Journal Article

  • As part of many universities' Business degrees, students will undertake an introductory statistics course.<br>Lecturers need to help these students appreciate and recognise the value of possessing quantitative skills<br>and to learn and apply such skills. Three components to teaching that address these aims as well as the<br>interdependence of these components as part of a process which enhances the teaching environment and<br>student outcomes are described. Methods and examples to perform the techniques and ideas are<br>provided along with a discussion of their implementation and effectiveness after delivery in a large first<br>year course.

  • This article shows a concrete and easy recognizable view of a cumulative distribution function(cdf).<br>Photograph views of the search tabs on dictionaries are used to increase students' understanding and<br>facility with the concept of a cumulative distribution function. Projects for student investigations are<br>also given. This motivation and view helps the cdf become a bit more tangible and understandable

  • Although graduate students in education are frequently required to write papers throughout their<br>coursework, they typically have limited experience in communicating in the language of statistics, both<br>verbally and in written form. To succeed in their future careers, students must be provided with<br>opportunities to develop deep understandings of concepts, develop reasoning skills, and become familiar<br>with verbalizing and writing about statistics. The instructional approach described here spans the entire<br>semester of a statistics course and consists of several aspects including cognitively rich individual<br>assignments, small group activities, and a student-led scoring activity. To demonstrate the impact of this<br>approach on student learning, qualitative and quantitative data were collected from students in two<br>statistics courses. Several assessments indicate improvement in students' reasoning and understanding,<br>written and verbal communication, and confidence.

  • The objective of the study is to determine if there is a significant difference in the effects of the treatment<br>and control groups on achievement as well as on attitude as measured by the posttest. A class of 38<br>sophomore college students in the basic statistics taught with the use of computer-assisted instruction and<br>another class of 15 students with the use of the traditional method from the University of the East, Manila<br>(SY 2003-2004) were the focus of this study. The research method used was the quasi-experimental, nonequivalent<br>control group design. The statistical tool was the Multiple Analysis of Covariance. The researcher<br>made use of the CD-ROM prepared by Math Advantage (1997) to serve as the teaching medium for the<br>experimental group. The following summarizes the findings of the study. The achievement posttest of the<br>treatment group has higher estimated marginal means than the control group and it is reversed in the attitude<br>posttest. Using Hotelling's Trace for the multivariate test, the achievement pretest, attitude pretest, and the<br>two groups have a significant effect on the dependent variables, achievement posttest and attitude posttest.<br>Using covariates to control for the effects of additional variables that might affect performance the attitude<br>pretest accounts for about 56% of the variability in the two groups while achievement pretest about 15%.<br>Levene's test shows that the homogeneity of variances assumption between the two groups is met for<br>achievement posttest but not for attitude posttest. The univariate effects for achievement posttest that are<br>significant are achievement pretest, college entrance test overall score, and groups. The univariate effects<br>that are significant for attitude posttest are attitude pretest and high school general weighted average.

  • This paper provides practical examples of how statistics educators may apply a cooperative framework to classroom teaching and teacher collaboration. Building on the premise that statistics instruction ought to resemble statistical practice, an inherently cooperative enterprise, our purpose is to highlight specific ways in which cooperative methods may translate to statistics education. So doing, we hope to address the concerns of those statistics educators who are reluctant to adopt more student-centered teaching strategies, as well as those educators who have tried these methods but ultimately returned to more traditional, teacher-centered instruction.

  • The pmg add-on package for the open source statistics software R is described. This package provides a<br>simple to use graphical user interface (GUI) that allows introductory statistics students, without<br>advanced computing skills, to quickly create the graphical and numeric summaries expected of them.

  • The purpose of this article is to sketch a hypothetical descriptive framework of statistical knowledge for teaching. Because statistics is a discipline in its own right rather than a branch of mathematics, the knowledge needed to teach statistics is likely to differ from the knowledge needed to teach mathematics. Doing statistics involves many primarily nonmathematical activities, such as building meaning for data by examining the context and choosing appropriate study designs to answer questions of interest. Although there are differences between mathematics and statistics, the two disciplines do share common ground in that statistics utilizes mathematics. This connection suggests that existing research on mathematical knowledge for teaching can help inform research on statistical knowledge for teaching.

  • The study describes levels of thinking in regard to the design of statistical studies.<br>Clinical interviews were conducted with 15 students who were enrolled in high<br>school or were recent high school graduates, and who represented a range of<br>mathematical backgrounds. During the clinical interview sessions students were<br>asked how they would go about designing studies to answer several different<br>quantifiable questions. Several levels of sophistication were identified in their<br>responses, and are discussed in terms of the Biggs and Collis (1982, 1991) cognitive<br>model.

  • In an effort to improve active learning in introductory statistics, we introduce the use of<br>concept mapping techniques as part of the course. While previous papers have touted the<br>use of this and other interactive teaching methods in statistics education, we add to this<br>literature by providing additional assessment of its efficacy. This comes through an<br>experimental design that involves a single instructor teaching two sections of the same<br>statistics course over the same semester. Both cover the same material in the same way<br>with the exception that concept mapping is used in one section, but not the other.<br>Assessment of learning outcomes is done through the use of pre-tests and post-tests of<br>understanding of statistical concepts. We also track changes in student's study habits over<br>the semester through additional surveys. We find only weak evidence that concept<br>mapping is effective in aiding student learning of statistics.

  • The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets available on the Internet to convey abstract concepts underlying ARIMA models and (4) showcasing forecasting tools in timely or familiar applications. These initiatives align with the best practices framed across the "Making Statistics More Effective in Schools of Business" (MSMESB) conferences. Course experiences and student feedback are also discussed.

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