Term Paper: Statistical Education Trains Students

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SAMPLE EXCERPT:

[. . .] Learning statistics means learning to communicate using the statistical language, solving statistical problems, drawing conclusions, and supporting conclusions by explaining the reasoning behind them.

There are often different ways to solve a statistical problem.

People may come to different conclusions based on the same data if they have different assumptions and use different methods of analysis.

Once teachers have described their goals for students in statistics classes, they must address the issue of how students can learn these ideas and change their pre-existing beliefs about statistics. Many college-level statistics classes involve listening to lectures and completing assignments in textbooks. However, many teachers are concerned that these activities do not help achieve the goals for their students.

Many researchers have indicated that students are not learning what their teachers want them to. Reviews by Garfield (2000) describe research related to learning and understanding probability and statistics. These studies fall into two categories: psychological research and statistics education research. In addition, some studies in mathematics education offer additional insights into the teaching and learning of quantitative information.

A second area of research conducted mainly by statistical educators, focuses less on basic patterns of thinking, and more on how statistics is learned. Some of these studies have contradicted ideas presented by the psychological studies described earlier (Garfield, 2000). For example, some of these studies indicate that students' use of heuristics (including representativeness and availability) often varies with problem context.

Garfield (2000) looked at the performance of students in an introductory course on a variety of parallel problems, designed to draw out use of the representative heuristic. Their results indicate that students do not rely solely on the representativeness heuristic to solve problems of a similar type. One researcher suggested that various perspectives that students use to reason cause inconsistencies in student responses. Students seem to understand and reconstruct a problem in different ways, causing them to apply different strategies to solve problems. Another researcher described other reasons for inconsistencies in student responses, including the constraints inflicted by artificial experiments and ambiguity of questions used. Thus, alternative methods of teaching could be greatly beneficial to statistical education.

For example, according to Harrington (1999), "Case study method has long been held as an effective tool for increasing student engagement in statistics. The practice of bringing realistic applications and cases into statistics education is growing in general. Improved statistical computer packages and the dilation of Internet-based access to data sets have expanded significantly the opportunity for statistical applications to business problems, particularly those germane to economics. Reports from a number of authors confirm the importance of active student involvement in the learning process. Students regularly report that the case projects require considerable effort but are a key component in the contribution to their learning. Case studies are particularly well suited for the business majors because they are interested primarily in the study of business (economic) problems and not mathematical statistics. Students are presented with situations that require statistical and economic analysis to solve a realistic problem. In the cases, students must first apply economic and business analysis to identify key issues and formulate the analysis. Written and oral reports (addressed to policy makers) are particularly powerful teaching and learning strategies when used with the case study."

Additional research on statistical education suggests ways to help students learn, as well as problems that must be considered. According to research, the following things help students learn (Garfield, 2000):

Activity-based courses and use of small groups appear to help students overcome some misconceptions of probability and enhance student learning of statistics concepts.

When students are tested and provided feedback on their misconceptions, followed by corrective activities (where students are encouraged to explain solutions, guess answers before computing them, and look back at their answers to determine if they make sense), this "corrective-feedback" strategy appears to help students overcome their misconceptions (e.g., believing that means have the same properties as simple numbers).

Students' ideas about the likelihood of samples (related to the representativeness heuristic) are improved by having them make predictions before gathering data to solve probability problems, then comparing the experimental results to their original predictions.

Use of computer simulations appears to lead students to give more correct answers to a variety of probability problems.

Using software that allows students to visualize and interact with data appears to improve students' understanding of random phenomena and their learning of data analysis.

Methodology

The role of researchers and teachers in developing statistical education research and curriculums will be considered for the purpose of this paper. In recent years, there have been many changes in the world of statistical education. Have these changes affected students learning, retention, and motivation? Is research tied to what actually goes on in the classroom? Does statistical education research have the visibility it needs to advance in the educational arena? These are all questions that need to be addressed.

It is clear from the literature that the issues and motivations surrounding statistical education and the related concerns of learning and teaching methods cannot be reduced to simple yes/no responses elicited by quantitative research methods. The majority of data will be collected using qualitative methods because the qualitative approach is concerned with "understanding the nature of a part of the social world, as far as possible from the perspective and context of the actors within it. It does not attempt to describe issues in the social world through measurement."

The researcher will examine the existing literature to identify common themes and potential overlap, and develop a conceptual basis for analyzing the obstacles, opportunities, and strategies for improving statistical education in the future.

The literature was searched for existing literature on statistical education and information on comparable studies. This literature was then evaluated and reviewed. In this paper, the researcher aims to establish what an ideal program for training future researchers in statistics education would be. The aim of this paper is to provide suggestions to create a collaborative research environment where active learning is the primary method used to teach statistics.

Conclusion

Over the past couple of decades, there has been a shift in focus in instruction for statistics, both in the goals of what instructors want students to learn and in the methods used to promote learning. Recent surveys reveal that many of today's teachers are merging toward this focus. Therefore, it is crucial that researchers document the effects of these changes on students learning, retention, and motivation.

The engagement of students in many statistics classes presents many challenges to teachers regardless of institutional or student population characteristics (Harrington, 1999). Students often find the initial experience with statistical education uninteresting, inapplicable, and uninspiring. Teachers often say that they are frustrated with failed attempts to invigorate and energize students in statistical education.

In the past, statistical education has favored theory over application and cursory attention over practice and competency (Harrington, 1999). However, student expectation, the demand of workplace statistical competencies, and accreditation body criteria have changed this focus to the interpretation and meaning of statistics rather than on the memorization of abstract mathematical concepts. Teachers are now making efforts to provide students with opportunities to develop their skills and abilities as consumers, as well as practitioners of statistics. This is believed to be more effective for preparing students for the real world.

In his 1991 treatise, Teaching Statistics: More Data, Less Lecturing, George Cobb, stated "lectures don't work nearly as well as many of us would like to think." This assertion follows from two groups of research results - the first describes what makes learning hard and lecturing ineffective, the second describes what does seem to work when lecturing fails (Harrington, 1999).

Even the most basic ideas of statistics are difficult for students to learn and often conflict with many of their beliefs and attitudes about chance and data. Typically, students change their false beliefs only when their old ideas no longer work. Tone goal of statistical education is to force to confront their misconceptions, a process for which lectures often do not work.

Therefore, statistical educators are learning to rely less on lecturing, and more on the following alternatives (Harrington, 1999):

Group or individual projects;

Lab exercises;

Group problem solving and discussion;

Written and oral presentations; and Demonstrations based on class-generated data.

This means that statistical educators must change their strategies to enhance student learning and revitalize statistical curriculums by facilitating student engagement in the learning process. In order to implement these changes, it is obvious that further research is needed.

Because statistics education research is still relatively new to the educational world, researchers come from diverse disciplines, educational backgrounds, and training. For instance, research in psychology has focused primarily on finding student misconceptions and fault reasoning and research in statistics education has tended to focus on comparing instructional modes, including laboratory environment vs. traditional lecture, and on prediction of student achievement in statistics based on various factors.

Still, researchers must make an attempt… [END OF PREVIEW]

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Statistical Education Trains Students.  (2003, August 27).  Retrieved July 19, 2019, from https://www.essaytown.com/subjects/paper/statistical-education-trains-students/7193713

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