Described is a strategy that allows students to experiment with probability without applying formulas to solve problems. Students are able to intuitively develop concepts of probability before formal definitions and properties. Sample problems are included along with BASIC programs for some of the problems. (KR)
Many authors have argued the benefits of collaborative learning (diSessa and Minstrel, 1995; Cohen, 1994; Reynolds, 1995; Bruer, 1993; von Glaserfield, 1991) and activity based courses (Jones, 1991; Yackel, Cobb and Wood, 1991). However, few have presented tools or methods for applying these ideas in large undergraduate service courses. In the context of undergraduate statistics education, we introduce ``Virtual Benchmark Instruction'' a method to facilitate collaborative learning using HyperNews, a structured hypertext bulletin board on the World Wide Web. We draw extensively on previous work by Minstrell, diSessa, and others, who developed and evaluated ``Benchmark Instruction'' in the context of the high school physics classroom. We adapt their ideas and add a virtual environment, generalizing the technique to larger audiences.
Describes the development of an artificial intelligence system called GIDE that analyzes student errors in statistics problems by inferring the students' intentions. Learning strategies involved in problem solving are discussed and the inclusion of goal structures is explained. (LRW)
Described and evaluated is a course structured around SPSS (the Statistical Packages for Social Sciences) that helps psychology students review what they have learned in an introductory statistics course and apply these quantitative skills to other courses. Student evaluation showed that course objectives were met. (Author/RM)
The idea of data as a mixture of signal and noise is perhaps the most fundamental concept in statistics. Research suggests, however, that current instruction is not helping students to develop this idea, and that though many students know, for example, how to compute means or medians, they do not know how to apply or interpret them. Part of the problem may be that the interpretations we often use to introduce data summaries, including viewing averages as typical scores or fair shares, provide a poor conceptual basis for using them to represent the entire group for purposes such as comparing one group to another. To explore the challenges of learning to think about data as signal and noise, the authors examine the "signal/noise" metaphor in the context of three different statistical processes: repeated measures, measuring individuals, and dichotomous events. On the basis of this analysis, several recommendations are made about research and instruction.
The computer's potential to improve the teaching of data analysis is now a well-known litany (Jones, 1997; Snell & Peterson, 1992; Velleman & Moore, 1998). It includes its power to illuminate key concepts through simulations and multiple-linked representations. It also includes its ability to free students up, at the appropriate time, from time-intensive tasks - from what NCTM's (1989) Standards referred to as the "narrow aspects of statistics" (p. 113). This potentially allows instruction to focus more attention on the processes of data analysis - exploring substantive questions of interest, searching for and interpreting patterns and trends in data, and communicating findings.
Some questions that teachers who are ready to venture online for the first time or who are trying to rethink how they are currently using online resources in the classroom should ask are provided. These questions ask what the educational purpose of the activity is, where the activity fits into the curriculum, how using the Internet will enhance the activity, how students will use the online resources, what experience students have with data analysis and thoughtful discussion, and what will happen if the intended resources are not available.
The school and classroom offer the best opportunities for students to practice becoming skilled participants in reflective online discussion. The role of the teacher in leading productive discussions is considered, and the ways in which online exchanges can support classroom discussion are discussed.
In the third part of a series, advice for teachers on moving students engaged in Internet science projects beyond collecting and uploading data to analyzing data from many sites is presented. The advice deals with what is involved in a data-centered investigation and with leading such investigations by using reliable data, beginning with familiar contexts, using data with salient trends, and working with representations that students understand.
In the final part of a four-part series, the writers reflect on the ways in which teaching practices need to change if they are to take advantage of rapidly emerging technologies. They investigate what a classroom looks like and how student learning is widened and deepened when technology is integrated thoroughly into teaching and learning, and examine how schools or districts can support teachers in their integration of technology into teaching and learning practices.