type II error, and the use of power in choosing an appropriate sample size based on power when designing an experiment are routinely included in many introductory statistics courses. However, many students do not fully grasp the importance of these ideas and are unable to implement them in any meaningful way at the conclusion of the course. This paper presents a number of applets intended to help students understand the role of power in hypothesis testing and which allow them to obtain numerical values without having to perform any calculations for a variety of scenarios, complementing some of the applets presented in Aberson, Berger, Healy, and Romero (2002). Ideas are given about how to incorporate the materials into an introductory course.
Many introductory courses teach traditional probability concepts. The objectives of these courses may be better met by emphasizing characteristics of random variation rather than formal probability. To illustrate a different approach, some alternative concepts and related activities are described and discussed.