Teaching

  • This paper deals mainly with the experiences of teaching statistics at the Agricultural Faculty in Novi Sad. Statistics was incorporated into the teaching programmes for students from the establishment of the Agricultural Faculty in 1954. Statistical programmes are oriented to different agricultural courses at the Faculty. Statistical education for agriculturists tries to give them a solid foundation in statistics. An emphasis is placed on mastering a wide use of statistical methods in order to allow the students to apply these techniques in many fields of agricultural science like: field crops production, vegetable crop production, horticulture, fruit growing, grape production, plant protection, livestock, veterinary medicine, agricultural mechanization, water resources, agricultural economics etc. Problems and dilemmas encountered in statistical education will be presented and some ideas on how to improve the teaching of agricultural statistics. It is expected that the statistical knowledge achieved by finished agricultural students will provide a solid foundation for master degree studies in Biometrics. It is necessary to emphasize the important role of teaching statistics to agricultural students for improving their general knowledge and for better use of statistical methods in research work.

  • Some students in a service statistics course struggle with the material because they focus too much on the mathematical details and miss the broader issues and relevance to their degree program. It has proved useful for them to have the lecturer narrate a story, which gives a broad overview of the area while simultaneously drawing a rough concept map as an illustration. Of course this is very time consuming and impractical for large classes. We are currently developing and trialing a computer-based version of this setting, creating an interactive concept map with a narrative that students can follow as needed.

  • Instructional methods involving students in activities for exploring statistical concepts have proven to be highly effective. Formal mathematics, on the other hand, constitutes the basis of inductive reasoning. This paper reports on an "ActivStats" class for college math majors that teaches statistical concepts as well as mathematical foundations. Its basis is a four-step procedure comprising problem analysis, student activities, computer simulation, and formal mathematical analysis.

  • As statistics continues to increase its presence in the school curriculum, particularly the mathematics one, it becomes increasingly more difficult for teachers to be able to fit everything in. They complain that if statistics must be included, then something must go. One suggestion to solve their problem is to combine the topics of statistics and mathematics so that both are presented together. The NSF-funded project Data-Driven Mathematics has done precisely that. The series of eleven modules motivates mathematics topics found in pre-algebra, algebra, geometry, advanced algebra, and advanced mathematics from a data point of view that involves students. This paper presents some insight as to how this may be done with the advanced mathematics topic of matrices. (See Burrill, Burrill, Landwehr, & Witmer, 1998).

  • Most managers do not instinctively think statistically, mainly because they are not convinced that statistical thinking adds any value to management and decision-making. Traditional business statistics courses tend to reinforce this view by concentrating on mathematical detail and computation. Without the ability to think statistically, and to understand and interpret data, managers have to resort to gut reactions, which are invariably misguided and unreliable. In this paper we advocate a problem centred approach to teaching statistical thinking based on realistic business examples. Students must be thoroughly involved in the learning process, and encouraged to discover for themselves the meaning, importance and relevance of statistical concepts. Time should be devoted to thinking about the key issues, and for significant interaction both between student and teacher and also, more importantly, between the students themselves.

  • Teaching statistics to students aspiring to other professions can be both frustrating and rewarding. The frustration arises from (a) having limited time to cover everything from introductory to advanced material, (b) receiving little input from staff in the client profession, (c) the concepts of unpredictability and randomness being alien to students' thinking, particularly for engineering students, and (d) students not having the background knowledge or skills necessary to understand the methods fully. The reward comes from seeing students understand both basic and advanced concepts and methods, and from requests for assistance with later work as former students discover the relevance of statistics. This paper will address some methods used to overcome the frustration and to enhance the rewards in teaching a first course in statistics to engineering students. Although situations vary, these ideas will hopefully provide helpful tools.

  • Even the best engineering undergraduates often have little enthusiasm for our statistics courses. Some of their disinterest is really traceable not just to a skepticism about whether using our apparently boring methods will help them be better engineers, but to an even more fundamental ignorance of what engineers do and what kinds of environments they work in. Where a statistician has the luxury of giving a second course in engineering statistics (like a statistical quality control (SQC) course), some part of that course can be aimed at providing not just statistical methodology, but also a proper context for that methodology. This paper discusses ideas in this direction, some of which I have used in an SQC course for industrial engineering students and are documented at http://www.public.iastate.edu/~vardeman/IE361/ie361vard.html,a course Web page, and others of which I am still thinking about how to utilize.

  • Using real life examples in Introductory Statistics courses is now accepted as very desirable and even necessary. There are many resources available on the Internet and elsewhere that makes this particularly easy. In this paper we discuss some advantages of using homegrown examples, obtained mainly from consulting work. However, to be able to use these examples in undergraduate courses they must be framed in such a way that the students can understand them. We also give a number of examples where we have done this.

  • This paper handles the use of technology in teaching statistics at the college level. It distinguishes between teaching statistics at an introductory level in core courses, and integrating statistics as "tools" in different disciplines, and teaching statistics as a science for specialized students. Teaching objectives are different for each category, and thus teaching methods should also be different. While stress is given to computations and how they are done using calculators in some classes, emphasis is given to concepts and their meanings in others where the use of technology promotes active learning, enhances the teaching objectives which in turn influences the method statistics is taught and introduced.

  • One often hears that "data are not information, information is not knowledge, knowledge is not wisdom". But what will turn data into information, information into knowledge, and knowledge into wisdom? The first two facets of this question are at the core of every university course in statistics. They provide a motivation for understanding statistical description and statistical inference, respectively. It is the third facet, the getting of wisdom, which adds depth, resilience and realism to that understanding, yet its importance is often underrated in professional statistics programs. Crucial to the getting of wisdom in this context is a competence to argue back to a statistic and to criticise a statistical argument. Imparting this competence should be a vital concern in designing the program syllabus. In this paper I argue that, by adding a little to the syllabus, such a program can also aid the statistician in opening up for his/her client the client's own path to statistical knowledge and wisdom. Such a move constructively addresses an abiding social issue: the need to enhance the level of numeracy in our alarmingly innumerate society.

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