Thesis / Dissertation

  • The conjecture driving this study is that if statistics curricula were to put<br>more emphasis on helping students improve their intuitions about variation and its<br>relevance to statistics, we would be able to witness improved comprehension of<br>statistical concepts (Ballman, 1997). Both the research literature and previously<br>conducted research by the author indicate that variation is often neglected, and its<br>critical role in statistical reasoning is under-recognized.<br>A nontraditional approach to statistics instruction that has variation as its<br>central tenet, and perceives learning as a dynamic process subject to development<br>for a long period of time and through a variety of contexts and tools, is laid out in<br>this thesis. The experiences and insights gained from adopting such an approach<br>in a college level, introductory statistics classroom are reported.

  • Our analysis identified problems both with the subject matter of statistics (e.g. multiple levels of abstraction, difficulty mapping statistical representations to real-world situations) and with its pedagogy (which typically does little to help concertize abstract concepts or illuminate the mapping process). Drawing on research in education, cognitive psychology and statistical computing, we designed, implemented, and pilot-tested software (ELASTIC) and a curriculum (Reasoning Under Uncertainty) to address these problems. Our approach was successful in many of the problem areas identified above; in addition, our experiences in classrooms helped us better understand the difficulties students have in understanding and applying statistical reasoning.

  • This paper describes the conceptual base for the development of a computer-based expert system. After reviewing developments in computer-based learning and experiments with computer-assisted learning in statistics, the paper describes the nature of expert systems and desired attributes of expert systems in statistics. An overview of proposed research projects to develop a computer-based expert system research outliner/statistical tutor is presented. Current progress, anticipated timelines and methodological concerns are provided. Two figures--The Changing Focus of Attention in Technology for Computer-Assisted Learning and System Delivery Tools--are included. (Contains 37 references.) (Author/ALF)

  • The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was administered to the statistics classes at the College of Education and at the College of Commerce and Business Administration at the University of Alabama (Tuscaloosa). The STATS required students to describe themselves based on a 0 to 9 scale, with 0 being "does not describe me" and 9 being "describes me." The sample included 79 male and 97 female students in undergraduate and graduate statistics classes. The data were analyzed in contingency tables using chi square statistics to compute significance of relationships. All data analyses were performed on an IBM miniframe computer. The association analysis showed that there was a significant relationship between students' anxiety in learning statistics and the variables of students' achievement, statistical preknowledge, school, and current class level. However, the results do not provide enough evidence to suggest that there was a relationship between students' anxiety in learning statistics and the other variables (such as college mathematics background, gender, and ethnicity). (RLC)

  • This paper discusses the use of an interactive computer system as a major component of instruction for the graduate level introductory educational statistics course at the University of Maine at Orono. Four major computer topics are covered in the statistics course: (1) terminal and computer operation, (2) Montana State University Interactive Statistical Analysis Program (MSUSTAT), (3) the CMS Editor, and (4) the Statistical Package for the Social Sciences (SPSS). These topics are introduced sequentially during the first six weeks of the semester. The major objective is for the students to be able to use SPSS; the other three topics provide the prerequisite skills. Four references are listed and the appendices include a course syllabus for the summer, 1981; instructions on how to use the Interactive Statistics Program; instructions for using the terminal; three study guides; and instructions for card order and deck setup for generating and processing SPSS files. (CHC)

  • Three aspects to be considered when teaching a one-semester beginning economics statistics course are coverage, mastery, and applications. There is a difference between coverage and mastery. Moreover, mastery is not an end in itself; instructors must consider how statistics courses will influence students' approaches to other subjects and applications. In principle, computer activities can be designed and implemented to improve any and all of these three goals. The HyperCard software for the Macintosh computer should result in an important advance in the interface between computer and user. This will be valuable for tutorial programs. Cognitive scientists are designing software which analyzes student solutions to standard problems by inferring a student's intentions from the details of her/his solution and then offering diagnostic assistance. Programs like "Stat Helper" (briefly discussed) for the Macintosh allow students to interact with the computer in solving a variety of problems. Students can learn about regressions better through hands-on experience on personal or mainframe computers. Computer experiments can exhibit a variety of points about regression applications. Computers can expand coverage and make applications more accessible to the average student. Students must develop some sense about what questions regressions can and cannot be expected to answer. Four examples, including graphs and statistical data, are given: automobile weight and fuel mileage, polynomial (quadratic), omitted independent variable, and logarithmic relationship. Nine references and numerous tables and graphs are provided. (GEA)

  • A knowledge of statistics is an essential part of the training of all students in the filed of education and other behavioral sciences. There are many reasons for this. First, an understanding of the modern literature of behavioral sciences requires a knowledge of statistical method and modes of thought. A high proportion of current books and journal articles either report experimental findings in statistical form or present theories or arguments involving statistical concepts. Therefore, those who aim to become professional personnel in the field of education and other behavioral sciences need competency in the quantitative concepts and skills of statistics for several essential purpose.

  • This doctoral dissertation describes a study that attempted to isolate factors which influence attainment of statistical competence in an introductory college statistics course. The study defined a broad goal of an introductory course: to enable students to solve basic, applied statistical problems. A ten-stage model of the problem solving process was used to develop a framework for evaluating achievement of this goal. A problem-oriented statistics course was developed and taught to four classes that included two different experimental treatments. These treatments involved different types of supplementary statistics problems given to students for each instructional unit. Dependent variables included scores on unit tests and a final exam. Independent variables included scores on a pretest of basic mathematics skills and scores on the Mathematics Anxiety Rating Scale. Students also completed an end-of-course attitude questionnaire. Although the treatments did not appear to be related to significant differences in student learning, several relationships were observed between the variables measured and students reported a high level of satisfaction with the problem-oriented course.

  • This study was undertaken with the goal of inferring an informal approach to probability that would explain, among other things, why subject responses to problems involving uncertainty deviate from those prescribed by formal theories. On the basis of an initial set of interviews such as an informal approach was hypothesized and described as outcome-oriented. In a second set of interviews, the outcome approach was used to successfully predict the performance of subjects on a different set of problems. In this chapter I will elaborate on the importance of understanding that subjects' performance in situations involving uncertainty is based on a theoretical framework that is different in important respects from any formal theory of probability. Additionally, I will argue that the outcome approach is reasonable given the nature of the decisions people face in a natural environment. To this end, I will review research which suggests some reasons why causal as opposed to statistical explanations of events are salient and functionally adaptive.