Journal Article

  • Novice problem solvers often fail to recognize structural similarities between problems they know and a new problem because they are more concerned with the surface features rather than the structural features of the problem. The surface features are the story line of the problem whereas the structural features involve the relationships between objects in the problem. We used an online technology to investigate whether students' self-explanations and reception of feedback influenced recognition of similarities between surface features and structural features of statistical problems. On average students in our experimental group gave 12 comments in the form of self-explanation and peer feedback. Students in this Feedback group showed statistically significantly higher problem scores over the No-Feedback group; however, the mean self-efficacy scores were lower for both groups after the problem solving experiment. The incongruence in problem scores with self-efficacy scores was attributed to students' over-rating of their abilities prior to actually performing the tasks. This process of calibration was identified as an explanation for the statistically significant positive correlation between problem solving scores and post self efficacy scores for the Feedback group (p<.01).

  • We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: studentsÅ' performance on the final exam and their perceptions of the relevance of statistics for their professional development. We find no statistically significant difference between ASL and CS students with regard to final examination performance, but students who participated in the ASL project as opposed to CS were less likely to agree that "Å[they] will have no application for statistics in [their] profession[s]." The estimated relationship is both large and statistically significant (p < 0.01).

  • Statistics education has become an increasingly important component of the mathematics education of today.s citizens. In part to address the call for a more statistically literate citizenship, The Guidelines for Assessment and Instruction in Statistics Education (GAISE) were developed in 2005 by the American Statistical Association. These guidelines provide a framework for statistics education towards the end of enabling students to achieve statistical literacy, both for their personal lives and in their careers. In order to achieve statistical literacy by adulthood, statistics education must begin at the elementary school level. However, many elementary school teachers have not had the opportunity to become statistically literate themselves. In addition, they are not equipped pedagogically to provide effective instruction in statistics. This article will discuss statistical concepts that have been identified as necessary for statistical literacy and describe how an undergraduate course in Probability and Statistics for pre-service elementary and middle school teachers was revised and implemented using the GAISE framework, in conjunction with the NCTM Standards for Data Analysis and Probability. The aims of the revised course were to deepen pre-service elementary and middle school teachers. conceptual knowledge of statistics; to provide them with opportunities to engage in, design, and implement pedagogical strategies for teaching statistics concepts to children; and, to help them make connections between the statistical concepts they are learning and the statistical concepts they will someday teach to elementary and middle school students.

  • A course disk in either CD or DVD format can be very beneficial to online, hybrid, or distance courses in statistics as well as traditional on-campus courses, augmenting existing technologies like course management systems. A typical course disk may include the syllabus and course outline, calendar, instructions, lecture notes and lecture outlines, handouts, assignments, interactive content such as quizzes and surveys, software, statistical tables, example program files, program code, data files, video lectures and tutorials, and pertinent website links. In most cases, a course disk would be used in addition to traditional methods like course management systems rather than in place of these traditional methods. Most of the benefits of a course disk are shared with course management systems like Blackboard or Moodle; however, a course disk has the distinct advantages that it need not rely on internet access and it provides access to course materials after a course has ended.<br><br>One course disk was developed and used in teaching a graduate-level introductory statistical methods course in three different settings: distance learning off-campus condensed course, online course, and traditional on-campus course. The course disk provided a variety of benefits across delivery formats as well as benefits unique to each delivery format.<br><br>This article will (1) review relevant literature, (2) describe the course disk and compare its use to other content delivery methods, (3) discuss the experiences and evaluation of using the course disk in three different settings and how the students in each setting benefited from using the course disk, and (4) discuss the necessary hardware and software and the process of making a course disk.

  • The increasing popularity of R is leading to an increase in its use in undergraduate courses at universities (R Development Core Team 2008). One of the strengths of R is the flexible graphics provided in its base package. However, students often run up against its limitations, or they find the amount of effort to create an interesting plot may be excessive. The grid package (Murrell 2005) has a wealth of graphical tools which are more accessible to such R users than many people may realize. The purpose of this paper is to highlight the main features of this package and to provide some examples to illustrate how students can have fun with this different form of plotting and to see that it can be used directly in the visualization of data.

  • Operating room (OR) management differs from clinical anesthesia in that statistical literacy<br><br>is needed daily to make good decisions. Two of the authors teach a course in operations research<br><br>for surgical services to anesthesiologists, anesthesia residents, OR nursing directors, hospital<br><br>administration students, and analysts to provide them with the knowledge to make evidencebased management decisions. Some of these students do not remember enough of their basic<br><br>statistics class(es) to understand the principles presented. We performed a systematic, qualitative<br><br>survey of previous experimental and quasi-experimental studies of the impact of a computer on<br><br>student learning of the basic statistical topics that form a prerequisite to the management course.<br><br>Computer-assisted instruction enhanced student learning of the basic statistical topics.<br><br>We created slides containing both hyperlinks to specific pages of Rice University's introductorylevel free web-based "Online Statistics Book" and OR management examples to provide contex<br>for the material. The website is effective at teaching the material because it directs students<br><br>to test their predictions, which has been shown to enhance learning. Once students have<br><br>completed the statistics review, they have sufficient background to learn the material in the<br><br>OR management course. The students use an interactive Excel spreadsheet dealing with OR<br><br>management topics to provide additional computer-assisted instruction

  • This paper is intended as a contribution to the advancement of scholarship in the field of statistics education, which directly links with the scholarship of teaching and learning. It is apparent from the literature, that statistics education research, as an interdisciplinary field, does not rely on a single tradition of research methodology. There are different research backgrounds, different research methods are used, studies have different foci and different outcome variables are studied. What constitutes research in statistics education is therefore still a fundamental issue, with a consequent call for more research in this field. The present study attempts to identify the major themes of statistics education research in order to provide an overview of its current thematic nature. Twenty-four doctoral dissertations as well as 138 articles in three specialist statistics education journals, published between 2005 and 2009, were analyzed regarding their key themes and topics. The frequency of occurrence of the key themes is summarized.<br>We found that the teaching and learning of statistics was the most popular theme or topic. In particular, there is a growing network of researchers interested in studying the development of students' statistical reasoning. Only 15% of the literature was dedicated to studies on the use of information communications technology (ICT), with the relevant studies reflecting the popularity of JAVA Applets and simulation tools. A smaller portion<br>of the literature was devoted to course design and non-cognitive factors.<br>This study provides a framework for understanding current developments in statistics education research and suggests structure to the field, making it easier for future researchers to become acquainted with the discipline. In this way a contribution is made in furthering scholarship in statistics education.

  • We provide an overview of EarlyStatistics, an online professional development course in statistics education targeting European elementary and middle school teachers. The course facilitates intercultural collaboration of teachers using contemporary technological and educational tools. An online information base offers access to all of the course content and resources.

  • This article offers, with accompanying rationale, simple data sets that produce distinct values of certain basic summary statistics.

  • This article provides real life examples that can be used to explain statistical concepts. It does not attempt to be exhaustive, but rather, provide a few examples for selected concepts based on what students should know after taking a statistics course

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